关键词: 原对偶遗传算法, 遗传算法, 蚁群算法, 融合

Abstract: Primal-Dual Genetic Algorithm（PDGA） has higher ability of global searching quickly and stochastically. It improves the searching ability in the searching space. But it can’t make full use of system feedback information such that it iterates redundantly and its efficiency is reduced. Meanwhile, Ant Colony Optimization converges to the optimization path by information pheromone accumulation and renewal. It has the ability of parallel processing and global searching. Since there is little information pheromone on the path at the beginning, the speed of the Ant Colony Optimization is slowed down. A combination algorithm of PDGA and Ant Colony Optimization is put forward. It utilizes the advantages of the two algorithms and overcomes their disadvantages. It is heuristic, and performs better in converging and more efficient. Experimental results from the simulation of the algorithm show that the algorithm excels genetic algorithm, Primal-Dual Genetic Algorithm and ant colony optimization in performance.

Key words: Primal-Dual Genetic Algorithm（PDGA）, genetic algorithm, ant colony optimization, combination