A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

doi:10.1007/s40305-021-00341-0

Abstract

Abstract: In this paper, a hybrid of grey wolf optimization (GWO) and genetic algorithm (GA) has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system. It was named as hybrid of grey wolf optimization and genetic algorithm (HGWOGA). HGWOGA was applied to this hybrid problem through three procedures.First,thebalancebetweentheexplorationandtheexploitationprocesswas done by grey wolf optimizer algorithm. Then, we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes. At last, mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima. MATLAB code was designed to implement the proposed methodology. The result of this algorithm is compared with the results of iteration method, GWO, GA, artificial bee colony (ABC) and particle swarm optimization (PSO) techniques. The results obtained by this algorithm are better when compared with those mentioned in the text.

Key words: Hybrid renewable energy, Optimization, Nature-inspired algorithm, Grey wolf optimization, Genetic algorithm

CLC Number:

Diriba Kajela Geleta, Mukhdeep Singh Manshahia. A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System[J]. Journal of the Operations Research Society of China, 2022, 10(4): 749-762.

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