Ant Colony Optimization Algorithm with LionKing Competition Parameter
带有狮王竞比参数的蚁群优化算法

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.