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山东大学学报(工学版) ›› 2010, Vol. 40 ›› Issue (3): 26-30.

• 机器学习与数据挖掘 • 上一篇    下一篇

一种基于微粒群思想的蚁群参数自适应优化算法

夏辉1,王华1,陈熙2   

  1. 1. 山东大学计算机科学与技术学院, 山东 济南 250101; 2. 山东大学管理学院, 山东 济南 250101
  • 收稿日期:2009-10-20 出版日期:2010-06-16 发布日期:2009-10-20
  • 作者简介:夏辉(1986-),男,山东潍坊人,硕士研究生,主要研究方向为网络优化算法,网络路由和组播.E-mail: sprit-xiahui@mail.sdu.edu.cn
  • 基金资助:

    国家自然科学基金资助项目(60773101)

A kind of ant colony parameter adaptive optimization algorithm  based on particle swarm optimization thought

XIA Hui1, WANG Hua1, CHEN Xi2   

  1. 1. Department of Computer Science and Technology, Shandong University, Jinan 250101, China;
    2. Department of Management, Shandong University, Jinan 250101, China
  • Received:2009-10-20 Online:2010-06-16 Published:2009-10-20

关键词: 微粒群优化, 蚁群优化, 自适应选取, 优质组合, 货郎问题

Abstract:

The parameter values of the ant colony optimization (ACO) algorithm was optimized based on particle swarm optimization(PSO) thought . Through the high-quality combination of the particles search and adaptive selection of parameter values, the ACO algorithm parameter values could be selected without relying on human experience or trial and error of artificial selection. The parameter combination obtained from the algorithm could significantly improve the performance of the ACO algorithm and give parameter values in continuity, randomness and accuracy. By using the highquality combination of parameter values feedback to the ACO algorithm, this algorithm can work well in solving traveling salesman problem (TSP)  with excellent results.

Key words: particle swarm optimization, ant colony optimization, adaptive selection, high-quality combination, traveling salesman problem

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