针对海鸥优化算法(seagull optimization algorithm,SOA)在求解高维问题时容易早熟、解精度低的问题,提出一种引入学习行为的自适应海鸥优化算法(self adaptive SOA with learning,ASOAL)。在迁徙阶段,引入学习行为实现个体间的信息交流...针对海鸥优化算法(seagull optimization algorithm,SOA)在求解高维问题时容易早熟、解精度低的问题,提出一种引入学习行为的自适应海鸥优化算法(self adaptive SOA with learning,ASOAL)。在迁徙阶段,引入学习行为实现个体间的信息交流,并利用非线性参数控制个体对解空间的平滑搜索,以避免迁徙的盲目性。在攻击阶段,引入自适应行为,较好地平衡了前后期种群的开发和勘探行为。选择IEEE CEC(congress on evolutionary computation)2017的10个标准函数进行测试,结果表明,所提出的ASOAL算法相较于SOA算法,解精确度有较大幅度提升,且具有更强的鲁棒性。同时,选择车间作业调度问题(job shop schedule problem,JSP)来测试ASOAL求解约束问题的能力,仿真结果显示,ASOAL比SOA找到的最优解及平均解分别最高提升约15.2%和14.5%。综上所述,ASOAL算法适合求解较高维度的无约束函数优化问题及部分约束工程优化问题。展开更多
The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid different...The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid differential evolution(DE)algorithm combined with an improved equilibrium optimizer(DE-IEO)specifically for the CHPED problem.The DE-IEO incorporates three enhancement strategies:a chaotic mechanism for initializing the population,an improved equilibrium pool strategy,and a quasi-opposite based learning mechanism.These strategies enhance the individual utilization capabilities of the equilibrium optimizer,while differential evolution boosts local exploitation and escape capabilities.The IEO enhances global search to enrich the solution space,and DE focuses on local exploitation for more accurate solutions.The effectiveness of DE-IEO is demonstrated through comparative analysis with other metaheuristic optimization algorithms,including PSO,DE,ABC,GWO,WOA,SCA,and equilibrium optimizer(EO).Additionally,improved algorithms such as the enhanced chaotic gray wolf optimization(ACGWO),improved particle swarm with adaptive strategy(MPSO),and enhanced SCA with elite and dynamic opposite learning(EDOLSCA)were tested on the CEC2017 benchmark suite and four CHPED systems with 24,84,96,and 192 units,respectively.The results indicate that the proposed DE-IEO algorithm achieves satisfactory solutions for both the CEC2017 test functions and real-world CHPED optimization problems,offering a viable approach to complex optimization challenges.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
Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its...Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its performance.The proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and exploitation.Also,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better searching.To evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark suite.The results have been compared to that of six state-of-the-art algorithms and five BOA variants.Moreover,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed mLBOA.Finally,the mLBOA has been applied to solve three real-world engineering design problems.From all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants.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
文摘针对海鸥优化算法(seagull optimization algorithm,SOA)在求解高维问题时容易早熟、解精度低的问题,提出一种引入学习行为的自适应海鸥优化算法(self adaptive SOA with learning,ASOAL)。在迁徙阶段,引入学习行为实现个体间的信息交流,并利用非线性参数控制个体对解空间的平滑搜索,以避免迁徙的盲目性。在攻击阶段,引入自适应行为,较好地平衡了前后期种群的开发和勘探行为。选择IEEE CEC(congress on evolutionary computation)2017的10个标准函数进行测试,结果表明,所提出的ASOAL算法相较于SOA算法,解精确度有较大幅度提升,且具有更强的鲁棒性。同时,选择车间作业调度问题(job shop schedule problem,JSP)来测试ASOAL求解约束问题的能力,仿真结果显示,ASOAL比SOA找到的最优解及平均解分别最高提升约15.2%和14.5%。综上所述,ASOAL算法适合求解较高维度的无约束函数优化问题及部分约束工程优化问题。
基金supported by the Scientific Research Project of Xiangsihu College of Guangxi Minzu University,Grant No.2024XJKY06the National Natural Science Foundation of China under Grant No.U21A20464.
文摘The combined heat and power economic dispatch(CHPED)problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints.This paper presents a hybrid differential evolution(DE)algorithm combined with an improved equilibrium optimizer(DE-IEO)specifically for the CHPED problem.The DE-IEO incorporates three enhancement strategies:a chaotic mechanism for initializing the population,an improved equilibrium pool strategy,and a quasi-opposite based learning mechanism.These strategies enhance the individual utilization capabilities of the equilibrium optimizer,while differential evolution boosts local exploitation and escape capabilities.The IEO enhances global search to enrich the solution space,and DE focuses on local exploitation for more accurate solutions.The effectiveness of DE-IEO is demonstrated through comparative analysis with other metaheuristic optimization algorithms,including PSO,DE,ABC,GWO,WOA,SCA,and equilibrium optimizer(EO).Additionally,improved algorithms such as the enhanced chaotic gray wolf optimization(ACGWO),improved particle swarm with adaptive strategy(MPSO),and enhanced SCA with elite and dynamic opposite learning(EDOLSCA)were tested on the CEC2017 benchmark suite and four CHPED systems with 24,84,96,and 192 units,respectively.The results indicate that the proposed DE-IEO algorithm achieves satisfactory solutions for both the CEC2017 test functions and real-world CHPED optimization problems,offering a viable approach to complex optimization challenges.
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.
文摘Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its performance.The proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and exploitation.Also,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better searching.To evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark suite.The results have been compared to that of six state-of-the-art algorithms and five BOA variants.Moreover,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed mLBOA.Finally,the mLBOA has been applied to solve three real-world engineering design problems.From all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants.
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.
