[1]刘文英,张自鲁,路慎强,等.基于粒子群-遗传混合算法的函数优化研究[J].计算机技术与发展,2019,29(10):170-174.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 033]
　LIU Wen-ying,ZHANG Zi-lu,LU Shen-qiang,et al.Research on Function Optimization Based on Particle Swarm-Genetic Hybrid Algorithm[J].,2019,29(10):170-174.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 033]
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基于粒子群-遗传混合算法的函数优化研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年10期
页码:
170-174
栏目:
智能、算法、系统工程
出版日期:
2019-10-10

文章信息/Info

Title:
Research on Function Optimization Based on Particle Swarm-Genetic Hybrid Algorithm
文章编号:
1673-629X(2019)10-0170-05
作者:
刘文英1 ; 张自鲁1 ; 路慎强2 ; 张晓燕1
1. 中国石油大学(华东) 计算机与通信工程学院,山东 青岛 266580; 2. 中国石化胜利油田分公司物探研究院,山东 东营 257000
Author(s):
LIU Wen-ying 1 ; ZHANG Zi-lu 1 ; LU Shen-qiang 2 ; ZHANG Xiao-yan 1
1. School of Computer and Communication Engineering,China University of Petroleum,Qingdao 266580,China; 2. Geophysical Research Institute of Sinopec Shengli Oilfield Company,Dongying 257000,China
关键词:
粒子群算法; 遗传算法; 混合算法; 小生境; 函数优化
Keywords:
particle swarm optimization; genetic algorithm; hybrid algorithm; niche; function optimization
分类号:
TP312
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 10. 033
摘要:
基于传统粒子群优化算法(PSO)和遗传算法(GA),提出了一种混合算法(hybrid particle swarm optimization and genetic algorithm,h-PSO-GA)。 该算法借鉴小生境的思想,将种群划分为不同的子种群,并设计一种个体评价策略,防止非最优个体被过早淘汰,增加非最优个体被选择的几率,保持种群的多样性;引入相似度概念,依据不同个体进行不同交叉操作,产生更优的个体;将遗传算法中的变异操作引入粒子群算法的个体更新中,使算法的速度更新方式兼具本身的速度算子和遗传算法的变异操作,使该混合算法兼具遗传算法的全局搜索能力和粒子群算法的局部搜索优势。 将其应用到函数优化中,通过对 5 个测试函数进行实验验证,结果表明,该混合算法较之传统的遗传算法与粒子群算法具有较快的收敛性和全局最优性。
Abstract:
Based on particle swarm optimization and genetic algorithm,we propose a hybrid algorithm (h-PSO-GA). On the basis of the idea of niche,the algorithm divides the population into different sub-populations. An individual evaluation strategy is designed to prevent pre-existing individuals from being eliminated prematurely,increase the probability of non-optimal individuals being selected, and maintain the population diversity. The concept of similarity is introduced,and different crossover operations are carried out according to different individuals to produce better individuals. The mutation operation in the genetic algorithm is introduced into the individual update of the particle swarm optimization,so that the speed update of the particle swarm optimization has its own velocity operator and the mutation operation of the genetic algorithm,and the velocity operator in the particle swarm optimization algorithm is used instead of the mutation calculation. The hybrid algorithm combines the global search ability of the genetic algorithm with the local search advantage of the particle swarm algorithm. The results of five test functions show that combined with the GA and PSO,the hybrid algorithm has quick convergence rate and better global performance on function optimization.

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更新日期/Last Update: 2019-10-10