带有狮王竞比参数的蚁群优化算法

Ant Colony Optimization Algorithm with LionKing Competition Parameter

由于蚁群算法采用随机选择策略,使得进化速度较慢,容易出现停滞现象,从而不能对解空间进一步进行搜索,不利于发现更好的解.针对以上问题,提出了一个带有狮王竞比参数的蚁群优化算法.该算法借鉴狮子种群生存竞争中狮王法则的作用,减少大量不必要的搜索,从而大大缩短了求解时间,同时又引用了最大—最小蚂蚁系统(MMAS)算法对信息素的限制,有效地控制了搜索停滞的问题.通过结合MMAS算法的仿真,结果表明:带有狮王竞比参数的改良算法,在求解同样TSP问题时,大大地缩短了优化时间,并且得到了更优的解.

The random selection strategy is the basic selection method for ant colony optimization（ACO） algorithm, but it tends toward resulting in the slow convergence and premature convergence. For the above-mentioned problems, this paper proposes a new method called ant colony optimization algorithm with LionKing competition parameter（ACO-）. The algorithm profits from the laws of species competition（lion） and MAX-MIN Ant System（MMAS）, improved the convergence speed and utilization quality. Meanwhile, in order to avoid stagnation of the search, the range of possible pheromone trails on each solution component is limited to a maximum-minimum interval. In the end, an example of Traveling Salesman Problem（TSP） is given in the paper, which is simulated by using MMAS and ACO-. The simulation resules show that the kind of advanced ant colony algorithm improves the nature of random search, so the algorithm can converge more rapidly to the optimization answer.