Niching Grey Wolf Optimizer for Multimodal Optimization Problems

Metaheuristic algorithms are widely used for optimization in both research and the industrial community simplicity, flexibility, robustness. However, multi-modal is a difficult task, even metaheuristic algorithms. Two important issues that need to be handled solving problems (a) categorize multiple local/global optima (b) uphold these till ending. Besides, robust local search ability also prerequisite reach exact global optima. Grey Wolf Optimizer (GWO) recently developed nature-inspired algorithm requires less parameter tuning. GWO suffers from premature convergence fails maintain balance between exploration exploitation problems. This study proposes niching (NGWO) incorporates personal best features of PSO technique address issues. The proposed has been tested 23 benchmark functions three engineering cases. NGWO outperformed all other considered most test compared state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya two improved variants CSA. Statistical analysis Friedman tests have conducted compare performance thoroughly.