Enhancing the Social Learning Ability of Spider Monkey Optimization Algorithm

In the arena of swarm intelligence algorithms, spider monkey works as a very powerful algorithm. In this article, an efficient modification of SMO is presented. In the proposed method the social learning of a spider monkey is enhanced with using the local leader of the neighboring group. The proposed algorithm is titled the social learner spider monkey optimization algorithm (SLSMO). This modified variant exploits the search space efficiently as well as the convergence speed of the algorithm is also enhanced respected the optimal solution. For validating the authenticity of this proposed SLSMO, it is collated with three benchmark sets i.e. 23 global optimization problems, 3 engineering design problems, and 16 constraint optimization problems. The attained outcomes are also collated with the significant approaches available in the literature. The obtained outcomes prove the authenticity of the propounded approach.