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

10.22099/ijstc.2013.1802

Abstract

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.

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