Abstract
This article studies the efficiency of an Ant Colony Optimization (ACO) algorithm for the solving of electromagnetic optimization problems. A modified version of the ACO for continuous optimization, namely ACO R , is used to solve two benchmark electromagnetic problems referring to the optimization of coils configuration. After choosing the appropriate size of the population of ants, the ACO R algorithm performances are compared with results obtained with other evolutionary computation strategies such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). For one problem ACO R outperforms the variants of the GA and PSO used at all statistical criteria checked, whereas for the other problem ACO R is ranked as the second best after the PSO based algorithm tested.
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