Hybrid-behavior ant-colony optimization algorithm with pheromone updated by multiple good solutions
多优解更新信息素的混合行为蚁群算法

Ant-colony optimization algorithm(ACO) has been successfully applied to the optimization field, especially to combinatorial optimization problems. However, it may encounter premature convergence or costs an excessively long computation-time. To overcome these shortcomings, we present an improved ACO algorithm. This algorithm incorporates a random selection-strategy into the typical state transition rule, for ensuring its exploration ability. Meanwhile, the algorithm maintains a good-solution pool and alternately uses the optimal solution or the sub-optimal solution from the pool to update the pheromone. Thus, the intensification and the diversification of search are balanced. We also discuss the settings of related parameters, and analyze the computational complexity and convergence of the proposed algorithm. Additionally, simulation experiment is performed on typical traveling salesman problems. The results demonstrate that this algorithm generates higher quality solutions than existing algorithms.