A novel metaheuristic global optimisation method based on grey wolf optimiser and salp swarm algorithm
Mohamed Abderraouf Ferradji, Rachid Seghir · Journal of Experimental & Theoretical Artificial Intelligence · 2025
Grey-Wolf Optimizer (GWO) and Salp-Swarm Algorithm (SSA) are among the recent metaheuristic algorithms that have shown considerable potential in solving practical problems. Nevertheless, these optimisation methods usually suffer from a poor performance, especially when dealing with multimodal and high-dimensional problems. To overcome drawbacks of these algorithms, a new population-based global optimisation algorithm inspired by GWO and SSA is proposed in this paper. The basic idea behind the proposed approach is to divide the population into two groups that adopt new position updating strategies. The movement method of the first group is inspired by the hunting mechanism used in GWO algorithm. On the other hand, the population of the second group moves into the search space using a new strategy based on the position updating method employed by the follower salps in SSA. The performances of the proposed algorithm, named GW-SSA, have been studied through 23 benchmark functions of different types and dimensions. The obtained results were statistically analysed using Wilcoxon signed-rank. Furthermore, GW-SSA algorithm has been applied to solve four real engineering design problems. The experimental results show the superior performance of GW-SSA compared to GWO, SSA and other recently proposed optimisation algorithms.