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
Hybrid metaheuristic algorithms play a prominent role in improving algorithms'searchability by combining each algorithm's advantages and minimizing any substantial shortcomings.The Quantum-based Avian Navigati...Hybrid metaheuristic algorithms play a prominent role in improving algorithms'searchability by combining each algorithm's advantages and minimizing any substantial shortcomings.The Quantum-based Avian Navigation Optimizer Algorithm(QANA)is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds.Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields.However,it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems.Therefore,instead of proposing a new algorithm to solve these weaknesses,we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA.The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm(HQANA)is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions,30,50,and 100.The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms,including PSO,KH,GWO,WOA,CSA,HOA,BO,and QANA.Ultimately,five constrained engineering optimization problems from the latest test suite,CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems.The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.展开更多
Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,...Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.展开更多
Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as ...Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.展开更多
Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food s...Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food source.This algorithm has the characteristics of a simple structure and high performance.However,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation capabilities.This paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization problems.Firstly,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the algorithm.Secondly,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst solutions.Thirdly,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence speed.These three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the algorithm.In performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic algorithms.The numerical experimental results demonstrated the superiority of APFA.展开更多
This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological population.ABO is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and ...This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological population.ABO is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and eliminating.The foraging behavior prompts the bison to seek a richer food source for survival.When bison find a food source,they stick around for a while by bathing behavior.The jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating behavior.The eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent individuals.The above behaviors are translated into ABO by mathematical modeling.To assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with constraints.The findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.展开更多
The paper presents the comparative study on numerical methods of Euler method,Improved Euler method and fourth-order Runge-Kutta method for solving the engineering problems and applications.The three proposed methods ...The paper presents the comparative study on numerical methods of Euler method,Improved Euler method and fourth-order Runge-Kutta method for solving the engineering problems and applications.The three proposed methods are quite efficient and practically well suited for solving the unknown engineering problems.This paper aims to enhance the teaching and learning quality of teachers and students for various levels.At each point of the interval,the value of y is calculated and compared with its exact value at that point.The next interesting point is the observation of error from those methods.Error in the value of y is the difference between calculated and exact value.A mathematical equation which relates various functions with its derivatives is known as a differential equation.It is a popular field of mathematics because of its application to real-world problems.To calculate the exact values,the approximate values and the errors,the numerical tool such as MATLAB is appropriate for observing the results.This paper mainly concentrates on identifying the method which provides more accurate results.Then the analytical results and calculates their corresponding error were compared in details.The minimum error directly reflected to realize the best method from different numerical methods.According to the analyses from those three approaches,we observed that only the error is nominal for the fourth-order Runge-Kutta method.展开更多
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but...This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.展开更多
Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)...Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and exploitation.The literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these drawbacks.Therefore,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising solutions.Furthermore,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA algorithm.The improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification component.The results obtained show that the proposed CQFFA model has been significantly improved.It is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and visually.Also,the CQFFA algorithm has evaluated six real-world engineering problems.The experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.展开更多
The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through fie...The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through field and laboratory tests.The paper analyzes the mechanism and causes of engineering geological problems caused by tropical volcanic rock and soil and puts forward measures to control subgrade slope instability by rationally determining project type,making side slope stability control and strengthening waterproofing and drainage.The“zero front slope”tunneling technology at the portal,the simplified excavation method of double-side wall heading and the cross brace construction method of arch protection within the semi-open cut row pile frame in the“mountainside”eccentrically loaded soft soil stratum are adopted to control the instability of tunnel side and front slopes,foundation pits and working faces;CFG or pipe piles shall be used to reinforce soft and expansive foundation or replacement measures shall be taken,and the scheme of blind ditch+double-layer water sealing in ballastless track section shall be put forward to prevent arching deformation of foundation;the treatment measures of CFG pile,pipe pile and vacuum combined piled preloading are adopted to improve the bearing capacity of foundation in deep soft soil section and solve the problems of settlement control and uneven settlement.These engineering countermeasures have been applied during the construction of Jakarta-Bandung HSR and achieved good results.展开更多
Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of t...Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of the data.It excludes irrelevant or surplus features,thus boosting the performance and efficiency of the model.Particle Swarm Optimization(PSO)boasts a streamlined algorithmic framework and exhibits rapid convergence traits.Compared with other algorithms,it incurs reduced computational expenses when tackling high-dimensional datasets.However,PSO faces challenges like inadequate convergence precision.Therefore,regarding FS problems,this paper presents a binary version enhanced PSO based on the Support Vector Machines(SVM)classifier.First,the Sand Cat Swarm Optimization(SCSO)is added to enhance the global search capability of PSO and improve the accuracy of the solution.Secondly,the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity.The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed.To verify the capability of Self-adaptive Cooperative Particle Swarm Optimization(SCPSO),the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems.Compared with the standard PSO algorithm,SCPSO converges faster,and the convergence accuracy is significantly improved.Moreover,SCPSO’s comprehensive performance far exceeds that of other algorithms.Six datasets from the University of California,Irvine(UCI)database were selected to evaluate SCPSO’s effectiveness in solving feature selection problems.The results indicate that SCPSO has significant potential for addressing these problems.展开更多
To solve the teaching difficulties,including hard cultivating engineering thinking,a reasonable transition of professional training,and deep cooperation of students,which impeded the cultivation effectiveness of stude...To solve the teaching difficulties,including hard cultivating engineering thinking,a reasonable transition of professional training,and deep cooperation of students,which impeded the cultivation effectiveness of students’ability to solve complex engineering problems,the paper proposed a Zongheng group teaching model of curriculum cluster based on projects.Firstly,from the perspective of Metaverse,and considering the professional,current teaching situation and learning situation,we analyzed the professional background and proposed the Zongheng group teaching model of curriculum cluster.Then,the connotation,teaching construction and implementation details are explained.After that,we summarized the teaching effect about the 2 years of exploration and practice in the major of software engineering at College of Computer and Information Technology in Mudanjiang Normal University,to clarify the effect of teaching reform.2 years of teaching practice shows that making full use of the advantages of curriculum cluster,Zongheng group and the project-based teaching method,the long-range training,in-depth student cooperation,and the students’ability of solving complex engineering problems are improved.展开更多
This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optima...This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.展开更多
The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited...The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex problems.To address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of difficulty.MTV-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and exploitation.For the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test functions.Moreover,MTV-WOA is compared with well-stablished,recent,and WOA variant algorithms.The experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence rate.Additionally,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative algorithms.Finally,we solved five engineering design problems to demonstrate the practicality of MTV-WOA.The results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.展开更多
While many metaheuristic optimization algorithms strive to address optimization challenges,they often grapple with the delicate balance between exploration and exploitation,leading to issues such as premature converge...While many metaheuristic optimization algorithms strive to address optimization challenges,they often grapple with the delicate balance between exploration and exploitation,leading to issues such as premature convergence,sensitivity to parameter settings,and difficulty in maintaining population diversity.In response to these challenges,this study introduces the Chase,Pounce,and Escape(CPE)algorithm,drawing inspiration from predator-prey dynamics.Unlike traditional optimization approaches,the CPE algorithm divides the population into two groups,each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima.By incorporating a unique search mechanism that integrates both the average of the best solution and the current solution,the CPE algorithm demonstrates superior convergence properties.Additionally,the inclusion of a pouncing process facilitates rapid movement towards optimal solutions.Through comprehensive evaluations across various optimization scenarios,including standard test functions,Congress on Evolutionary Computation(CEC)-2017 benchmarks,and real-world engineering challenges,the effectiveness of the CPE algorithm is demonstrated.Results consistently highlight the algorithm’s performance,surpassing that of other well-known optimization techniques,and achieving remarkable outcomes in terms of mean,best,and standard deviation values across different problem domains,underscoring its robustness and versatility.展开更多
This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by anima...This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another target.Moreover,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment behavior.This paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation ability.The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering problems.The results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other algorithms.Moreover,the source code of the CPA will be publicly available after publication.展开更多
The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search sta...The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.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.
文摘Hybrid metaheuristic algorithms play a prominent role in improving algorithms'searchability by combining each algorithm's advantages and minimizing any substantial shortcomings.The Quantum-based Avian Navigation Optimizer Algorithm(QANA)is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds.Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields.However,it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems.Therefore,instead of proposing a new algorithm to solve these weaknesses,we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA.The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm(HQANA)is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions,30,50,and 100.The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms,including PSO,KH,GWO,WOA,CSA,HOA,BO,and QANA.Ultimately,five constrained engineering optimization problems from the latest test suite,CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems.The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.
基金supported by Grant(42271391 and 62006214)from National Natural Science Foundation of Chinaby Grant(8091B022148)from Joint Funds of Equipment Pre-Research and Ministry of Education of China+1 种基金by Grant(2023BIB015)from Special Project of Hubei Key Research and Development Programby Grant(KLIGIP-2021B03)from Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing.
文摘Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.
文摘Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.
基金supported by the National Natural Science Foundation of China under Grant No.61802328,61972333,and 61771415.
文摘Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food source.This algorithm has the characteristics of a simple structure and high performance.However,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation capabilities.This paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization problems.Firstly,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the algorithm.Secondly,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst solutions.Thirdly,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence speed.These three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the algorithm.In performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic algorithms.The numerical experimental results demonstrated the superiority of APFA.
基金the National Natural Science Foundation of China(Grant No.U1731128)the Natural Science Foundation of Liaoning Province(Grant No.2019-MS-174)+1 种基金the Foundation of Liaoning Province Education Administration(Grant No.LJKZ0279)the Team of Artificial Intelligence Theory and Application for the financial support.
文摘This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological population.ABO is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and eliminating.The foraging behavior prompts the bison to seek a richer food source for survival.When bison find a food source,they stick around for a while by bathing behavior.The jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating behavior.The eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent individuals.The above behaviors are translated into ABO by mathematical modeling.To assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with constraints.The findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.
文摘The paper presents the comparative study on numerical methods of Euler method,Improved Euler method and fourth-order Runge-Kutta method for solving the engineering problems and applications.The three proposed methods are quite efficient and practically well suited for solving the unknown engineering problems.This paper aims to enhance the teaching and learning quality of teachers and students for various levels.At each point of the interval,the value of y is calculated and compared with its exact value at that point.The next interesting point is the observation of error from those methods.Error in the value of y is the difference between calculated and exact value.A mathematical equation which relates various functions with its derivatives is known as a differential equation.It is a popular field of mathematics because of its application to real-world problems.To calculate the exact values,the approximate values and the errors,the numerical tool such as MATLAB is appropriate for observing the results.This paper mainly concentrates on identifying the method which provides more accurate results.Then the analytical results and calculates their corresponding error were compared in details.The minimum error directly reflected to realize the best method from different numerical methods.According to the analyses from those three approaches,we observed that only the error is nominal for the fourth-order Runge-Kutta method.
文摘This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
文摘Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization problems.Nevertheless,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and exploitation.The literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these drawbacks.Therefore,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising solutions.Furthermore,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA algorithm.The improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification component.The results obtained show that the proposed CQFFA model has been significantly improved.It is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and visually.Also,the CQFFA algorithm has evaluated six real-world engineering problems.The experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.
文摘The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through field and laboratory tests.The paper analyzes the mechanism and causes of engineering geological problems caused by tropical volcanic rock and soil and puts forward measures to control subgrade slope instability by rationally determining project type,making side slope stability control and strengthening waterproofing and drainage.The“zero front slope”tunneling technology at the portal,the simplified excavation method of double-side wall heading and the cross brace construction method of arch protection within the semi-open cut row pile frame in the“mountainside”eccentrically loaded soft soil stratum are adopted to control the instability of tunnel side and front slopes,foundation pits and working faces;CFG or pipe piles shall be used to reinforce soft and expansive foundation or replacement measures shall be taken,and the scheme of blind ditch+double-layer water sealing in ballastless track section shall be put forward to prevent arching deformation of foundation;the treatment measures of CFG pile,pipe pile and vacuum combined piled preloading are adopted to improve the bearing capacity of foundation in deep soft soil section and solve the problems of settlement control and uneven settlement.These engineering countermeasures have been applied during the construction of Jakarta-Bandung HSR and achieved good results.
基金supported by the Fundamental Research Funds for the Central Universities of China(No.300102122105)the Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-YB-023).
文摘Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of the data.It excludes irrelevant or surplus features,thus boosting the performance and efficiency of the model.Particle Swarm Optimization(PSO)boasts a streamlined algorithmic framework and exhibits rapid convergence traits.Compared with other algorithms,it incurs reduced computational expenses when tackling high-dimensional datasets.However,PSO faces challenges like inadequate convergence precision.Therefore,regarding FS problems,this paper presents a binary version enhanced PSO based on the Support Vector Machines(SVM)classifier.First,the Sand Cat Swarm Optimization(SCSO)is added to enhance the global search capability of PSO and improve the accuracy of the solution.Secondly,the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity.The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed.To verify the capability of Self-adaptive Cooperative Particle Swarm Optimization(SCPSO),the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems.Compared with the standard PSO algorithm,SCPSO converges faster,and the convergence accuracy is significantly improved.Moreover,SCPSO’s comprehensive performance far exceeds that of other algorithms.Six datasets from the University of California,Irvine(UCI)database were selected to evaluate SCPSO’s effectiveness in solving feature selection problems.The results indicate that SCPSO has significant potential for addressing these problems.
基金supported by the Foundation of Mudanjiang Normal University“Research and Practice on the Construction of Software Engineering Professional Course Group for Engineering Education Certification”(Grant NO.21-XJ21042),“Quality Course Construction for Graduate Course:Information Retrieval and Thesis Writing”(Grant NO.JPKC-2022011)Research Foundation of Education Department of Heilongjiang“Exploration and Practice of New Engineering Talents Training Mode for Computer Majors for Free Trade College”(Grant NO.SJGY 20200732).
文摘To solve the teaching difficulties,including hard cultivating engineering thinking,a reasonable transition of professional training,and deep cooperation of students,which impeded the cultivation effectiveness of students’ability to solve complex engineering problems,the paper proposed a Zongheng group teaching model of curriculum cluster based on projects.Firstly,from the perspective of Metaverse,and considering the professional,current teaching situation and learning situation,we analyzed the professional background and proposed the Zongheng group teaching model of curriculum cluster.Then,the connotation,teaching construction and implementation details are explained.After that,we summarized the teaching effect about the 2 years of exploration and practice in the major of software engineering at College of Computer and Information Technology in Mudanjiang Normal University,to clarify the effect of teaching reform.2 years of teaching practice shows that making full use of the advantages of curriculum cluster,Zongheng group and the project-based teaching method,the long-range training,in-depth student cooperation,and the students’ability of solving complex engineering problems are improved.
基金King Saud University for funding this research through Researchers Supporting Program Number(RSPD2023R704),King Saud University,Riyadh,Saudi Arabia.
文摘This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.
文摘The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex problems.To address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of difficulty.MTV-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and exploitation.For the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test functions.Moreover,MTV-WOA is compared with well-stablished,recent,and WOA variant algorithms.The experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence rate.Additionally,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative algorithms.Finally,we solved five engineering design problems to demonstrate the practicality of MTV-WOA.The results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.
文摘While many metaheuristic optimization algorithms strive to address optimization challenges,they often grapple with the delicate balance between exploration and exploitation,leading to issues such as premature convergence,sensitivity to parameter settings,and difficulty in maintaining population diversity.In response to these challenges,this study introduces the Chase,Pounce,and Escape(CPE)algorithm,drawing inspiration from predator-prey dynamics.Unlike traditional optimization approaches,the CPE algorithm divides the population into two groups,each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima.By incorporating a unique search mechanism that integrates both the average of the best solution and the current solution,the CPE algorithm demonstrates superior convergence properties.Additionally,the inclusion of a pouncing process facilitates rapid movement towards optimal solutions.Through comprehensive evaluations across various optimization scenarios,including standard test functions,Congress on Evolutionary Computation(CEC)-2017 benchmarks,and real-world engineering challenges,the effectiveness of the CPE algorithm is demonstrated.Results consistently highlight the algorithm’s performance,surpassing that of other well-known optimization techniques,and achieving remarkable outcomes in terms of mean,best,and standard deviation values across different problem domains,underscoring its robustness and versatility.
基金This research was supported by the National Natural Science Foundation of China(62076185,U1809209).
文摘This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in nature.CPA utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another target.Moreover,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment behavior.This paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation ability.The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering problems.The results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other algorithms.Moreover,the source code of the CPA will be publicly available after publication.
基金Project of Key Science and Technology of the Henan Province(No.202102310259)Henan Province University Scientific and Technological Innovation Team(No.18IRTSTHN009).
文摘The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.
