基于蚁群算法的多目标优化问题研究

Multi-objective Optimization Based on Ant Colony Algorithm

为保持所求得的多目标优化问题Pareto最优解的多样性,文章提出了一种新的蚁群算法。选择策略采用多信息素权重,信息素更新结合了局部信息素更新与全局信息素更新。其中,全局信息素更新采用了两个最好解。此外,通过在外部设置外部集来存储Pareto解,并将改进的算法应用在双目标TSP上。最后进行了仿真实验,结果表明新方法比NSGA-II和SPEA2更有效。

In order to preserve the diversity of Pareto optimal solutions in multi-objective optimization problems,A new ant colony algorithm is proposed.In the proposed algorithm,the selection strategy is multi-pheromone-weighted,and pheromone update uses the combination of the local and global pheromone update.Especially,the global pheromone update adopts the best solution and the second-best solution.In addition,an external set is set up outside to store the Pareto solution,and the improved algorithm is used to solve the bi-criterion TSP.The experiment show that the new algorithm is more efficient than SPEA2 and NSGA-II.