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- W2023996475 abstract "An Autonomous Bee Colony Optimization (A-BCO) algorithm for solving multi-objective numerical problems is proposed. In contrast with previous Bee Colony algorithms, A-BCO utilizes a diversity-based performance metric to dynamically assess the archive set. This assessment is employed to adapt the bee colony structures and flying patterns. This self-adaptation feature is introduced to optimize the balance between exploration and exploitation during the search process. Moreover, the total number of search iterations is also determined/optimized by A-BCO, according to user pre-specified conditions, during the search process. We evaluate A-BCO upon numerical benchmark problems and the experimental results demonstrate the effectiveness and robustness of the proposed algorithm when compared with the Non-dominated Sorting Genetic Algorithm II and the latest Multi-objective Bee Colony Algorithm proposed to date." @default.
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- W2023996475 date "2010-07-01" @default.
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- W2023996475 title "Autonomous Bee Colony Optimization for multi-objective function" @default.
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- W2023996475 doi "https://doi.org/10.1109/cec.2010.5586057" @default.
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