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A new redefined model of Firefly Algorithm with application to strategic bidding problem in Power Sector
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2020-02-06 , DOI: 10.1002/2050-7038.12279
Pooja Jain 1 , Akash Saxena 1
Affiliation  

Real‐life optimization problems require an effective mechanism that completely utilizes the search space to obtain optimal solutions. A designer has always an opportunity to propose a new effective solution technique to address this issue. In this order, this paper presents a new upgraded redefined model of nature‐inspired state of art meta‐heuristic firefly algorithm (FA). FA is centered on swarm intelligence, which is motivated by the flashing pattern and behavior of fireflies. However, for a few instances, FA has a tendency to trap in local optima and it exhibits slow convergence. The proposed model is enabled through Time‐varying Inertia Weight (TIW), Opposition Based Learning (OBL) and hybridized with sine cosine operators to get updated positions of search agents. A set of twenty‐two classical benchmark function problems with numerous range and features are employed to prove the efficacy of the proposed model. Also, the effects of the above‐mentioned modifications during the whole optimization routine are analyzed through different statistical and numerical analyses. The result analysis proves that the proposed modifications make FA more compatible. The proposed model is also tested on the strategic bidding problem of the power market of two different test systems with single and multi‐trading hours. All the reported results confirm the supremacy of the proposed redefined model of FA.

中文翻译:

一种新的Firefly算法重新定义模型及其在电力行业战略招标中的应用

现实生活中的优化问题需要一种有效的机制,该机制必须完全利用搜索空间来获得最佳解决方案。设计师总是有机会提出一种新的有效解决方案技术来解决这个问题。按此顺序,本文提出了一个新的升级的重新定义模型,该模型具有自然启发性的最新元启发式萤火虫算法(FA)。FA集中在群智能上,这是由萤火虫的闪烁模式和行为所激发的。但是,在少数情况下,FA倾向于陷入局部最优状态,并且收敛速度较慢。通过随时间变化的惯性权重(TIW),基于对立的学习(OBL)启用了建议的模型,并与正弦余弦运算符混合以获取搜索代理的最新位置。一组22个具有众多范围和特征的经典基准函数问题被用来证明所提出模型的有效性。同样,通过不同的统计和数值分析来分析上述修改在整个优化例程中的效果。结果分析证明,所提出的修改使FA更加兼容。所提议的模型还针对具有两个小时的单笔交易时间和多笔交易时间的两种不同测试系统的电力市场战略投标问题进行了测试。所有报道的结果都证实了所提出的FA重新定义模型的优越性。通过不同的统计和数值分析,分析了上述修改在整个优化程序中的作用。结果分析证明,所提出的修改使FA更加兼容。所提议的模型还针对具有两个小时的单笔交易时间和多笔交易时间的两种不同测试系统的电力市场战略投标问题进行了测试。所有报道的结果都证实了所提出的FA重新定义模型的优越性。通过不同的统计和数值分析,分析了上述修改在整个优化程序中的作用。结果分析证明,所提出的修改使FA更加兼容。所提出的模型还针对具有两个小时的单笔交易时间和多笔交易时间的电力市场的战略性竞标问题进行了测试。所有报道的结果都证实了所提出的FA重新定义模型的优越性。
更新日期:2020-02-06
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