连续空间多目标最优化问题的蚁群遗传算法

An Ant Colony Genetic Algorithm for the Multi-Objective Optimization Problem of the Continuous Space

本文提出了一种基于蚁群算法和遗传算法的多目标蚁群遗传算法,用于解决连续空间中带约束条件多目标最优化问题。本算法先将解空间分解成子区域,再用信息素标定这些子区域,信息素对遗传搜索进行指导,在搜索中更新信息素,同时采用了最优决策集的更新策略和搜索收敛退出机制,从而提高求解效率,降低算法复杂度。实验证明,与以往算法相比,此算法能更快、更精确地逼近Pareto前沿。

A new algorithm based on ant colony algorithms and genetic algorithms called Multi-Objective Ant-Genetic Algorithm, which is used to solve the multi-objective optimization problem constrained by some conditions, is presented in this paper. Firstly, the solution space is divided into some subspaces, and all the subspaces are labeled by pheromone, then the pheromone guides the inheritance searching and updates itself. Meanwhile, the strategy of updating the Pareto optimal decisions and the scheme of converging and exiting the searching are used to promote the efficiency and reduce the complexity of the algorithm. In the end, an example is listed to prove that the algorithm can approach the Pareto front more quickly and accurately than the previous algorithm.