Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems...Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.展开更多
Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and ...Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.展开更多
To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was presented.The chaotic elite oppositi...To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was presented.The chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the algorithm.The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum.The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence speed.In addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.展开更多
As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the...As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.展开更多
The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing in...The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.展开更多
6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,faul...6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.展开更多
Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the ...Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature convergence.In response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as OChOA.Then,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called RLOChOA.This way effectively avoids the algorithm falling into local optimum.The performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world problems.Numerical results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.展开更多
Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-...Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.展开更多
This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the...This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration ability.The new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning.Hence,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence.Higher-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural networks.They consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between them.However,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such networks.Here,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated manner.The proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy prices.Experiments,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.展开更多
This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques strug...This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex,nonlinear natures.The Sperm Swarm Optimization(SSO)algorithm,which mimics the sperm’s movement to reach an egg,is one such technique.To improve SSO,researchers combined it with three strategies:opposition-based learning(OBL),Cauchy mutation(CM),and position clamping.OBL introduces diversity to SSO by exploring opposite solutions,speeding up convergence.CM enhances both exploration and exploitation capabilities throughout the optimization process.This combined approach,RSSO,has been rigorously tested on standard benchmark functions,real-world engineering problems,and through statistical analysis(Wilcoxon test).The results demonstrate that RSSO significantly outperforms other optimization algorithms,achieving faster convergence and better solutions.The paper details the RSSO algorithm,discusses its implementation,and presents comparative results that validate its effectiveness in solving complex engineering design challenges.展开更多
This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-object...This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.展开更多
To reduce NO_(x) emissions of coal-fired power plant boilers,this study introduced particle swarm optimization employing opposition-based learning(OBLPSO)and particle swarm optimization employing generalized oppositio...To reduce NO_(x) emissions of coal-fired power plant boilers,this study introduced particle swarm optimization employing opposition-based learning(OBLPSO)and particle swarm optimization employing generalized opposition-based learning(GOBLPSO)to a low NO_(x) combustion optimization area.Thermal adjustment tests under different ground conditions,variable oxygen conditions,variable operation modes of coal pulverizer conditions,and variable first air pressure conditions were carried out on a 660 MW boiler to obtain samples of combustion optimization.The adaptability of PSO,differential evolution algorithm(DE),OBLPSO,and GOBLPSO was compared and analyzed.Results of 51 times independently optimized experiments show that PSO is better than DE,while the performance of the GOBLPSO algorithm is generally better than that of the PSO and OBLPSO.The median-optimized NO_(x) emission by GOBLPSO is up to 15.8 mg/m^(3) lower than that obtained by PSO.The generalized opposition-based learning can effectively utilize the information of the current search space and enhance the adaptability of PSO to the low NO_(x) combustion optimization of the studied boiler.展开更多
Harris Hawks Optimization(HHO)is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems.Nevertheless,the ba...Harris Hawks Optimization(HHO)is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems.Nevertheless,the basic HHO algorithm still has certain limitations,including the tendency to fall into the local optima and poor convergence accuracy.Coot Bird Optimization(CBO)is another new swarm-based optimization algorithm.CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface.Although the framework of CBO is slightly complicated,it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions.This paper proposes a novel enhanced hybrid algorithm based on the basic HHO and CBO named Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization(EHHOCBO).EHHOCBO can provide higher-quality solutions for numerical optimization problems.It first embeds the leadership mechanism of CBO into the population initialization process of HHO.This way can take full advantage of the valuable solution information to provide a good foundation for the global search of the hybrid algorithm.Secondly,the Ensemble Mutation Strategy(EMS)is introduced to generate the mutant candidate positions for consideration,further improving the hybrid algorithm’s exploration trend and population diversity.To further reduce the likelihood of falling into the local optima and speed up the convergence,Refracted Opposition-Based Learning(ROBL)is adopted to update the current optimal solution in the swarm.Using 23 classical benchmark functions and the IEEE CEC2017 test suite,the performance of the proposed EHHOCBO is comprehensively evaluated and compared with eight other basic meta-heuristic algorithms and six improved variants.Experimental results show that EHHOCBO can achieve better solution accuracy,faster convergence speed,and a more robust ability to jump out of local optima than other advanced optimizers in most test cases.Finally,EHHOCBOis applied to address four engineering design problems.Our findings indicate that the proposed method also provides satisfactory performance regarding the convergence accuracy of the optimal global solution.展开更多
Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengt...Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.展开更多
The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ...The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.展开更多
The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource...The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.展开更多
Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design...Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design(CAD)system that presents a new method for DED classification called(IAOO-PSO),which is a powerful Feature Selection technique(FS)that integrates with Opposition-Based Learning(OBL)and Particle Swarm Optimization(PSO).We improve the speed of convergence with the PSO algorithmand the exploration with the IAOO algorithm.The IAOO is demonstrated to possess superior global optimization capabilities,as validated on the IEEE Congress on Evolutionary Computation 2022(CEC’22)benchmark suite and compared with seven Metaheuristic(MH)algorithms.Additionally,an IAOO-PSO model based on Support Vector Machines(SVMs)classifier is proposed for FS and classification,where the IAOO-PSO is used to identify the most relevant features.This model was applied to the DED dataset comprising 20,000 cases and 26 features,achieving a high classification accuracy of 99.8%,which significantly outperforms other optimization algorithms.The experimental results demonstrate the reliability,success,and efficiency of the IAOO-PSO technique for both FS and classification in the detection of DED.展开更多
基金funded by Firat University Scientific Research Projects Management Unit for the scientific research project of Feyza AltunbeyÖzbay,numbered MF.23.49.
文摘Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.
基金This work is financially supported by the Fundamental Research Funds for the Central Universities under Grant 2572014BB06.
文摘Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.
基金supported by the National Natural Science Foundation of China(61572444,62176238)Natural Science Foundation of Henan Province,China(222300420088)+3 种基金Training Program of Young Backbone teachers in Colleges and universities in Henan Province,China(2020GGJS006)Program for Science&Technology Innovation Talents in Universities of Henan Province,China(23HASTIT023)Program for Science&Technology Innovation Teams in Universities of Henan Province,China(23IRTSTHN010)National Key Research and Development Program of China(2022YFD2001205).
文摘To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was presented.The chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the algorithm.The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum.The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence speed.In addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.
基金supported by the First Batch of Teaching Reform Projects of Zhejiang Higher Education“14th Five-Year Plan”(jg20220434)Special Scientific Research Project for Space Debris and Near-Earth Asteroid Defense(KJSP2020020202)+1 种基金Natural Science Foundation of Zhejiang Province(LGG19F030010)National Natural Science Foundation of China(61703183).
文摘As a new bionic algorithm,Spider Monkey Optimization(SMO)has been widely used in various complex optimization problems in recent years.However,the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant.Thus,this paper focuses on how to reconstruct SMO to improve its performance,and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design(SMO^(3))is developed.A position updatingmethod based on the historical optimal domain and particle swarmfor Local Leader Phase(LLP)andGlobal Leader Phase(GLP)is presented to improve the diversity of the population of SMO.Moreover,an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values.Also,a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time.Furthermore,an extended SMO^(3)named CSMO^(3)is investigated to deal with constrained optimization problems.The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems.Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems.
基金support from the Ningxia Natural Science Foundation Project(2023AAC03361).
文摘The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.
基金supported in part by the National Key Research and Development Project under Grant 2020YFB1806805partially funded through a grant from Qualcomm。
文摘6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.
文摘Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature convergence.In response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as OChOA.Then,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called RLOChOA.This way effectively avoids the algorithm falling into local optimum.The performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world problems.Numerical results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.
文摘Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.
基金supported by the Yonsei Fellow Program funded by Lee Youn Jae,Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government,Ministry of Science and ICT(MSIT)(No.2020-0-01361,Artificial Intelligence Graduate School Program(Yonsei University)No.2022-0-00113,Developing a Sustainable Collaborative Multi-modal Lifelong Learning Framework)the support of Teachers Associateship for Research Excellence(TARE)Fellowship(No.TAR/2021/00006)of the Science and Engineering Research Board(SERB),Government of India.
文摘This study attempts to accelerate the learning ability of an artificial electric field algorithm(AEFA)by attributing it with two mechanisms:elitism and opposition-based learning.Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far,and opposition-based learning helps enhance its exploration ability.The new version of the AEFA,called elitist opposition leaning-based AEFA(EOAEFA),retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning.Hence,the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence.Higher-order neural networks(HONNs)have single-layer adjustable parameters,fast learning,a robust fault tolerance,and good approximation ability compared with multilayer neural networks.They consider a higher order of input signals,increased the dimensionality of inputs through functional expansion and could thus discriminate between them.However,determining the number of expansion units in HONNs along with their associated parameters(i.e.,weight and threshold)is a bottleneck in the design of such networks.Here,we used EOAEFA to design two HONNs,namely,a pi-sigma neural network and a functional link artificial neural network,called EOAEFA-PSNN and EOAEFA-FLN,respectively,in a fully automated manner.The proposed models were evaluated on financial time-series datasets,focusing on predicting four closing prices,four exchange rates,and three energy prices.Experiments,comparative studies,and statistical tests were conducted to establish the efficacy of the proposed approach.
文摘This paper introduces a novel optimization approach called Recuperated Seed Search Optimization(RSSO),designed to address challenges in solving mechanical engineering design problems.Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex,nonlinear natures.The Sperm Swarm Optimization(SSO)algorithm,which mimics the sperm’s movement to reach an egg,is one such technique.To improve SSO,researchers combined it with three strategies:opposition-based learning(OBL),Cauchy mutation(CM),and position clamping.OBL introduces diversity to SSO by exploring opposite solutions,speeding up convergence.CM enhances both exploration and exploitation capabilities throughout the optimization process.This combined approach,RSSO,has been rigorously tested on standard benchmark functions,real-world engineering problems,and through statistical analysis(Wilcoxon test).The results demonstrate that RSSO significantly outperforms other optimization algorithms,achieving faster convergence and better solutions.The paper details the RSSO algorithm,discusses its implementation,and presents comparative results that validate its effectiveness in solving complex engineering design challenges.
基金supported by the National Key Research and Development Program of China(2016YFD0700605)the Fundamental Research Funds for the Central Universities(JZ2016HGBZ1035)the Anhui University Natural Science Research Project(KJ2017A891)
文摘This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.
文摘To reduce NO_(x) emissions of coal-fired power plant boilers,this study introduced particle swarm optimization employing opposition-based learning(OBLPSO)and particle swarm optimization employing generalized opposition-based learning(GOBLPSO)to a low NO_(x) combustion optimization area.Thermal adjustment tests under different ground conditions,variable oxygen conditions,variable operation modes of coal pulverizer conditions,and variable first air pressure conditions were carried out on a 660 MW boiler to obtain samples of combustion optimization.The adaptability of PSO,differential evolution algorithm(DE),OBLPSO,and GOBLPSO was compared and analyzed.Results of 51 times independently optimized experiments show that PSO is better than DE,while the performance of the GOBLPSO algorithm is generally better than that of the PSO and OBLPSO.The median-optimized NO_(x) emission by GOBLPSO is up to 15.8 mg/m^(3) lower than that obtained by PSO.The generalized opposition-based learning can effectively utilize the information of the current search space and enhance the adaptability of PSO to the low NO_(x) combustion optimization of the studied boiler.
基金supported by the National Natural Science Foundation of China under Grant 52075090Key Research and Development Program Projects of Heilongjiang Province under Grant GA21A403+1 种基金the Fundamental Research Funds for the Central Universities under Grant 2572021BF01Natural Science Foundation of Heilongjiang Province under Grant YQ2021E002.
文摘Harris Hawks Optimization(HHO)is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems.Nevertheless,the basic HHO algorithm still has certain limitations,including the tendency to fall into the local optima and poor convergence accuracy.Coot Bird Optimization(CBO)is another new swarm-based optimization algorithm.CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface.Although the framework of CBO is slightly complicated,it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions.This paper proposes a novel enhanced hybrid algorithm based on the basic HHO and CBO named Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization(EHHOCBO).EHHOCBO can provide higher-quality solutions for numerical optimization problems.It first embeds the leadership mechanism of CBO into the population initialization process of HHO.This way can take full advantage of the valuable solution information to provide a good foundation for the global search of the hybrid algorithm.Secondly,the Ensemble Mutation Strategy(EMS)is introduced to generate the mutant candidate positions for consideration,further improving the hybrid algorithm’s exploration trend and population diversity.To further reduce the likelihood of falling into the local optima and speed up the convergence,Refracted Opposition-Based Learning(ROBL)is adopted to update the current optimal solution in the swarm.Using 23 classical benchmark functions and the IEEE CEC2017 test suite,the performance of the proposed EHHOCBO is comprehensively evaluated and compared with eight other basic meta-heuristic algorithms and six improved variants.Experimental results show that EHHOCBO can achieve better solution accuracy,faster convergence speed,and a more robust ability to jump out of local optima than other advanced optimizers in most test cases.Finally,EHHOCBOis applied to address four engineering design problems.Our findings indicate that the proposed method also provides satisfactory performance regarding the convergence accuracy of the optimal global solution.
基金funded by the Researchers Supporting Program at King Saud University(RSPD2024R809).
文摘Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154)the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)+1 种基金the Young and Middle-Aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province,China(Grant No.T2023007)the National Natural Science Foundation of China(Grant No.U23A20318).
文摘The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.
文摘The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.
文摘Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design(CAD)system that presents a new method for DED classification called(IAOO-PSO),which is a powerful Feature Selection technique(FS)that integrates with Opposition-Based Learning(OBL)and Particle Swarm Optimization(PSO).We improve the speed of convergence with the PSO algorithmand the exploration with the IAOO algorithm.The IAOO is demonstrated to possess superior global optimization capabilities,as validated on the IEEE Congress on Evolutionary Computation 2022(CEC’22)benchmark suite and compared with seven Metaheuristic(MH)algorithms.Additionally,an IAOO-PSO model based on Support Vector Machines(SVMs)classifier is proposed for FS and classification,where the IAOO-PSO is used to identify the most relevant features.This model was applied to the DED dataset comprising 20,000 cases and 26 features,achieving a high classification accuracy of 99.8%,which significantly outperforms other optimization algorithms.The experimental results demonstrate the reliability,success,and efficiency of the IAOO-PSO technique for both FS and classification in the detection of DED.
