A New Salp Swarm Algorithm for the Numerical Optimization Problems Based on An Elite Opposition-based Learning

Abstract

An Elite Opposition-based Learning Salp Swarm Algorithm (EOSSA) for the numerical optimization problems is introduced in this article. An attempt is made to introduce a hybrid model of two new paradigms: Salp Swarm Algorithm (SSA) and Elite Opposition-based Learning (EOL). SSA is a novel population-based meta-heuristic optimization algorithm with excellent edges while solving real-world optimization problems. EOL is a novel scheme in intelligent computing with advantages of economic and practical in solving optimization problems. The objective of this hybridization is to augment the exploration and evaluation aptitude of the original SSA. The obtained result of the proposed strategy is evaluated on the different configurations of well-known global benchmark functions and compared with the development of standard SSA. The experimental results show that EOSSA is competitive to the traditional SSA approaches.