Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization

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 Gre...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2019-01, Vol.2019 (2019), p.1-18
Main Authors: Gu, Qinghua, Jiang, Song, Li, Xuexian
Format: Article
Language:English
Subjects:
Algorithms
Animal populations
Computer simulation
Decomposition
Design engineering
Evolution
Genetic algorithms
Global optimization
Heuristic methods
Intelligence
International conferences
Job shops
Machine learning
Nonlinear programming
Optimization algorithms
Scheduling
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Summary: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:1076-2787
1099-0526
DOI:10.1155/2019/2653512