广东工业大学学报 ›› 2016, Vol. 33 ›› Issue (05): 44-48.doi: 10.3969/j.issn.1007-7162.2016.05.008

• 综合研究 • 上一篇    下一篇

求解多目标背包问题的改进人工鱼群算法

黄美华， 温洁嫦， 何勇   

  1. 广东工业大学 应用数学学院, 广东 广州 510520
  • 收稿日期:2015-04-29 出版日期:2016-09-10 发布日期:2016-09-10
  • 作者简介:黄美华(1987-)，女，硕士研究生，主要研究方向为最优化方法与应用.
  • 基金资助:

    广东省特色创新项目(2014KTSCX055)

An Improved Artificial Fish Swarm Algorithm for Multi-objective Knapsack Problem

Huang Mei-hua, Wen Jie-chang, He Yong   

  1. School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2015-04-29 Online:2016-09-10 Published:2016-09-10

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摘要：

作为一种新的群智能算法,在求解多目标背包问题时,人工鱼群算法存在盲目搜索、收敛速度慢和求解精度低等问题.针对这些问题,本文结合人工鱼位置全局最优信息,对人工鱼的移动策略进行自适应改进,提出一种改进的人工鱼群算法.对多目标背包优化问题实验仿真表明,本文改进的人工鱼群算法收敛速度和搜索到的非劣解的精度均优于粒子群算法和遗传算法.

关键词: 多目标优化； 背包问题； 人工鱼群算法； 自适应

Abstract:

As a swarm intelligence, the Artificial Fish Swarm Algorithm(AFSA) has its weakness in solving the problem of Multi-objective Knapsack, such as blindness search, low speed of convergence and low accuracy in solution. Combining the global information of the artificial fish position with improving the moving strategy of artificial fish self-adapting, an improved AFSA is proposed. Simulation on multi-objective knapsack problem shows that the convergence rate as well as the accuracy in the non-dominated solutions which have been found out in the improved AFSA is superior to Genetic Algorithm and Particle Swarm Optimization.

Key words: multi-objective optimization; Knapsack problem; artificial fish swarm algorithm; self-adaptive

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