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Application of Bacterial Foraging and Firefly Optimization Algorithm to Economic Load Dispatch including valve point loading

机译:细菌觅食和萤火虫优化算法在包括阀点负荷在内的经济负荷分配中的应用

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In the current scenario, where the industries are plagued with global energy crisis and skyrocketing fuel prices, the need of the hour is efficient utilization of the available resources without compromising the demand. Several traditional approaches like lambda-iteration and gradient method were utilized in solving non-linear problems. Recently, soft-computing techniques have received attention and have already been applied successfully for many practical applications. Evolutionary programming is an efficient optimization tool for solving non-linear programming problems. In this paper, nature inspired algorithms such as Bacterial Foraging Optimization Algorithm (BFOA) and Firefly Algorithms (FA) were implemented to Economic Load Dispatch (ELD) problem for three generator system and thirteen generator systems with both inclusion and omission of valve point loading. The results obtained are by applying both the algorithms separately to the ELD problem. By comparing other optimization algorithm techniques, we can observe the dominance of the proposed algorithms and confirm their potentiality in solving non-linear ELD issues.
机译:在当前的情况下,当行业遭受全球能源危机和燃料价格飞涨的困扰时,小时的需求是在不损害需求的情况下有效利用可用资源。解决lambda迭代和梯度法的几种传统方法被用来解决非线性问题。近来,软计算技术已经受到关注,并且已经成功地应用于许多实际应用中。进化规划是解决非线性规划问题的有效优化工具。在本文中,针对三台发电机系统和十三台发电机系统的经济负荷分配(ELD)问题,采用了细菌觅食优化算法(BFOA)和萤火虫算法(FA)等自然启发性算法,并同时包含和省略了阀点载荷。通过将两种算法分别应用于ELD问题而获得的结果。通过比较其他优化算法技术,我们可以观察到所提出算法的优势,并确认了它们在解决非线性ELD问题中的潜力。

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