Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem
Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic travelling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Mavrovouniotis, M., Yang, S., Van, M., Li, C. and Polycarpou, M. (2019) Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem. IEEE Computational Intelligence Magazine,
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes