A novel hybrid GWO-PSO-CSA for achieving an optimal solution of the manipulators

This work presents a novel Hybrid meta-heuristic that combines the grey wolf optimizer (GWO), particle swarm optimization (PSO), and crow search method (CSA). Initially, our approach integrated the two techniques GWO and PSO in the initialization then followed by CSA to explore and selection is investigated. Our approach has been examined using an 18 benchmark function, a single-link manipulator(SLM), a single-link flexible manipulator (SLFM), and a double-link manipulator (DLM). We compared our method against the CSA, GWO, PSO, DE, GEO, JS, and WSO algorithms throughout our assessments. Our simulation findings in MATLAB 2014 demonstrate that our hybrid strategy effectively combines the three algorithms and outperforms all comparative approaches. In addition, the results demonstrate that our method converges to more optimum solutions with fewer iterations.