Empirical study of sperm swarm optimization algorithm

Abstract

This paper gives an empirical study to estimate the performance of our proposed optimization method called Sperm Swarm Optimization (SSO). The SSO is evaluated frequently with different mathematical benchmark models utilized in the scope of optimization. Various asymmetric parameters and settings are chosen for these benchmark functions. The acquired results are compared with the results of four methods, such as Genetic Algorithms (GA), Parallel Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO). The outcomes present that the proposed approach outperforms other approaches in terms of quality of result because of using the technique of inherently continuous to update the sperm location. In addition, it uses different types of mutations which are utilized to increase the method convergence.