The Combination Of Primal-dual Genetic Algorithm And Ant Colony Optimization

Primal-Dual Genetic Algorithm（PDGA） is an optimization algorithm which is based onthe mechanism of evolution. Although the algorithm has the features of population diversity,operation stability and parallelity, it can’t make full use of enough system feedback information.Its efficiency is reduced, since it has to do a lot of redundant iterations. Ant Colony Optimization(ACO) converges to the optimization path by information pheromone accumulation and renewal.It is of parallel processing and global searching. However, its convergence speed is slow becauseof poor pheromone during the early period.Based on the research of PDGA and ACO, this paper summarizes the advantages anddisadvantages of the two algorithms and puts forward a combination algorithm of Primal-DualGenetic Algorithm and Ant Colony Optimization (PDGAA). The numerical test examples showthat PDGAA exceeds Genetic Algorithm (GA)、PDGA、ACOCombination of Genetic Algorithmand Ant Algorithm (GAAA) in both performance and time.