A Novel Hybrid GWO with WOA for Global Numerical Optimization and
Solving Pressure Vessel Design
Abstract
Note: This paper has been accepted by the journal of neural computing
and applications.
A recent metaheuristic algorithm, such as Whale Optimization Algorithm
(WOA), was proposed. The idea of proposing this algorithm belongs to the
hunting behavior of the humpback whale. However, WOA suffers from poor
performance in the exploitation phase and stagnates in the local best
solution. Grey Wolf Optimization (GWO) is a very competitive algorithm
comparing to other common metaheuristic algorithms as it has a super
performance in the exploitation phase while it is tested on unimodal
benchmark functions. Therefore, the aim of this paper is to hybridize
GWO with WOA to overcome the problems. GWO can perform well in
exploiting optimal solutions. In this paper, a hybridized WOA with GWO
which is called WOAGWO is presented. The proposed hybridized model
consists of two steps. Firstly, the hunting mechanism of GWO is embedded
into the WOA exploitation phase with a new condition which is related to
GWO. Secondly, a new technique is added to the exploration phase to
improve the solution after each iteration. Experimentations are tested
on three different standard test functions which are called benchmark
functions: 23 common functions, 25 CEC2005 functions and 10 CEC2019
functions. The proposed WOAGWO is also evaluated against original WOA,
GWO and three other commonly used algorithms. Results show that WOAGWO
outperforms other algorithms depending on the Wilcoxon rank-sum test.
Finally, WOAGWO is likewise applied to solve an engineering problem such
as pressure vessel design. Then the results prove that WOAGWO achieves
optimum solution which is better than WOA and Fitness Dependent
Optimizer (FDO).