Large-scale global optimization based on hybrid swarm intelligence algorithm

Article type: Research Article

Authors: Mashwani, Wali Khan^{a; *} | Hamdi, Abdelouahed^b | Asif Jan, Muhammad^a | Göktaş, Atila^c | Khan, Fouzia^a

Affiliations: [a] Institute of Numerical Sciences, Kohat University of Science & Technology, KPK, Pakistan | [b] Department of Mathematics, Statistics & Physics College of Arts and Sciences, University of Qatar, Doha, Qatar | [c] Department of Statistics, Mugla Sitki Kocman University, Turkey

Correspondence: [*] Corresponding author. Wali Khan Mashwani, Institute of Numerical Sciences, Kohat University of Science & Technology, KPK, Pakistan. E-mail: mashwanigr8@gmail.com.

Note: [1] https://www.britannica.com/science/optimization

Abstract: There are numerous large-scale global optimization problems encountered in real-world applications including engineering, manufacturing, economics, networking fields. Over the last two decades different varieties of swarm intelligence and nature inspired based evolutionary algorithms (EAs) were developed and still. Among them, particles swarm optimization, Firefly algorithm, Ant colony optimization, Bat algorithm are the most popular and recently developed leading swarm intelligence based approaches. They are mainly inspired by the social and cooperative behaviors of swarm likewise herds of animals, flocking of birds, schooling of fish, ant colonies, herds of bisons and packs of wolves working together for their common benefit. Due to easy implementation and high capability in achieving of absolute optimum, swarm intelligence based algorithms have attained a great deal attention in both academic and industrial applications. This paper proposes a hybrid swarm intelligence (HSI) algorithm that employs the Bat Algorithm (BA) and the Practical Swarm Optimization (PSO) as constituents to perform their search process for dealing with recently designed benchmark functions in the special session of the 2017 IEEE congress of evolutionary computation (CEC’17) [3]. The approximate solutions for most of the CEC’17 benchmark functions obtained by the suggested algorithm in its twenty five independent runs of trails are much promising as compared to its competitors.

Keywords: Global optimization, optimization problems, soft computing, evolutionary computing (EC), evolutionary algorithms (EAs), swarm intelligence based approaches and hybrid swarm intelligence algorithm

DOI: 10.3233/JIFS-192162

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1257-1275, 2020