Abstract
The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO) and Whale Optimizer Algorithm (WOA). We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO) algorithm: (i) firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii) secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.
Highlights
Many metaheuristic algorithms have been developed by researchers and scientists in different fields
We have developed an improved hybrid algorithm utilizing the strengths of Whale Optimizer Algorithm and Grey Wolf Optimizer algorithm
The global optimal solution quality of benchmark function has been improved with Hybrid Whale Optimizer Algorithm (WOA)–Mean Grey Wolf Optimizer (MGWO) as it extracts quality characteristics of both WOA and MGWO
Summary
Many metaheuristic algorithms have been developed by researchers and scientists in different fields. These include Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), Bat Algorithm (BA), Biogeographically Based Optimization (BBO), Firefly Algorithm (FA), Sine Cosine Algorithm (SCA), Robust Optimization (RO), Grey Wolf Optimizer (GWO), Whale Optimizer Algorithm (WOA), Mean Grey Wolf Optimizer (MGWO) and many others. The goal of all metaheuristics is to balance the capability of exploration and exploitation in order to search for the best global optimal solution in the search space. Several algorithms have been developed to improve the convergence performance of GWO that includes parallelized GWO [8,9], a hybrid version of GWO with PSO [10] and binary GWO [11].
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