计算机科学 ›› 2010, Vol. 37 ›› Issue (2): 186-188.

• 人工智能 • 上一篇    下一篇

一种新的融合分布估计的蚁群优化算法

许昌,常会友,徐俊,衣杨   

  1. (中山大学信息科学与技术学院 广州510275)
  • 出版日期:2018-12-01 发布日期:2018-12-01

Novel Ant Colony Optimization Algorithm with Estimation of Distribution

XU Chang,CHANG Hui-you,XU Jun,YI Yang   

  • Online:2018-12-01 Published:2018-12-01

摘要: 提出了一种新的融合分布估计的蚁群优化算法。该算法突破了传统蚁群过早收敛的局限性,且蚁群中的每个蚂蚁具有更全面的学习能力,从而能够有效地解决组合优化问题。仿真实验结果表明该算法的性能优于现有的其它几种蚁群优化算法。

关键词: 蚁群优化算法,分布估计,旅行商问题,组合优化问题

Abstract: In order to improve the performance of the ant colony optimization algorithm, a new ant colony optimization algorithm with estimation of distribution (ACO-ED)was presented. ACO-ED uses probabilistic model based on estimating the distribution of promising solutions in the search space,and adjusts the state transition rule and the global updating rule. Furthermore,ACO-ED is significantly improved by extending with a local search procedure. We applied ACO-ED to TSP problems and compared it with other ant colony optimization algorithms. Simulation results show that ACO-ED is an effective and efficient way to solve combinatorial optimization problems.

Key words: Ant colony optimization, Estimation of distribution, TSP problem, Combinatorial optimization problem

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