A hybird self-learning method based on particle swarm optimization and salp swarm algorithm

Abstract

This paper presents a novel self-learning hybrid optimization algorithm based on the particle swarm optimization (PSO) algorithm and the salp swarm algorithm (SSA) algorithm, namely HSL-PSO-SSA, for solving the function optimization problems. In HSL-PSO-SSA, three search strategies based on the ideas of PSO and SSA are adopted and a probability model is designed to determine the probability of a search strategy being used to update an individual in the search population. The performance of the HSL-PSO-SSA is investigated on solving the unimodal and multimodal benchmark functions. From the experimental results, it is observed that the proposed HSL-PSO-SSA outperforms the compared algorithms including the standard PSO and the original SSA.