Cauchy Grey Wolf Optimiser for continuous optimisation problems. Issue 6 (2nd November 2018)

• Record Type: Journal Article
• Authors: Gupta, Shubham; Deep, Kusum
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 30:Issue 6(2018)
• Page Start: 1051
• Page End: 1075
• Publication Date: 2018-11-02
• Subjects: Grey Wolf Optimiser -- optimisation -- swarm intelligence -- Cauchy distribution; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2018.1513080
• Languages: English
• ISSNs: 0952-813X

Abstract

ABSTRACT: Grey Wolf Optimiser (GWO) is a recently developed optimisation approach to solve complex non-linear optimisation problems. It is relatively simple and leadership-hierarchy based approach in the class of Swarm Intelligence based algorithms. For solving complex real-world non-linear optimisation problems, the search equation provided in GWO is not of sufficient explorative behaviour. Therefore, in the present paper, an attempt has been made to increase the exploration capability along with the exploitation of a search space by proposing an improved version of classical GWO. The proposed algorithm is named as Cauchy-GWO. In Cauchy-GWO Cauchy operator has been integrated in which first two new wolves are generated with the help of Cauchy distributed random numbers and then another new wolf is generated by taking the convex combination of these new wolves. The performance of Cauchy-GWO is exhibited on standard IEEE CEC 2014 benchmark problem set. Statistical analysis of the results on CEC 2014 benchmark set and popular evaluation criteria, Performance Index (PI) proves that Cauchy-GWO outperforms GWO in terms of error values defined in IEEE CEC 2014 benchmarks collection. Later on in the paper, GWO and Cauchy-GWO algorithms have been used to solve three well-known engineering application problems and two problems of reliability. From the analysis conducted in the present paper, it can be concluded that the proposed algorithm, Cauchy-GWO is reliable and efficient algorithm to solve continuous benchmark test problems, as well as real-life applications problems.