蚁群优化算法
旅行商问题
数学优化
初始化
计算机科学
元启发式
组合优化
算法
最优化问题
数学
程序设计语言
作者
Zhaojun Zhang,Kuansheng Zou
标识
DOI:10.23919/chicc.2017.8028925
摘要
Ant colony optimization (ACO) as a kind of distributed intelligent bionic optimization algorithm has been widely used to solve a variety of optimization problems, especially combinatorial optimization problems. The model and management mechanism of pheromone are very important to the performance of ACO. The Max-Min ant system (MMAS) is a classical ACO algorithm and has unique characteristics in terms of pheromones management. In this paper, we analyze the advantages and disadvantages of MMAS. Then we propose a novel ACO algorithm called simple ant colony optimization (SACO). In SACO, constant pheromone bounds are used and the update amount and initialization of pheromone are also set a constant. One of benefit is can reduce the coupling of parameters. Then we study the parameters setting about the initial value of pheromone and evaporation rate. The effect of parameters on the algorithm performance is also studied by experimental method based on traveling salesman problems. Finally, the performance of SACO is compared with other novel algorithms based on traveling salesman problems to show the feasibility and effectiveness of improvements.
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