Multiple Heterogeneous Ant Colonies with Information Exchange

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI:10.1109/CEC.2008.4631244

A. Arami, B. Rofoee, C. Lucas

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引用次数: 3

Abstract

The method of multiple heterogeneous ant colonies with information exchange (MHACIE) is presented in this paper with emphasis on the speed of finding the optimal solution and the corresponding computational complexity. The proposed method which is inspired by biology and psychology has a structure composed of several ant colonies. These colonies participate in solving problems in a concurrently manner and also exchange information with each other in communicational steps. Each ant colony is considered as an intelligent agent with behavioral traits. These behavioral traits play a key role in the solving procedure, in interrelation circumstances and in installation of relations. Faster solutions have been achieved using different employments of agents in the algorithm structure. Experimental results show the superiority of Multiple heterogeneous ant colonies algorithm in comparison to the standard ant colony system (ACS) and particle swarm optimization (PSO) algorithms on different benchmarks. A dynamic, control engineering benchmark is also provided in order to gain a more complete evaluation of the proposed algorithm.

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具有信息交换的多异质蚁群

本文提出了具有信息交换的多异构蚁群算法(MHACIE)，重点讨论了该算法的寻优速度和计算复杂度。该方法受到生物学和心理学的启发，具有由多个蚁群组成的结构。这些群体以并行的方式参与解决问题，并在通信步骤中相互交换信息。每个蚁群都被认为是一个具有行为特征的智能体。这些行为特征在解决过程、相互关系环境和关系的建立中起着关键作用。通过在算法结构中使用不同的代理，可以获得更快的解。实验结果表明，在不同的基准上，多异构蚁群算法优于标准蚁群系统(ACS)和粒子群优化(PSO)算法。为了对所提出的算法进行更全面的评价，还提供了一个动态的控制工程基准。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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