Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. (15th July 2022)

• Record Type: Journal Article
• Authors: Chopra, Nitish; Mohsin Ansari, Muhammad
• Is Part Of: Expert systems with applications. Volume 198(2022)
• Publication Date: 2022-07-15
• Subjects: Nature-inspired algorithm -- Golden Jackal optimization -- Metaheuristic -- Constrained problems -- Optimization algorithm -- GJO; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116924
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Developed Golden Jackal Optimization (GJO) Algorithm as an optimization method. Tested the performance of proposed algorithm against mathematical and engineering benchmarks. Compared proposed algorithm with other well-known optimization algorithms. Conducted statistical analyses. Demonstrated superiority of proposed algorithm in various conditions. Abstract: A new nature-inspired optimization method, named the Golden Jackal Optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems. GJO is inspired by the collaborative hunting behaviour of the golden jackals (Canis aureus). The three elementary steps of algorithm are prey searching, enclosing, and pouncing, which are mathematically modelled and applied. The ability of proposed algorithm is assessed, by comparing with different state of the art metaheuristics, on benchmark functions. The proposed algorithm is further tested for solving seven different engineering design problems and introduces a real implementation of the proposed method in the field of electrical engineering. The results of the classical engineering design problems and real implementation verify that the proposed algorithm is appropriate for tackling challenging problems with unidentified search spaces.