An improved symbiotic organisms search algorithm with good point set and memory mechanism

Symbiotic organisms search (SOS) algorithm is a current popular stochastic optimization algorithm. It has been widely used to handle all kinds of optimization problems, whereas SOS has some disadvantages, such as over-exploration phenomenon and unbalance between exploration and exploitation. To improve the search capability of SOS, in this study, a novel improved SOS (GMSOS) with good point set and memory mechanism is presented. For enhancing the population diversity and the optimization ability of SOS algorithm, good point set instead of uniform distribution is utilized to produce the initial population, and memory mechanism is employed in three stages of SOS algorithm. In the mutualism stage and commensalism stage, history best organism in memory takes the place of the current best organism. In the parasitism stage, the new parasite vector based on history best organism is produced. These strategies help to effectively provide a better trade-off between exploration and exploitation in the search scope, and avoiding falling into local optima synchronously. The performance of the presented SOS is evaluated on 35 typical benchmark functions and 3 engineering design problems. The experimental results attest that the proposed algorithm is competitive as compared to other algorithms considered.