A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems

Grey Wolf Optimizer (GWO) tends to converge prematurely when dealing with multimodal problems. Using the benefits of hybridizing algorithm to boost the performance of GWO is a recent trend. Therefore, a novel improved GWO called collaboration-based Hybrid GWO-SCA optimizer (cHGWOSCA) is developed. Given the powerful exploration of Sine Cosine Algorithm (SCA), SCA is incorporated into the position update of leading wolves in GWO. Then a collaboration between the personal best and leading wolves is applied in the hybridized position update, which can improve the global exploration. To balance the exploitation, weight-based individual position update and crossover with personal best are used to guide the exploitation of promising areas. The factor a -> modified by a sine function is employed to equilibrate exploration and exploitation. In addition, the global convergence of cHGWOSCA is proved. IEEE CEC 2013, 2014 and 2019 are applied to verify the validity of cHGWOSCA. PV model parameter extraction and three constrained engineering design problems are used to further demonstrate the performance of cHGWOSCA. Experimental results indicate that cHGWOSCA is a high -performing algorithm in global optimization.