Multi-strategy white shark optimiser and its engineering applications

White Shark Optimisation Algorithm (WSO) is a novel metaheuristic algorithm proposed in recent years, crucial for solving optimisation problems in continuous search spaces. However, it suffers from limited exploration and susceptibility to local optima in complex problems. To enhance the optimisation performance of WSO, a Multi-Strategy Integrated White Shark Optimisation (MIWSO) algorithm is proposed, incorporating refracted opposition-based learning for population diversity, adaptive inertia weight for improving search balance, and Cauchy-Gaussian mutation for enhancing escape from local optima. Benchmark evaluations on CEC 2017 and CEC 2022, along with Wilcoxon rank-sum tests, show that MIWSO outperforms eight peer algorithms in convergence accuracy and robustness. Meanwhile, the proposed algorithm is applied to solve two engineering optimisation problems, the tension/compression spring and pressure vessel design, where it achieves reductions in objective function values of 3.74% and 2.39% compared to WSO, further verifying the superiority and applicability of the MIWSO algorithm.