A conceptual and practical comparison of PSO-style optimization algorithms

• Published in: Expert systems with applications Vol. 167; p. 114430
• Main Authors: Tharwat, Alaa, Schenck, Wolfram
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.04.2021; Elsevier BV
• Subjects: Algorithms; Bat algorithm; Gray wolf optimization (GWO); Nature-inspired optimization algorithms; Optimization; Optimization algorithms; Particle swarm optimization (PSO); Sine–cosine algorithm (SCA); Swarm optimization algorithms; Whale optimization algorithm (WOA); Bat algorithm; Particle swarm optimization (PSO); Whale optimization algorithm (WOA); Nature-inspired optimization algorithms; Gray wolf optimization (GWO); Swarm optimization algorithms; Sine–cosine algorithm (SCA)
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.114430

Optimization algorithms are widely employed for finding optimal solutions in many applications. Stochastic optimization algorithms including nature-inspired optimization algorithms are simple and easy to implement, and this is the reason why there is a growing interest in this research area. Recently, many nature-inspired optimization algorithms have been proposed for solving many optimization problems. Moreover, with the aim of improving the performance of optimization algorithms, some modifications were applied such as combining different algorithms and employing some sampling techniques for replacing critical parameters in the optimization algorithms. This paper compares five different widely used PSO-style optimization algorithms to investigate if there is a significant difference between them or not. Theoretically, we explain different PSO-style algorithms and discuss the similarities and differences between them. Practically, a number of experiments were conducted to compare these algorithms. Theoretical analysis and practical results prove that there is not any significant difference between the PSO-style algorithms regarding their performance. •Nature-inspired optimization algorithms are used to solve optimization problems.•Many comparisons are presented to compare PSO-style optimization algorithms.•Under the same metaheuristic framework, the algorithms perform similarly.