An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems
As an efficient meta-heuristic algorithm, the whale optimization algorithm (WOA) has been extensively applied to practical problems. However, WOA still has the drawbacks of converging slowly, and jumping out from extreme points especially for large scale optimization problems. To overcome these defe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/9/1475 |
| _version_ | 1797505102752251904 |
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| author | Guanglei Sun Youlin Shang Roxin Zhang |
| author_facet | Guanglei Sun Youlin Shang Roxin Zhang |
| author_sort | Guanglei Sun |
| collection | DOAJ |
| description | As an efficient meta-heuristic algorithm, the whale optimization algorithm (WOA) has been extensively applied to practical problems. However, WOA still has the drawbacks of converging slowly, and jumping out from extreme points especially for large scale optimization problems. To overcome these defects, a modified whale optimization algorithm integrated with a crisscross optimization algorithm (MWOA-CS) is proposed. In MWOA-CS, each dimension of the optimization problem updates its position by randomly performing improved WOA or crisscross optimization algorithm during the entire iterative process. The improved WOA adopts the new nonlinear convergence factor and nonlinear inertia weight to tune the ability of exploitation and exploration. To analyze the performance of MWOA-CS, a series of numerical experiments were performed on 30 test benchmark functions with dimension ranging from 300 to 1000. The experimental results revealed that the presented MWOA-CS provided better convergence speed and accuracy, and meanwhile, displayed a significantly more effective and robust performance than the original WOA and other state of the art meta-heuristic algorithms for solving large scale global optimization problems. |
| first_indexed | 2024-03-10T04:13:49Z |
| format | Article |
| id | doaj.art-27dd63936fa543c6be75618798e5b77e |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T04:13:49Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-27dd63936fa543c6be75618798e5b77e2023-11-23T08:04:08ZengMDPI AGElectronics2079-92922022-05-01119147510.3390/electronics11091475An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization ProblemsGuanglei Sun0Youlin Shang1Roxin Zhang2College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaCollege of Information Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaDepartment of Mathematics and Computer Science, Northern Michigan University, Marquette, MI 49855, USAAs an efficient meta-heuristic algorithm, the whale optimization algorithm (WOA) has been extensively applied to practical problems. However, WOA still has the drawbacks of converging slowly, and jumping out from extreme points especially for large scale optimization problems. To overcome these defects, a modified whale optimization algorithm integrated with a crisscross optimization algorithm (MWOA-CS) is proposed. In MWOA-CS, each dimension of the optimization problem updates its position by randomly performing improved WOA or crisscross optimization algorithm during the entire iterative process. The improved WOA adopts the new nonlinear convergence factor and nonlinear inertia weight to tune the ability of exploitation and exploration. To analyze the performance of MWOA-CS, a series of numerical experiments were performed on 30 test benchmark functions with dimension ranging from 300 to 1000. The experimental results revealed that the presented MWOA-CS provided better convergence speed and accuracy, and meanwhile, displayed a significantly more effective and robust performance than the original WOA and other state of the art meta-heuristic algorithms for solving large scale global optimization problems.https://www.mdpi.com/2079-9292/11/9/1475large scale global optimizationwhale optimization algorithmcrisscross optimization algorithmnonlinear parameters |
| spellingShingle | Guanglei Sun Youlin Shang Roxin Zhang An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems Electronics large scale global optimization whale optimization algorithm crisscross optimization algorithm nonlinear parameters |
| title | An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems |
| title_full | An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems |
| title_fullStr | An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems |
| title_full_unstemmed | An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems |
| title_short | An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems |
| title_sort | efficient and robust improved whale optimization algorithm for large scale global optimization problems |
| topic | large scale global optimization whale optimization algorithm crisscross optimization algorithm nonlinear parameters |
| url | https://www.mdpi.com/2079-9292/11/9/1475 |
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