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Author Gu, Qinghua ♦ Li, Xuexian ♦ Jiang, Song ♦ {"id":"U16359098","contrib_type":"Guest Editor","orcid":"http://orcid.org/0000-0003-2151-0977","surname":"Mora","given-names":"Higinio"}
Source Hindawi
Content type Text
Publisher Hindawi
File Format PDF
Copyright Year ©2019
Language English
Abstract Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.
ISSN 10762787
Learning Resource Type Article
Publisher Date 2019-02-12
Rights License This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e-ISSN 10990526
Journal Complexity
Volume Number 2019
Page Count 18


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