Enhanced grey wolf optimisation algorithm for constrained optimisation problems

Abstract

Grey wolf optimiser (GWO) is a recent, fast and easy-to-implement, nature inspired meta-heuristic optimisation algorithm that focuses on social behaviour of grey wolves. GWO algorithm is prominent in terms of finding global optima without getting trapped in premature convergence. In order to find a fast convergent behaviour of GWO, an enhanced grey wolf optimisation (EGWO) algorithm is proposed in this paper. Basically, GWO is modified in two ways in this study, first, to improve exploitation capability of GWO, the hunting mechanism makes the best use of the global best solution, i.e., alpha and secondly, a random parameter of existing GWO algorithm is emended in order to produce promising results compared to state-of-the-art algorithms. To validate the effectiveness of proposed EGWO algorithm, penalty function is consolidated and diverse experiments are executed on different constrained benchmark functions of different complexities and characteristics. Further, a classical engineering design problem (pressure vessel) is solved using the proposed algorithm. The performance evaluation of proposed EGWO algorithm along with other standard meta-heuristic optimisation algorithms proved that the proposed EGWO algorithm to be a competitive algorithm in the field of nature inspired meta-heuristic optimisation algorithms.