An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation
To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussi...
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| Format: | Article |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8790677/ |
| _version_ | 1818603416052563968 |
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| author | Yintong Li Tong Han Hui Zhao Hanjie Gao |
| author_facet | Yintong Li Tong Han Hui Zhao Hanjie Gao |
| author_sort | Yintong Li |
| collection | DOAJ |
| description | To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction. Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter. When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum. Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions. The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO. The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy. Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs). To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective. The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results. |
| first_indexed | 2024-12-16T13:22:49Z |
| format | Article |
| id | doaj.art-a4433fc5400e4414a092604cf4a14ccb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:22:49Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a4433fc5400e4414a092604cf4a14ccb2022-12-21T22:30:19ZengIEEEIEEE Access2169-35362019-01-01711013811015810.1109/ACCESS.2019.29336618790677An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task AllocationYintong Li0https://orcid.org/0000-0001-5304-5147Tong Han1Hui Zhao2Hanjie Gao3College of Aeronautics Engineering, Air Force Engineering University, Xi’an, ChinaCollege of Aeronautics Engineering, Air Force Engineering University, Xi’an, ChinaCollege of Aeronautics Engineering, Air Force Engineering University, Xi’an, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaTo overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction. Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter. When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum. Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions. The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO. The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy. Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs). To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective. The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results.https://ieeexplore.ieee.org/document/8790677/Whale optimization algorithmCEC 2014numerical optimizationUCAVtask allocation |
| spellingShingle | Yintong Li Tong Han Hui Zhao Hanjie Gao An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation IEEE Access Whale optimization algorithm CEC 2014 numerical optimization UCAV task allocation |
| title | An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation |
| title_full | An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation |
| title_fullStr | An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation |
| title_full_unstemmed | An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation |
| title_short | An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation |
| title_sort | adaptive whale optimization algorithm using gaussian distribution strategies and its application in heterogeneous ucavs task allocation |
| topic | Whale optimization algorithm CEC 2014 numerical optimization UCAV task allocation |
| url | https://ieeexplore.ieee.org/document/8790677/ |
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