蚁群算法研究进展

Advances in research of ant colony algorithm

人工蚁群算法是受到蚂蚁在觅食过程中能发现蚁巢到食物的最短路径这种搜索机制的启发而发展起来的一种群体智能算法.蚁群算法在求解一系列困难的组合优化问题上取得成效,成为解决TSP,VRP,QAP,JSP等典型问题的一种新型的强有力算法.对蚁群算法的起源和发展历史、算法理论研究的主要内容和方法、基于算法的改进以及应用范畴等,进行了系统的总结与综述,并对这一新型现代启发式算法的发展方向进行了展望.

The artificial Ant Colony Algorithm (ACA) is a new type of swarm intelligence algorithm with the ability to successfully achieve better solution to complicated combinatorial optimization problems than other popular metaheuristic algorithms. The algorithm takes inspiration from the observations of ant colonies foraging behavior with which ants can find the shortest paths from food sources to their nests. Research on ACA have revealed its potential to solve some classic combinatorial optimization problems, such as TSP, VRP, QAP, JSP, etc. The origin, the development process, and the methodologies of ACA were systematically reviewed, as well as its improvements and applications. Finally, expectation of future research on this new metaheuristic algorithm was presented.