电子学报 ›› 2018, Vol. 46 ›› Issue (1): 110-117.DOI: 10.3969/j.issn.0372-2112.2018.01.016

• 学术论文 • 上一篇    下一篇

基于拟熵自适应启动局部搜索策略的混合粒子群算法

曹玉莲, 李文锋, 张煜   

  1. 武汉理工大学物流工程学院, 湖北武汉 430063
  • 收稿日期:2017-02-28 修回日期:2017-08-16 出版日期:2018-01-25 发布日期:2018-01-25
  • 通讯作者: 李文锋
  • 作者简介:曹玉莲,女,1987年5月出生,湖南益阳人.2012年获得武汉理工大学的工学硕士学位,现为武汉理工大学物流工程学院博士研究生.研究方向:群智能算法、物流系统建模与优化、数据挖掘.E-mail:yalianjingren@126.com;张煜,男,1974年6月出生,天津人,教授、博士生导师.2007年毕业于武汉理工大学,获工学博士学位.发表学术论文40余篇,获省部级科技成果奖一等奖1项,二等奖3项.主要研究方向:物流系统建模、仿真与优化,港口物流及其优化决策,智慧港口顶层设计等.E-mail:sanli@whut.edu.cn
  • 基金资助:
    国家自然科学基金(No.61571336,No.61603280,No.71372202)

Hybrid Particle Swarm Optimization Algorithm with Adaptive Starting Strategy of Local Search Based on Quasi-Entropy

CAO Yu-lian, LI Wen-feng, ZHANG Yu   

  1. School of Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430063, China
  • Received:2017-02-28 Revised:2017-08-16 Online:2018-01-25 Published:2018-01-25

摘要: 在继承综合学习粒子群算法(Comprehensive Learning Particle Swarm Optimizer,CLPSO)全局探索优势的基础上,引入具有高效收敛性能的传统局部搜索(Orthodox Local Search,OLS)方法,提出了基于拟熵自适应启动局部搜索策略的混合粒子群算法(Hybrid Particle Swarm Optimization algorithm with Adaptive starting strategy of Local Search based on Quasi-Entropy,ALSQE-HPSO).采用拟熵指标解决何时启动OLS这一关键问题.对8个标准函数的10维和20维问题的测试结果,表明了ALSQE-HPSO算法的性能优势.本文提出的算法也与包含两种基于CLPSO的改进算法和一种带OLS的粒子群算法在内的其他6种改进粒子群算法进行了对比,实验结果表明ALSQE-HPSO算法的性能优于对比算法.

关键词: 进化算法, 粒子群优化, 自适应策略, 局部搜索, 种群多样性

Abstract: Based on inheriting the advantage of global exploration of Comprehensive Learning Particle Swarm Optimizer (CLPSO),the Orthodox Local Search (OLS) approaches with efficient convergence are introduced and a Hybrid Particle Swarm Optimization algorithm with Adaptive starting strategy of Local Search based on Quasi-Entropy (ALSQE-HPSO) is proposed.A quasi-entropy index is utilized to solve the key issue of when to start OLS.The test results of 10-dimension and 20-dimension of eight benchmark functions show the performance advantages of the ALSQE-HPSO algorithm.The comparisons between the proposed algorithm and six other improved PSO algorithms,including two improved CLPSO algorithms and one PSO algorithm with OLS,are also made.The numerical results indicate that the performance of the ALSQE-HPSO is superior to the compared algorithms.

Key words: evolutionary algorithm, particle swarm optimization, adaptive strategy, local search, population diversity

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