摘要
针对蚁群算法易陷入局部最优、收敛速度较慢的问题,提出一种基于粒子群参数优化的同构双种群蚁群算法。将蚂蚁均分为两个子群,第一子群引入单位距离信息素路径构建算子,加强距离因素和信息素因子的协同作用;第二子群引入粒子群优化算法,对蚁群算法的多个参数在三维空间中进行优化,提高了解的质量。两个种群在参数方面优势互补并进行协同交流,共同促进算法找到全局最优解。针对TSP问题,实验表明,所设计的算法增强了算法的种群多样性。
In order to solve the problem that the ant colony algorithm is easy to fall into local optimum and the convergence speed is slow,a homogeneous dual ant colony algorithm based on particle swarm optimization(PSO)is proposed.The ants are divided into two subgroups.The first subgroup introduces a unit distance pheromone path construction operator to enhance the synergy between distance factors and pheromone factors.The second subgroup introduces PSO algorithm to optimize multiple parameters of ant colony alorithm in three-dimensional space to improve the quality of solution.The two subgroups complement each other's advantages in terms of parameters and communicate synergistically to promote the algorithm to find the global optimal solution.Experiments show that the proposed algorithm enhances the population diversity of the algorithm.
作者
朱宏伟
游晓明
刘升
ZHU Hong-wei;YOU Xiao-ming;LIU Sheng(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;School of Management,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《测控技术》
2019年第9期24-29,共6页
Measurement & Control Technology
基金
国家自然科学基金(61673258,61075115,61403249,61603242)
关键词
蚁群算法
粒子群优化算法
协同交流
TSP
ant colony algorithm
particle swarm optimization
collaborative communication
TSP
