A novel chaos-integrated symbiotic organisms search algorithm for global optimization

Abstract

Symbiotic organisms search (SOS) algorithm imitates the symbiotic relationship between different biological species. Simulation procedure of this algorithm is in three different phases, viz. mutualism, commensalism and parasitism. In this paper, the basic SOS algorithm is reduced and a chaotic local search is integrated into the reduced SOS to form chaotic SOS (CSOS) for improving the solution accuracy and convergence mobility of the basic SOS algorithm. The proposed CSOS algorithm is implemented and tested, successfully, on twenty-six unconstrained benchmark test functions. Experimental results presented in this paper are compared to those offered by the basic SOS. Additionally, the proposed algorithm is utilized to solve a real-world power system problem (siting and sizing problem of distributed generators in radial distribution system). The results presented in this paper show that the proposed CSOS algorithm yields superior solution over the other popular techniques in terms of convergence characteristics and global search ability for both benchmark function optimization and power engineering optimization task.