Multi-strategy grey wolf optimization algorithm for global optimization and engineering applications

The grey wolf optimizer(GWO), a population-based meta-heuristic algorithm, mimics the predatory behavior of grey wolf packs. Continuously exploring and introducing improvement mechanisms is one of the keys to drive the development and application of GWO algorithms. To overcome the premature and stagnation of GWO, the paper proposes a multiple strategy grey wolf optimization algorithm (MSGWO). Firstly, an variable weights strategy is proposed to improve convergence rate by adjusting the weights dynamically. Secondly, this paper proposes a reverse learning strategy, which randomly reverses some individuals to improve the global search ability. Thirdly, the chain predation strategy is designed to allow the search agent to be guided by both the best individual and the previous individual. Finally, this paper proposes a rotation predation strategy, which regards the position of the current best individual as the pivot and rotate other members for enhacing the exploitation ability. To verify the performance of the proposed technique, MSGWO is compared with seven state-of-the-art meta-heuristics and four variant GWO algorithms on CEC2022 benchmark functions and three engineering optimization problems. The results demonstrate that MSGWO has better performance on most of benchmark functions and shows competitive in solving engineering design problems.