基于蚁群优化算法求解最大团问题的研究

蚁群算法是一种通过模拟自然界中蚂蚁觅食行为而发展而来的新型启发式仿生优化算法,提出至今被研究人员广泛应用于各种组合优化问题.最大团问题是图论中著名的NPC问题,本文对于基本蚁群算法进行了分析与讨论,针对基本蚁群算法的容易陷入局部最优解、收敛速度慢等问题进行了改进,提出了一种新型蚁群优化算法.本文提出的新型蚁群优化算法增加了结点度和历史选择次数表策略影响蚂蚁选点;另外提出了构造独立的局部信息素更新机制.最后通过对比实验验证,数据结果证明新提出的优化算法相对于基本蚁群算法的优越性和可行性.

The Research of Ant Colony Optimization Algorithm forMaximum Clique Problem

Ant colony algorithm is a new heuristic bionic optimization algorithm developed by simulating ant foraging behavior in nature.It has been widely used by researchers to solve all kinds of combinatorial optimization problems.In this paper,the basic ant colony algorithm is analyzed and discussed.In view of the problem that the basic ant colony algorithm is easy to fall into the local optimal solution and the convergence speed is slow,it proposes a kind of New Ant Colony Optimization Algorithm.In this paper,the new ant colony optimization algorithm is proposed to increase the number of nodes and the number of historical selections to influence the ant selection points.In addition,an independent local pheromone updating mechanism is presented.Finally,the experimental results show that the feasibility of the proposed algorithm is superior to the basic ant colony algorithm.