2019
DOI: 10.1109/access.2019.2933661
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An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

Abstract: 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 Gaus… Show more

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Cited by 23 publications
(14 citation statements)
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“…The estimation of distribution algorithm (EDA) can estimate the evolution direction of the promising population using probabilistic model learning and sampling. Some studies have shown that EDA has promising performance when dealing with complex optimization problems [39] [40]. GEDM is the core component of EDA.…”
Section: ) Gaussian Estimation Of Distribution Methodsmentioning
confidence: 99%
“…The estimation of distribution algorithm (EDA) can estimate the evolution direction of the promising population using probabilistic model learning and sampling. Some studies have shown that EDA has promising performance when dealing with complex optimization problems [39] [40]. GEDM is the core component of EDA.…”
Section: ) Gaussian Estimation Of Distribution Methodsmentioning
confidence: 99%
“…The metaheuristic algorithm has the advantages of not relying on the problem model, not requiring gradient information, having strong search capability and wide applicability, and can achieve a good balance between solution quality and computational cost [ 3 ]. Therefore, the metaheuristic algorithms have been proposed to solve real-world optimization problems, such as image segmentation [ 4 , 5 ], feature selection [ 6 , 7 ], mission planning [ 8 , 9 ], parameter optimization [ 10 , 11 ], job shop scheduling [ 12 , 13 ], etc.…”
Section: Introductionmentioning
confidence: 99%
“…us they are widely used for solving optimization problems, such as mission planning [4][5][6][7], image segmentation [8][9][10], feature selection [11][12][13], and parameter optimization [14][15][16][17][18]. Metaheuristic algorithms find optimal solutions by modeling physical phenomena or biological activities in nature.…”
Section: Introductionmentioning
confidence: 99%