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BAS-ADAM:An ADAM Based Approach to Improve the Performance of Beetle Antennae Search Optimizer

查看全文 作　　者：Ameer Hamza [1]Khan;Xinwei [2]Cao;Shuai [3]Li;Vasilios [4]N.Katsikis;Liefa [5]Liao 高影响力作者 机构地区：[1]Department of Computing,Hong Kong Polytechnic University,Hong Kong,China;[2]School of Management,Shanghai University,Shanghai 201900,China;[3]Department of Electronics and Electrical Engineering,Swansea University,Swansea SA18EN,UK;[4]Department of Economics,Division of Mathematics and Informatics,National and Kapodistrian University of Athens,Athens 10679,Greece;[5]School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China高影响力机构 出　　处：《IEEE/CAA Journal of Automatica Sinica》索引2020年第7卷第2期,共11页高影响力期刊 摘　　要：In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm. 关 键 词：Adaptive moment estimation(ADAM) Beetle antennae search(BAM) gradient estimation metaheuristic optimization nature-inspired algorithms neural network
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