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蚁群算法是受自然界中真实蚁群集体行为的启发而提出的一种基于种群的模拟进化算法,属于带构造性的随机搜索算法.本文对应用蚁群算法求解连续空间优化问题作了一些探索性研究,以基本蚁群算法的性能分析为背景,探讨了蚁群算法的构成、性能及特点,对基本蚁群算法作了一系列详细的阐述。一、蚁群算法的基本模型为了便于理解,下面以TSP问题为例说明蚁群算法的基本模型,对于其它问题,可以对此模型稍作修改,便可应用,首先引入以下符号:m——蚁群中蚂蚁的总数目;n——TSP规模(即城市数目);dij ——城市i和城市j之间的距离(i,j 1,2,3,,n);
Ant colony algorithm is a kind of simulated evolutionary algorithm based on population inspired by the real colony behavior in nature and belongs to a structured random search algorithm.This paper makes some exploration on the application of ant colony algorithm to solve continuous space optimization Based on the performance analysis of the basic ant colony algorithm, this paper discusses the composition, performance and characteristics of the ant colony algorithm, and gives a series of detailed exposition of the basic ant colony algorithm. First, the basic model of ant colony algorithm In order to facilitate understanding, the following TSP problem as an example to illustrate the basic model of ant colony algorithm, for other problems, this model can be slightly modified, can be applied, the first introduction of the following symbols: m - The total number of ants in the ant colony; n - the size of the TSP (ie the number of cities); dij - the distance between the city i and the city j (i, j, 1,2,3, n);