A Symbiotic Organisms Search Algorithm With Memory Guidance for Global Optimization Problems

Abstract

Symbiotic organisms search (SOS) algorithm is a nature-inspired meta-heuristic algorithm, which has been successfully applied to solve a large number of problems of different areas. In this work, a novel modified variant of SOS with a memory strategy and good-point set (GMSOS) is proposed to improve the properties of exploration and exploitation. For improving the population diversity and the search capability of the SOS algorithm, the good point set theory rather than random selection is used to generate the initial population, and the memory strategy is adopted in three phases of the SOS algorithm, which aims at maintaining the trade-off between exploration and exploitation effectively, and preventing the current best solution from getting trapped into local optima. The performance of the proposed version of SOS is tested on standard benchmark functions with different characteristics and real-world problems. The numerical and statistical results on these applications demonstrate the competitive ability of the proposed algorithm as compared to other popular algorithms available in the literature.