Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHIS...Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHISAO)is proposed.In this paper,a snow blowing strategy was introduced during the initialization phase of the population to improve population diversity.Secondly,the dual-population iterative strategy of SAO has been replaced by a multi-population iterative strategy,which is supplemented with a position update formula for the water evaporation phase.Additionally,Cauchy mutation perturbation has been introduced in the snow melting phase.This set of improvements better balances the exploration and exploitation phases of the algorithm,enhancing its ability to pursue excellence.Finally,a fluid activation strategy is added to activate the potential of the algorithm when its update iterations enter stagnation,helping the algorithm to escape from the local optimum.Comparison experiments between PHISAO and six metaheuristics were conducted on the CEC(Congress on Evolutionary Computation)-2017 and CEC-2022 benchmark suites.The experimental results demonstrate that the PHISAO algorithm exhibits excellent performance and robustness.In addition,the PHISAO is applied into the unmanned aerial vehicle trajectory planning problem together with particle swarm optimization,beluga whale optimization,sand cat swarm optimization,and SAO.The simulation results show that the proposed PHISAO can plan the optimal trajectory in all two different maps.The proposed PHISAO objective function values were reduced by an average of 29.49%(map 1),and 18.34%(map 2)compared to SAO.展开更多
基金supported by the Key Research and Development Program of Henan Province under grant Nos.241111222900 and 241111210400the Natural Science Foundation of Henan under grant No.222300420583+3 种基金the Key Research Projects of Henan Higher Education Institutions under grant No.22A590003the National Natural Science Foundation of China under Grant 62103379the Key Science and Technology Program of Henan Province under grant No.232102220067the Maker Space Incubation Project under Grant No.2023ZCKJ102.
文摘Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHISAO)is proposed.In this paper,a snow blowing strategy was introduced during the initialization phase of the population to improve population diversity.Secondly,the dual-population iterative strategy of SAO has been replaced by a multi-population iterative strategy,which is supplemented with a position update formula for the water evaporation phase.Additionally,Cauchy mutation perturbation has been introduced in the snow melting phase.This set of improvements better balances the exploration and exploitation phases of the algorithm,enhancing its ability to pursue excellence.Finally,a fluid activation strategy is added to activate the potential of the algorithm when its update iterations enter stagnation,helping the algorithm to escape from the local optimum.Comparison experiments between PHISAO and six metaheuristics were conducted on the CEC(Congress on Evolutionary Computation)-2017 and CEC-2022 benchmark suites.The experimental results demonstrate that the PHISAO algorithm exhibits excellent performance and robustness.In addition,the PHISAO is applied into the unmanned aerial vehicle trajectory planning problem together with particle swarm optimization,beluga whale optimization,sand cat swarm optimization,and SAO.The simulation results show that the proposed PHISAO can plan the optimal trajectory in all two different maps.The proposed PHISAO objective function values were reduced by an average of 29.49%(map 1),and 18.34%(map 2)compared to SAO.
