Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors

• Published in: Applied soft computing Vol. 13; no. 10; pp. 4023 - 4037
• Main Authors: Mavrovouniotis, Michalis, Yang, Shengxiang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2013
• Subjects: Ant colony optimization; Dynamic optimization problem; Dynamic travelling salesman problem; Immigrants schemes; Traffic factor; Traffic factor; Ant colony optimization; Dynamic travelling salesman problem; Immigrants schemes; Dynamic optimization problem
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2013.05.022

[Display omitted] •A novel ant colony optimization framework to address dynamic environments is presented.•Integration of immigrants schemes with ant colony optimization algorithms.•A benchmark generator that generates dynamic travelling salesman problems with traffic factors. Traditional ant colony optimization (ACO) algorithms have difficulty in addressing dynamic optimization problems (DOPs). This is because once the algorithm converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to a new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution to address this problem is to increase the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants schemes. In this paper, an ACO framework for dynamic environments is proposed where different immigrants schemes, including random immigrants, elitism-based immigrants, and memory-based immigrants, are integrated into ACO algorithms for solving DOPs. From this framework, three ACO algorithms, where immigrant ants are generated using the aforementioned immigrants schemes and replace existing ants in the current population, are proposed and investigated. Moreover, two novel types of dynamic travelling salesman problems (DTSPs) with traffic factors, i.e., under random and cyclic dynamic environments, are proposed for the experimental study. The experimental results based on different DTSP test cases show that each proposed algorithm performs well on different environmental cases and that the proposed algorithms outperform several other peer ACO algorithms.