Ant colony optimization with self-adaptive evaporation rate in dynamic environments
The performance of ant colony optimization (ACO) algorithms in tackling optimization problems strongly depends on different parameters. One of the most important parameters in ACO algorithms when addressing dynamic optimization problems (DOPs) is the pheromone evaporation rate. The role of pheromone evaporation in DOPs is to improve the adaptation capabilities of the algorithm. When a dynamic change occurs, the pheromone trails of the previous environment will not match the new environment especially if the changing environments are not similar. Therefore, pheromone evaporation helps to eliminate pheromone trails that may misguide ants without destroying any knowledge gained from previous environments. In this paper, a self-adaptive evaporation mechanism is proposed in which ants are responsible to select an appropriate evaporation rate while tracking the moving optimum in DOPs. Experimental results show the efficiency of the proposed self-adaptive evaporation mechanism on improving the performance of ACO algorithms for DOPs.
Citation : Mavrovouniotis, M. and Yang, S. (2014) Ant colony optimization with self-adaptive evaporation rate in dynamic environments. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 47-54
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes