Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. (December 2017)

• Record Type: Journal Article
• Authors: Dhiman, Gaurav; Kumar, Vijay
• Is Part Of: Advances in engineering software. Volume 114(2017)
• Page Start: 48
• Page End: 70
• Publication Date: 2017-12
• Subjects: Optimization -- Optimization techniques -- Metaheuristics -- Constrained optimization -- Unconstrained optimization -- Benchmark test functions; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2017.05.014
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A new Metaheuristic named spotted hyena optimization algorithm inspired by hyenas is proposed. The SHO algorithm is tested on 29 well-known benchmark test functions. The exploitation property of SHO is confirmed by the results obtained on unimodal benchmark test functions. The exploration capability of SHO is validated by the results obtained on multimodal benchmark test functions. The results on five constrained engineering problems prove the applicability of SHO in real-life applications. Abstract: This paper presents a novel metaheuristic algorithm named as Spotted Hyena Optimizer (SHO) inspired by the behavior of spotted hyenas. The main concept behind this algorithm is the social relationship between spotted hyenas and their collaborative behavior. The three basic steps of SHO are searching for prey, encircling, and attacking prey and all three are mathematically modeled and implemented. The proposed algorithm is compared with eight recently developed metaheuristic algorithms on 29 well-known benchmark test functions. The convergence and computational complexity is also analyzed. The proposed algorithm is applied to five real-life constraint and one unconstrained engineering design problems to demonstrate their applicability. The experimental results reveal that the proposed algorithm performs better than the other competitive metaheuristic algorithms.