多种群蚁群算法在多目标优化中的研究

The Research of Multiple Colony Ant Algorithm in Multi-Objective Optimization Problem

本文针对多目标优化问题提出了一种多种群蚁群算法,按照目标函数的个数建立蚁群种群数,在各个种群搜索过程中,创新性的引入了种群间的全局信息素更新和局部信息素更新,既提高算法对pareto解的搜索效率又避免了陷入局部最优,并针对多目标优化问题进行了仿真,证明了算法的可行性。

Multiple colony ant algorithm is proposed for Multi-Objective Optimization Problem.To establish the number of ant colony population size in accordance with the number of objective function.Global pheromone and local pheromone update will conduct among populations when various population is searching.The new algorithm can improve efficiency of finding pareto solution and avoid falling into local optimum Algorithm is simulated in multi-objective optimization and proved the feasibility.