Transmission network expansion planning using state-of-art nature inspired algorithms: a survey

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Swarm Intelligence Research Pub Date : 2019-01-23 DOI:10.1504/IJSI.2019.10018603
A. Khandelwal, A. Bhargava, Ajay Sharma, Harish Sharma
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引用次数: 4

Abstract

Transmission network expansion planning (TNEP) problem has been continuously solved for many years still the cost effective, reliable, and optimise solution is always desirable. The TNEP has been solved by various conventional and non conventional strategies. The strategy to find the solution of TNEP by classical mathematical optimisation techniques is tedious, slow and inefficient. In recent years, nature inspired algorithms (NIAs) have proven their importance to provide the solutions of the TNEP problem over classical mathematical optimisation techniques. This paper presents a review on the key contributions of the state-of-art NIAs to solve the TNEP problem. Further, the TNEP system specific significant works presented in the literature are summarised for easy understanding of the readers. The readers can get a brief description of the considered NIAs algorithms which has been applied to solve various systems of TNEP problem and they can also identify the significant NIA which is being applied for specific TNEP system.
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传输网扩展规划使用最先进的自然启发算法:调查
输电网扩容规划(TNEP)问题多年来一直在不断解决,但经济、可靠、优化的解决方案始终是人们所需要的。通过各种常规和非常规策略来解决TNEP问题。用经典的数学优化方法求解TNEP问题是一种繁琐、缓慢和低效的方法。近年来,自然启发算法(NIAs)已经证明了它们在提供TNEP问题解决方案方面的重要性,而不是经典的数学优化技术。本文综述了目前最先进的nia在解决TNEP问题方面的主要贡献。此外,为了便于读者理解,总结了文献中提出的TNEP系统特定重要作品。读者可以得到被考虑的NIA算法的简要描述,这些算法已经应用于解决各种系统的TNEP问题,他们也可以识别用于特定TNEP系统的重要NIA。
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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