A Hybrid Spherical Evolution and Particle Swarm Optimization Algorithm
A Hybrid Spherical Evolution and Particle Swarm Optimization Algorithm
复制标题
DOI：
10.1109/icaiis49377.2020.9194851
复制
发表时间：
2020-03
期刊：
影响因子：
--
通讯作者：
Zhiming Zhang;Zhenyu Lei;Yu Zhang;Yuki Todo;Zheng Tang;Shangce Gao
中科院分区：
文献类型：
--
作者：
Zhiming Zhang;Zhenyu Lei;Yu Zhang;Yuki Todo;Zheng Tang;Shangce Gao
最近提出了一种称为球形进化（SE）的元启发式算法，该算法创新性地采用了一种新的球形搜索机制来代替传统的超立方体搜索机制。SE已经显示出优于其他元启发式算法的上级性能。然而，它仍然遭受低搜索性能和低收敛速度。本文首次提出了一种混合球形进化和粒子群优化算法，旨在充分利用两种不同搜索机制的优势，并设计了一种基于个体适应度的搜索机制控制规则。基于IEEE CEC2017的30个基准函数的实验结果表明，该算法的性能优于其他最先进的算法。
A metaheuristic algorithm called spherical evolution (SE) is proposed recently, and the SE innovatively adopts a novel spherical search mechanism instead of the conventional hypercube search mechanism. SE has shown superior performance over other metaheuristic algorithms. However, it still suffers from low search performance and low convergence speed. In this paper, we for the first time propose a hybrid spherical evolution and particle swarm optimization algorithm aimed to leverage strengths of two different search mechanism in a hybrid algorithm, and design a search mechanism control rule based on the fitness of individuals. Experimental results based on 30 benchmark functions of IEEE CEC2017 and results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.