EMPSACO: AN IMPROVED HYBRID OPTIMIZATION ALGORITHM BASED ON PARTICLE SWARM, ANT COLONY AND ELITIST MUTATION ALGORITHMS

This research presents an efficient and reliable swarm intelligence-based approach, ant colony optimization and elitist-mutated particle swarm optimization. Methods of particle swarm optimization (PSO) and ant colony optimization (ACO) and elitist mutation particle swarm optimization (EMPSO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants and Elitist mutation taken from genetic mutation from genetic algorithm techniques. In this study, we explore a simple approach to improve the performance of the PSO method for optimization of multimodal continuous functions. The proposed EMPSACO algorithm is tested on several test functions from the usual literature and compared with PSO, PSACO and GA (Genetic Algorithm). Results showed that the effectiveness and efficiency of the proposed EMPSACO method had suitable accuracy to optimize multimodal functions.