【摘要】 为了充分利用蚁群算法的并行、正反馈机制、高效求解和遗传算法的随机、快速以及全局收敛等优点,在分析遗传算法的选择、交叉、变异等策略和蚁群算法的寻优策略基础上,基于多种群和多策略,提出一种带有参数自适应调整的混合遗传-蚁群(HPSGAO)算法。在HPSGAO算法的每次循环中,遗传算法获得最优解用于初始化蚁群算法的信息素分配,以实现遗传策略和蚁群策略的有效结合,动态平衡HPSGAO算法的收索范围与收敛速度间的矛盾,进而提高HPSGAO算法的全局择优能力。为了验证提出混合遗传-蚁群算法的优化性能,选择10个TSP问题进行测试,仿真实验结果表明,在多次循环后,HPSGAO算法具有遗传算法和蚁群算法的优势互补,以及较好的求解效率。更多还原

【Abstract】 In order to make full use of the parallel,positive feedback mechanism and efficient solution of ant colony optimization(ACO)algorithm,and the stochastic,fast speed and global convergence of genetic algorithm(GA),on the basis of analyzing selection,crossover and mutation strategies of GA and optimization strategy of ACO algorithm,a hybrid genetic-ant colony(HPS-GAO)algorithm based on multi-population and multi-strategy is proposed in this paper.In each iteration of the proposed HPSGAO algorithm,the GA can obtain the optimal solution to initialize the pheromone distribution of ACO algorithm in order to achieve the effective combination of the GA and ACO algorithm,dynamically balance the contradiction between the convergence speed and convergence range and improve the global ability of the HPSGAO algorithm.In order to verify the optimization performance of the proposed HPSGAO algorithm,ten TSP cases are selected to test.The simulation results show that the proposed HPSGAO algorithm takes on the complementary advantages of the GA and ACO algorithm,and has better solving efficiency.更多还原