基于极值优化的混合差分进化算法

计算机科学 ›› 2013, Vol. 40 ›› Issue (5): 257-260.

基于极值优化的混合差分进化算法

王丛佼,王锡淮,肖建梅

上海海事大学电气自动化系上海201306;上海海事大学电气自动化系上海201306;上海海事大学电气自动化系上海201306

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受上海市教委科研创新重点项目(12ZZ158),上海市教委重点学科建设项目(J50602)资助

Hybrid Differential Evolutionary Algorithm Based on Extremal Optimization

WANG Cong-jiao,WANG Xi-huai and XIAO Jian-mei

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 针对标准差分进化算法在求解复杂优化问题时易陷入局部最优的问题,提出了一种基于极值动力学机制的混合差分进化算法。该算法的核心在于,当种群聚集度较高时, 利用极值优化算法强大的波动性,通过引入基于种群的极值优化算法来提高种群多样性,从而协助差分进化算法跳出局部最优。仿真实验表明,该混合算法具有较好的全局收敛性,能有效避免早熟收敛。

关键词: 差分进化,极值优化,混合算法,全局优化

Abstract: A new hybrid algorithm based on differential evolution (DE) and extremal optimization (EO) was proposed to solve the premature convergence and low precision of standard differential evolution when it is applied to complex optimization problems．The key points of it lie in:the hybrid algorithm introduces the population-based extremal optimization algorithm in the iteration process of DE when population aggregation gets the high degree,which uses the volatility of EO to increase the diversity of population and the ability of breaking away from the local optimum．Simulations show that the hybrid algorithm has remarkable global convergence ability,and can avoid the premature convergence effectively.

Key words: Differential evolution,Extremal optimization,Hybrid algorithm,Global optimization

王丛佼,王锡淮,肖建梅. 基于极值优化的混合差分进化算法[J]. 计算机科学, 2013, 40(5): 257-260. https://doi.org/

WANG Cong-jiao,WANG Xi-huai and XIAO Jian-mei. Hybrid Differential Evolutionary Algorithm Based on Extremal Optimization[J]. Computer Science, 2013, 40(5): 257-260. https://doi.org/

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