%0 Journal Article %T EMPSACO: AN IMPROVED HYBRID OPTIMIZATION ALGORITHM BASED ON PARTICLE SWARM, ANT COLONY AND ELITIST MUTATION ALGORITHMS %J Iranian Journal of Science and Technology Transactions of Civil Engineering %I Shiraz University %Z 2228-6160 %D 2013 %\ 12/18/2013 %V 37 %N C+ %P 491-501 %! EMPSACO: AN IMPROVED HYBRID OPTIMIZATION ALGORITHM BASED ON PARTICLE SWARM, ANT COLONY AND ELITIST MUTATION ALGORITHMS %K Particle swarm optimization %K ant colony %K elitist mutation %K metaheuristics %K EMPSACO %R 10.22099/ijstc.2013.1802 %X This research presents an efficient and reliable swarm intelligence-based approach, antcolony optimization and elitist-mutated particle swarm optimization. Methods of particle swarmoptimization (PSO) and ant colony optimization (ACO) and elitist mutation particle swarmoptimization (EMPSO) are co-operative, population-based global search swarm intelligencemetaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACOimitates foraging behavior of real life ants and Elitist mutation taken from genetic mutation fromgenetic algorithm techniques. In this study, we explore a simple approach to improve theperformance of the PSO method for optimization of multimodal continuous functions. Theproposed EMPSACO algorithm is tested on several test functions from the usual literature andcompared with PSO, PSACO and GA (Genetic Algorithm). Results showed that the effectivenessand efficiency of the proposed EMPSACO method had suitable accuracy to optimize multimodalfunctions. %U https://ijstc.shirazu.ac.ir/article_1802_e8a7cb2955e24b18bafc13f54bbd0587.pdf