Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to ...Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to solve optimization problems.Further,for the complex higher dimensional problems,MFO is unable to make a good trade-off between global and local search.To overcome these drawbacks of MFO,in this work,an enhanced MFO,namely WF-MFO,is introduced to solve higher dimensional optimization problems.For a more optimal balance between global and local search,the original MFO’s exploration ability is improved by an exploration operator,namely,Weibull flight distribution.In addition,the local optimal solutions have been avoided and the convergence speed has been increased using a Fibonacci search process-based technique that improves the quality of the solutions found.Twenty-nine benchmark functions of varying complexity with 1000 and 2000 dimensions have been utilized to verify the projected WF-MFO.Numerous popular algorithms and MFO versions have been compared to the achieved results.In addition,the robustness of the proposed WF-MFO method has been evaluated using the Friedman rank test,the Wilcoxon rank test,and convergence analysis.Compared to other methods,the proposed WF-MFO algorithm provides higher quality solutions and converges more quickly,as shown by the experiments.Furthermore,the proposed WF-MFO has been used to the solution of two engineering design issues,with striking success.The improved performance of the proposed WF-MFO algorithm for addressing larger dimensional optimization problems is guaranteed by analyses of numerical data,statistical tests,and convergence performance.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
文摘Moth Flame Optimization(MFO)is a nature-inspired optimization algorithm,based on the principle of navigation technique of moth toward moon.Due to less parameter and easy implementation,MFO is used in various field to solve optimization problems.Further,for the complex higher dimensional problems,MFO is unable to make a good trade-off between global and local search.To overcome these drawbacks of MFO,in this work,an enhanced MFO,namely WF-MFO,is introduced to solve higher dimensional optimization problems.For a more optimal balance between global and local search,the original MFO’s exploration ability is improved by an exploration operator,namely,Weibull flight distribution.In addition,the local optimal solutions have been avoided and the convergence speed has been increased using a Fibonacci search process-based technique that improves the quality of the solutions found.Twenty-nine benchmark functions of varying complexity with 1000 and 2000 dimensions have been utilized to verify the projected WF-MFO.Numerous popular algorithms and MFO versions have been compared to the achieved results.In addition,the robustness of the proposed WF-MFO method has been evaluated using the Friedman rank test,the Wilcoxon rank test,and convergence analysis.Compared to other methods,the proposed WF-MFO algorithm provides higher quality solutions and converges more quickly,as shown by the experiments.Furthermore,the proposed WF-MFO has been used to the solution of two engineering design issues,with striking success.The improved performance of the proposed WF-MFO algorithm for addressing larger dimensional optimization problems is guaranteed by analyses of numerical data,statistical tests,and convergence performance.
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.
