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Shuffled differential evolution for large scale economic dispatch

机译：改组差异化进化，实现大规模经济调度

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摘要

In this paper, a novel metaheuristic optimization methodology is proposed to solve large scale nonconvex economic dispatch problem. The proposed approach is based on a hybrid shuffled differential evolution (SDE) algorithm which combines the benefits of shuffled frog leaping algorithm and differential evolution. The proposed algorithm integrates a novel differential mutation operator specifically designed to effectively address the problem under study. In order to validate the SDE methodology, detailed simulation results obtained on three standard test systems 13,40, and 140-unit test system are presented and discussed. Transmission losses are considered along with valve point loading effects for 13 and 40-unit test systems and calculated using B-coefficient matrix. A comparative analysis with other settled nature-inspired solution algorithms demonstrates the superior performance of the proposed methodology in terms of both solution accuracy and convergence performances.
机译：本文提出了一种新颖的元启发式优化方法来解决大规模的非凸经济调度问题。所提出的方法基于混合混洗的差分进化（SDE）算法，该算法结合了混洗蛙跳算法和差分进化的优点。所提出的算法集成了一种新颖的差分变异算子，该算子专门设计用于有效解决正在研究的问题。为了验证SDE方法，提出并讨论了在三个标准测试系统13,40和140单元测试系统上获得的详细仿真结果。传动损耗与13个单元和40个单元测试系统的阀点负载效应一起考虑，并使用B系数矩阵进行计算。与其他已解决的自然启发式解决方案算法的比较分析表明，该方法在解决方案精度和收敛性能方面均具有出色的性能。

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