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A recent metaheuristic algorithm, such as Whale Optimization Algorithm (WOA),was proposed. The idea of proposing this algorithm belongs to the huntingbehavior of the humpback whale. However, WOA suffers from poor performance inthe exploitation phase and stagnates in the local best solution. Grey WolfOptimization (GWO) is a very competitive algorithm comparing to other commonmetaheuristic algorithms as it has a super performance in the exploitationphase while it is tested on unimodal benchmark functions. Therefore, the aim ofthis paper is to hybridize GWO with WOA to overcome the problems. GWO canperform well in exploiting optimal solutions. In this paper, a hybridized WOAwith GWO which is called WOAGWO is presented. The proposed hybridized modelconsists of two steps. Firstly, the hunting mechanism of GWO is embedded intothe WOA exploitation phase with a new condition which is related to GWO.Secondly, a new technique is added to the exploration phase to improve thesolution after each iteration. Experimentations are tested on three differentstandard test functions which are called benchmark functions: 23 commonfunctions, 25 CEC2005 functions and 10 CEC2019 functions. The proposed WOAGWOis also evaluated against original WOA, GWO and three other commonly usedalgorithms. Results show that WOAGWO outperforms other algorithms depending onthe Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve anengineering problem such as pressure vessel design. Then the results prove thatWOAGWO achieves optimum solution which is better than WOA and Fitness DependentOptimizer (FDO).

A Novel Hybrid GWO with WOA for Global Numerical Optimization and Solving Pressure Vessel Design