This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss r...This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.展开更多
Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of ...Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of data redundancy,the Metaheuristic Algorithm(MA)is introduced to select features beforemachine learning to reduce the dimensionality of data.Since a Tyrannosaurus Optimization Algorithm(TROA)has the advantages of few parameters,simple implementation,and fast convergence,and it shows better results in feature selection,TROA can be applied to abnormal traffic detection for SDN.However,TROA suffers frominsufficient global search capability,is easily trapped in local optimums,and has poor search accuracy.Then,this paper tries to improve TROA,namely the Improved Tyrannosaurus Optimization Algorithm(ITROA).It proposes a metaheuristic-driven abnormal traffic detection model for SDN based on ITROA.Finally,the validity of the ITROA is verified by the benchmark function and the UCI dataset,and the feature selection optimization operation is performed on the InSDN dataset by ITROA and other MAs to obtain the optimized feature subset for SDN abnormal traffic detection.The experiment shows that the performance of the proposed ITROA outperforms compared MAs in terms of the metaheuristic-driven model for SDN,achieving an accuracy of 99.37%on binary classification and 96.73%on multiclassification.展开更多
The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and u...The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes.展开更多
The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic ...The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.展开更多
The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are c...The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.展开更多
A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six ...A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.展开更多
Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safet...Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.展开更多
Metaheuristic algorithms are widely used in solving optimization problems.In this paper,a new metaheuristic algorithm called Skill Optimization Algorithm(SOA)is proposed to solve optimization problems.The fundamental ...Metaheuristic algorithms are widely used in solving optimization problems.In this paper,a new metaheuristic algorithm called Skill Optimization Algorithm(SOA)is proposed to solve optimization problems.The fundamental inspiration in designing SOA is human efforts to acquire and improve skills.Various stages of SOA are mathematically modeled in two phases,including:(i)exploration,skill acquisition from experts and(ii)exploitation,skill improvement based on practice and individual effort.The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal,high-dimensional multimodal,and fixed-dimensional multimodal types.The optimization results show that SOA,by balancing exploration and exploitation,is able to provide good performance and appropriate solutions for optimization problems.In addition,the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed approach.Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achievesmuch more competitive results.展开更多
Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features s...Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features selection approach plays an important role in constructing effective network IDS.Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy.Therefore,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack.The proposed model has two objectives;The first one is to reduce the number of selected features for Network IDS.This objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid model.The algorithms used in this paper are particle swarm optimization(PSO),multiverse optimizer(MVO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),firefly algorithm(FFA),and bat algorithm(BAT).The second objective is to detect the generic attack using machine learning classifiers.This objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)classifiers.UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model.UNSW-NB15 dataset has nine attacks type.The generic attack is the highest among them.Therefore,the proposed model aims to identify generic attacks.My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model.In terms of features reduction for the classification,my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy,sensitivity and F-measure of all features,whereas MVO-BAT model reduces features to 24 features with the same accuracy,sensitivity and F-measure of all features for all classifiers.展开更多
Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost.As with other types of algorithms,in metaheuristic a...Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost.As with other types of algorithms,in metaheuristic algorithms,one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms.In this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is designed.The speed of achieving the global optimum of SSA,its simplicity,easy hybridization and JAYA’s success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two algorithms.The hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution part.HSSJAYA works according to the best and worst food source positions.In this way,it is thought that the leader-follower salps will find the best solution to reach the food source.The hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark functions.The results were compared with SSA,JAYA,cuckoo search algorithm(CS),firefly algorithm(FFA)and genetic algorithm(GA).As a result,a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was obtained.In addition,these results were statistically compared using wilcoxon rank sum test with other algorithms.According to the statistical results obtained from the results of the benchmark functions,it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.展开更多
In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education O...In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education Optimization(LEO)is introduced,which is used to solve optimization problems.LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules.LEO is mathematically modeled in three phases:(i)students selecting their teacher,(ii)students learning from each other,and(iii)individual practice,considering exploration in local search and exploitation in local search.The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal,multimodal types and the CEC 2017 test suite.The optimization results show that LEO,with its acceptable ability in exploration,exploitation,and maintaining a balance between them,has efficient performance in optimization applications and solution presentation.LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms.Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior andmore competitive in combating the compared algorithms.The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.展开更多
Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task cl...Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.展开更多
This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyrax...This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food.Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group.Forty-eight(22 unimodal and 26 multimodal)test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm.A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization(PSO),Artificial-Bee-Colony(ABC),Gravitational Search Algorithm(GSA),and Grey Wolf Optimization(GWO).The obtained results showed the superiority of the RHSO algorithm over the selected algorithms;also,the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests.Further,RHSO is very effective in solving real issues with constraints and new search space.It is worth mentioning that the RHSO algorithm has a few variables,and it can achieve better performance than the selected algorithms in many test functions.展开更多
This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic ...This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.展开更多
Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 20...Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.展开更多
Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mo...Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.展开更多
In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal...In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.展开更多
Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms...Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization problems.Hence novel optimization methodologies are still needed.The Al-Biruni earth radius(BER)search optimization algorithm is proposed in this paper.The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals.The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems.The results show that BER can both explore and avoid local optima.BER has also been tested on an engineering design optimization problem.The results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.展开更多
This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to impro...This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to improve job,educational,economic,and living conditions,and so on.Themathematicalmodeling of the proposed MAis presented in two phases to empower the proposed approach in exploration and exploitation during the search process.In the exploration phase,the algorithm population is updated based on the simulation of choosing the migration destination among the available options.In the exploitation phase,the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions.MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite.In addition,MA’s results are compared with the performance of twelve well-known metaheuristic algorithms.The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems.The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions.Also,the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.展开更多
Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervise...Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic algorithms.As intrusion detection data usually contains several features,it is essential to select the best ones appropriately.Linear Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS design.Thus,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset.Nevertheless,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)Algorithm.Performance metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are exploited.The experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising results.PSO-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related works.Furthermore,the best-proposed approaches are valued in the most recent intrusion detection datasets:CSE-CICIDS2018 and LUFlow2020.The evaluation fallouts consolidate the previous results and confirm their correctness.展开更多
基金supported by the Scientific and Technological Research Council of Turkey(TUBITAK)under Grant No.124E002(1001-Project).
文摘This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of data redundancy,the Metaheuristic Algorithm(MA)is introduced to select features beforemachine learning to reduce the dimensionality of data.Since a Tyrannosaurus Optimization Algorithm(TROA)has the advantages of few parameters,simple implementation,and fast convergence,and it shows better results in feature selection,TROA can be applied to abnormal traffic detection for SDN.However,TROA suffers frominsufficient global search capability,is easily trapped in local optimums,and has poor search accuracy.Then,this paper tries to improve TROA,namely the Improved Tyrannosaurus Optimization Algorithm(ITROA).It proposes a metaheuristic-driven abnormal traffic detection model for SDN based on ITROA.Finally,the validity of the ITROA is verified by the benchmark function and the UCI dataset,and the feature selection optimization operation is performed on the InSDN dataset by ITROA and other MAs to obtain the optimized feature subset for SDN abnormal traffic detection.The experiment shows that the performance of the proposed ITROA outperforms compared MAs in terms of the metaheuristic-driven model for SDN,achieving an accuracy of 99.37%on binary classification and 96.73%on multiclassification.
基金supported by the National Natural Science Foundation of China(22408227,22238005)the Postdoctoral Research Foundation of China(GZC20231576).
文摘The optimization of reaction processes is crucial for the green, efficient, and sustainable development of the chemical industry. However, how to address the problems posed by multiple variables, nonlinearities, and uncertainties during optimization remains a formidable challenge. In this study, a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First, experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently, interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally, four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome, accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization, and the optimal process parameters are a catalyst concentration of 3.00% (mass), a methanol: PFAD molar ratio of 8.67, and a reaction time of 30 min. This study provides deeper insights into reaction process optimization, which will facilitate future applications in various reaction optimization processes.
文摘The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.
文摘The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.
基金funded by the National Science Foundation of China(41807259)the Innovation-Driven Project of Central South University(No.2020CX040)the Shenghua Lieying Program of Central South University(Principle Investigator:Dr.Jian Zhou)。
文摘A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.
基金supported by the National Natural Science Foundation Project of China(Nos.72088101 and 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073)The first author was funded by China Scholarship Council(No.202106370038).
文摘Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.
基金supported by Specific Research project 2022 Faculty of Education,University of Hradec Kralove.
文摘Metaheuristic algorithms are widely used in solving optimization problems.In this paper,a new metaheuristic algorithm called Skill Optimization Algorithm(SOA)is proposed to solve optimization problems.The fundamental inspiration in designing SOA is human efforts to acquire and improve skills.Various stages of SOA are mathematically modeled in two phases,including:(i)exploration,skill acquisition from experts and(ii)exploitation,skill improvement based on practice and individual effort.The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal,high-dimensional multimodal,and fixed-dimensional multimodal types.The optimization results show that SOA,by balancing exploration and exploitation,is able to provide good performance and appropriate solutions for optimization problems.In addition,the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed approach.Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achievesmuch more competitive results.
基金funded by The World Islamic Sciences and Education University。
文摘Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features selection approach plays an important role in constructing effective network IDS.Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy.Therefore,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack.The proposed model has two objectives;The first one is to reduce the number of selected features for Network IDS.This objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid model.The algorithms used in this paper are particle swarm optimization(PSO),multiverse optimizer(MVO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),firefly algorithm(FFA),and bat algorithm(BAT).The second objective is to detect the generic attack using machine learning classifiers.This objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)classifiers.UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model.UNSW-NB15 dataset has nine attacks type.The generic attack is the highest among them.Therefore,the proposed model aims to identify generic attacks.My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model.In terms of features reduction for the classification,my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy,sensitivity and F-measure of all features,whereas MVO-BAT model reduces features to 24 features with the same accuracy,sensitivity and F-measure of all features for all classifiers.
文摘Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost.As with other types of algorithms,in metaheuristic algorithms,one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms.In this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is designed.The speed of achieving the global optimum of SSA,its simplicity,easy hybridization and JAYA’s success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two algorithms.The hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution part.HSSJAYA works according to the best and worst food source positions.In this way,it is thought that the leader-follower salps will find the best solution to reach the food source.The hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark functions.The results were compared with SSA,JAYA,cuckoo search algorithm(CS),firefly algorithm(FFA)and genetic algorithm(GA).As a result,a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was obtained.In addition,these results were statistically compared using wilcoxon rank sum test with other algorithms.According to the statistical results obtained from the results of the benchmark functions,it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.
基金supported by the Project of Specific Research PˇrF UHK No.2104/2022-2023,University of Hradec Kralove,Czech Republic.
文摘In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education Optimization(LEO)is introduced,which is used to solve optimization problems.LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules.LEO is mathematically modeled in three phases:(i)students selecting their teacher,(ii)students learning from each other,and(iii)individual practice,considering exploration in local search and exploitation in local search.The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal,multimodal types and the CEC 2017 test suite.The optimization results show that LEO,with its acceptable ability in exploration,exploitation,and maintaining a balance between them,has efficient performance in optimization applications and solution presentation.LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms.Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior andmore competitive in combating the compared algorithms.The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.
基金Funding for this study is received from Taif University Researchers Supporting Project No.(Project No.TURSP-2020/150)Taif University,Taif,Saudi Arabia。
文摘Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.
文摘This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in nature.The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food.Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group.Forty-eight(22 unimodal and 26 multimodal)test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm.A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization(PSO),Artificial-Bee-Colony(ABC),Gravitational Search Algorithm(GSA),and Grey Wolf Optimization(GWO).The obtained results showed the superiority of the RHSO algorithm over the selected algorithms;also,the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests.Further,RHSO is very effective in solving real issues with constraints and new search space.It is worth mentioning that the RHSO algorithm has a few variables,and it can achieve better performance than the selected algorithms in many test functions.
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(No.RS-2023-00218176)the Soonchunhyang University Research Fund.
文摘This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.
基金funded by Deanship of Scientific Research,King Saud University,through the Vice Deanship of Scientific Research.
文摘Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.
文摘Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.
文摘In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.
文摘Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization problems.Hence novel optimization methodologies are still needed.The Al-Biruni earth radius(BER)search optimization algorithm is proposed in this paper.The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals.The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems.The results show that BER can both explore and avoid local optima.BER has also been tested on an engineering design optimization problem.The results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.
基金supported by the Project of Excellence PˇrFUHKNo.2210/2023-2024,University of Hradec Kralove,Czech Republic.
文摘This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to improve job,educational,economic,and living conditions,and so on.Themathematicalmodeling of the proposed MAis presented in two phases to empower the proposed approach in exploration and exploitation during the search process.In the exploration phase,the algorithm population is updated based on the simulation of choosing the migration destination among the available options.In the exploitation phase,the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions.MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite.In addition,MA’s results are compared with the performance of twelve well-known metaheuristic algorithms.The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems.The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions.Also,the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.
文摘Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic algorithms.As intrusion detection data usually contains several features,it is essential to select the best ones appropriately.Linear Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS design.Thus,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset.Nevertheless,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)Algorithm.Performance metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are exploited.The experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising results.PSO-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related works.Furthermore,the best-proposed approaches are valued in the most recent intrusion detection datasets:CSE-CICIDS2018 and LUFlow2020.The evaluation fallouts consolidate the previous results and confirm their correctness.
