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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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/2653512 |
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| author | Qinghua Gu Xuexian Li Song Jiang |
| author_facet | Qinghua Gu Xuexian Li Song Jiang |
| author_sort | Qinghua Gu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-77bb014d1d9f495d86fd2abf05f291be |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-77bb014d1d9f495d86fd2abf05f291be2025-02-03T05:50:25ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/26535122653512Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global OptimizationQinghua Gu0Xuexian Li1Song Jiang2School of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, ChinaMost 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.http://dx.doi.org/10.1155/2019/2653512 |
| spellingShingle | Qinghua Gu Xuexian Li Song Jiang Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization Complexity |
| title | Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization |
| title_full | Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization |
| title_fullStr | Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization |
| title_full_unstemmed | Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization |
| title_short | Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization |
| title_sort | hybrid genetic grey wolf algorithm for large scale global optimization |
| url | http://dx.doi.org/10.1155/2019/2653512 |
| work_keys_str_mv | AT qinghuagu hybridgeneticgreywolfalgorithmforlargescaleglobaloptimization AT xuexianli hybridgeneticgreywolfalgorithmforlargescaleglobaloptimization AT songjiang hybridgeneticgreywolfalgorithmforlargescaleglobaloptimization |