![](/images/graphics-bg.png)
Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization
Joint Authors
Gu, Qinghua
Li, Xuexian
Jiang, Song
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-12
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gu, Qinghua& Li, Xuexian& Jiang, Song. 2019. Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization. Complexity،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1131263
Modern Language Association (MLA)
Gu, Qinghua…[et al.]. Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization. Complexity No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1131263
American Medical Association (AMA)
Gu, Qinghua& Li, Xuexian& Jiang, Song. Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization. Complexity. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1131263
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131263