A powerful variant of symbiotic organisms search algorithm for global optimization. (January 2020) - Çelik, Emre
Abstract: This paper suggests a new variation to the existing symbiotic organisms search (SOS) algorithm developed by simulating three symbiotic strategies of mutualism, commensalism and parasitism used by the organisms. In the revised version called improved SOS (ISOS), the theory of quasi-oppositional based learning is employed during generation of initial population and in the parasitism phase to raise the possibility of getting closer to high-quality solutions. An efficient alternative for parasitism phase is also presented. The two upgraded parasitism strategies avoid the over exploration issue of original parasitism phase that causes unwanted long-time search in the inferior search space as the solution is already refined. To guide the algorithm perform an exhaustive search around the best solution in attempting to further improve the search model of ISOS, a chaotic local search based on the piecewise linear chaotic map is coupled into the proposed algorithm. Twenty-six benchmark functions and three engineering design problems are tested and a contrast with other popular metaheuristics is widely established. Comparative results substantiate the great contribution of proposed ISOS algorithm in solving various optimization problems with superior global search capability and convergence characteristics which render it useful in handling global optimization problems. Highlights: A new powerful variant of symbiotic organisms search algorithm is proposed. The proposal fruitfully employs quasi-oppositional learning and chaotic search. It is applied to 26 benchmark test functions and three engineering design problems. Application results are widely compared to the relevant results in literature. The findings are highly promising, affirming the overt contribution of this work.