文献类型：期刊文献

英文题名：An intelligent ant colony optimization using genetic operators and particles

作者：Kong, Xiangling[1]; Li, Hongxing[2]; Zhang, Yinong[2]

第一作者：Kong, Xiangling

通讯作者：Li, Hongxing

机构：[1] Beijing Union University, College of Information Technology, Beijing, China; [2] Beijing Union University, College of Automation, Beijing, China

第一机构：北京联合大学智慧城市学院

年份：2014

卷号：32

期号：5

起止页码：3713-3724

外文期刊名：Energy Education Science and Technology Part A: Energy Science and Research

收录：EI(收录号：20150700515904);Scopus(收录号：2-s2.0-84922432607)

语种：英文

外文关键词：Ant colony optimization - Artificial intelligence - Evolutionary algorithms - Genetic algorithms - Optimization - Particle swarm optimization (PSO) - Problem solving - Traveling salesman problem

摘要：Ant colony algorithm is a novel simulated evolutionary algorithm in recent years. It is widely used to solve optimization problems for discrete systems. However, the ant colony algorithm is used to have slow convergence and premature stagnation, when it is employed to optimize the complex system. This will affect the scope of its use. In order to solve the above problems, this paper presents an intelligent ant colony optimization algorithm based on genetic algorithm. It is introduced the idea of particle swarm algorithm in the ant colony algorithm, so the ants with particles. Besides, local and global optimal solutions are implemented by crossover and mutation operator, to dynamically update the pheromone. In this paper, the travelling salesman problem is solved by proposed algorithm and obtained satisfaction results. The emulation results show that it is effective. ? Sila Science. All rights reserved.