Solution of Non-Smooth Economic Dispatch Using Interactive Grouped Adaptive Bat Algorithm: Solving Practical Economic Dispatch

B. Mahdad
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引用次数: 4

Abstract

This article presents the application of new grouped adaptive Bat algorithm (GABA) based metaheuristic method to improve the solution of economic dispatch (ED) problem considering valve point effect, prohibited zones, ramp rate limits and total power loss. The Bat algorithm is a new swarm intelligence algorithm inspired by the echolocation phenomenon in bats. The Bat algorithm is easy to program, and like many metaheuristic methods has an exploration and exploitation phases which require fine adjustment to achieve the near global solution. A grouped search mechanism is introduced to enhance the performances of the original Bat algorithm. The robustness of the proposed algorithm in term of solution quality and convergence characteristic have been demonstrated of three test systems of various complexities 6 units considering simultaneously the prohibited zones, ramp rate limits and total power loss, 13 and 40 units considering valve point effect. Results show clearly the efficiency and superiority of the proposed algorithm compared with various techniques reported in the recent literature.
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用交互式分组自适应Bat算法求解非光滑经济调度问题:求解实际经济调度问题
本文提出了一种新的基于分组自适应蝙蝠算法(GABA)的元启发式方法,在考虑阀点效应、禁区、匝道速率限制和总功率损耗的情况下,对经济调度问题的求解进行了改进。蝙蝠算法是受蝙蝠回声定位现象启发而提出的一种新的群体智能算法。Bat算法易于编程,并且像许多元启发式方法一样,有一个探索和开发阶段,需要进行微调以实现近全局解。为了提高原算法的性能,引入了分组搜索机制。通过3个不同复杂度的测试系统(6个单元同时考虑禁区、斜坡速率限制和总功率损耗)和考虑阀点效应的13个和40个单元)验证了该算法在解质量和收敛特性方面的鲁棒性。结果表明,与近年来文献报道的各种技术相比,该算法的效率和优越性明显。
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