A Novel Hybrid Optimization Algorithm Combining GWO and SSA

Introduction: Metaheuristic algorithms often face challenges in global search and local optima in complex optimization tasks. Method: We propose TGWOSSA, a hybrid algorithm combining GWO, SSA, and an adaptive tdistribution strategy to enhance optimization. TGWOSSA uses an improved Sine chaotic map for initialization, enhances GWO with a nonlinear convergence factor, and applies SSA for local search. It dynamically switches between GWO and SSA and employs t-distribution mutation to avoid local optima. Results: Experiments on CEC2017 and CEC2022 functions show TGWOSSA outperforms other algorithms, excelling in 19 real-world engineering problems. Discussion: TGWOSSA was verified with strong performance; however, more focus needs to be put on developing suitable constraint handling techniques for TGWOSSA to enhance its performance in solving real-world constrained optimization problems. Conclusion: TGWOSSA provides strong performance in complex optimization tasks. The patented technology will be applied in the future.