摘要
结合粒子群算法的问题,提出用混合蚁群算法来求解著名的旅行商问题。问题的核心是应用粒子群算法对蚁群算法的控制参数:启发式因子、信息素挥发系数、随机性选择阈值进行优化,以及运用蚁群系统算法寻找最短路径。新算法对于蚂蚁算法中的参数调整大大减低,减少了大量盲目的实验,力求在开发最优解和探究搜索空间上找到平衡点。对旅行商问题的仿真实验表明,新算法的优化质量和效率都优于传统蚁群算法和遗传算法,接近理论最佳值。新算法也可推广用于其他NP问题的求解。
Combined with the idea of the particle swarm optimization （PSO） algorithm, the ant colony optimization （ACO） algorithm is presented to solve the well known traveling salesman problem （TSP）. The core of this algorithm is using PSO to optimize the control parameters of ACO which consist of heuristic factor, pheromone evaporation coefficient and the threshold of stochastic selection, and applying ant colony system to routing. The new algorithm effectively overcomes the influence of control parameters of ACO and decreases the numbers of useless experiments, aiming to find the balance between exploiting the optimal solution and enlarging the search space. Simulation results show that the new algorithm has better optimization quality and efficiency than the traditional ant colony algorithm and the genetic algorithm. The new algorithm can also be generalized to solve other NP problems.
出处
《计算机仿真》
CSCD
北大核心
2009年第8期89-91,136,共4页
Computer Simulation
关键词
蚁群算法
蚁群系统
粒子群算法
旅行商问题
Ant colony algorithm
Ant colony system
Particle swarm algorithm
Traveling salesman problem
