A Hybrid Algorithm Based on Particle Swarm and Spotted Hyena Optimizer for Global Optimization

Abstract

In this paper, a novel hybrid metaheuristic optimization algorithm which is based on Particle Swarm Optimization (PSO) and recently developed Spotted Hyena Optimizer (SHO) named as Hybrid Particle Swarm and Spotted Hyena Optimizer (HPSSHO) is presented. The main concept of this algorithm is to improve the hunting strategy of Spotted Hyena Optimizer using particle swarm algorithm. The proposed algorithm is compared with four metaheuristic algorithms (i.e., SHO, PSO, DE, and GA) and benchmarked it on thirteen well-known benchmark test functions which include unimodal and multimodal. The convergence analysis of the proposed as well as other metaheuristics has also been analyzed and compared. The algorithm is tested on 25-bar real-life constraint engineering design problem to demonstrate its applicability. The experimental results reveal that the proposed algorithm performs better than other metaheuristic algorithms.