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首页> 外文期刊>Journal of computational and theoretical nanoscience >Dynamic Population Artificial Bee Colony Algorithm for Multi-Objective Optimal Power Flow
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Dynamic Population Artificial Bee Colony Algorithm for Multi-Objective Optimal Power Flow

机译:多目标最佳功率流动动态群体人造群落算法

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This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee canreproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEEtest system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposedmethod.
机译:本文提出了一种具有动态群体(ABC-DP)的新型人工蜂殖民地算法,这协调了延长生命周期不断发展模型的思想,以平衡勘探和开发权衡。 所提出的ABC-DP是一种更具蜜蜂殖民地 - 现实模型,即在整个觅食过程和群体大小中动态地蜂犬制剂和死亡随着算法的运行而变化。 然后,ABC-DP用于求解考虑成本,损失和排放影响的功率系统中的最佳功率流量(OPF)问题作为目标函数。 提出了30总线IEEETEST系统以说明所提出的算法的应用。 展示的模拟结果与NondoMinated分类遗传算法II(NSGAII)和多目标ABC(MOABC)进行了比较,以说明拟议方法的有效性和鲁棒性。

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