基于混沌的捕食者-猎物头脑风暴优化算法降低功率损失

K. Lenin
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引用次数: 2

摘要

本文提出了一种求解最优无功问题的混沌捕食者-猎物脑风暴优化算法。在本工作中,捕食者-猎物脑风暴优化将集群中心定位为捕食者,从而使集群中心向越来越好的位置移动,而其余的思想则作为猎物;因此要远离附近的捕食者。在投影CPB算法中,混沌理论被应用于算法的建模。在提出的算法中,利用混沌的遍历性和不规则性等主要特性使算法跳出局部最优并确定最优参数,CPB算法在标准IEEE 57总线测试系统上进行了测试,仿真结果表明,投影算法显著降低了实际功率损耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power loss reduction by chaotic based predator-prey brain storm optimization algorithm
In this paper chaotic predator-prey brain storm optimization (CPB) algorithm is proposed to solve optimal reactive power problem. In this work predator-prey brain storm optimization position cluster centers to perform as predators, consequently it will move towards better and better positions, while the remaining ideas perform as preys; hence get away from their adjacent predators. In the projected CPB algorithm chaotic theory has been applied in the modeling of the algorithm. In the proposed algorithm main properties of chaotic such as ergodicity and irregularity used to make the algorithm to jump out of the local optimum as well as to determine optimal parameters CPB algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.
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