摘要
蚁群算法是一种具有分布计算、信息正反馈的新型启发式优化算法,初步的研究表明该算法在求解复杂优化问题,尤其是离散优化问题中具有许多优越性。阐述了蚁群算法在TSP问题求解中的应用,通过实验对蚁群算法的参数选择进行了分析,确定了参数的选择原则以及对算法性能的影响。对该算法做了一些改进尝试,仿真研究表明这些改进能在一定程度上使得算法取得更优的值。
Ant Colony Optimization is a novel heuristic optimization algorithm, which has the merit of distributed computation, information positive feedback and heuristic algorithm. Tentative researches show that it can solve complicated and combinatorial optimization problems, especially discrete optimization problems. This paper expatiates on the principle of ACO, and its application in TSP, analyses the selection of parameters impacting on the performance of ACO. Make attempt to improve this algorithm. Simulated data shows that these improving could get better result of this algorithm in some extent.
出处
《信息技术》
2009年第4期129-131,149,共4页
Information Technology
关键词
蚁群算法
TSP问题
仿真
Ant Colony Optimization （ACO）
TSP problem
simulation
