Short-term scheduling of thermal power systems using hybrid gradient based modified teaching–learning optimizer with black hole algorithm-Reference-Cited by-同舟云学术

Short-term scheduling of thermal power systems using hybrid gradient based modified teaching–learning optimizer with black hole algorithm

Published:2014-03 Issue: Volume:108 Page:16-34
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Azizipanah-Abarghooee Rasoul,Niknam Taher,Bavafa Farhad,Zare Mohsen

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference46 articles.

1. Short-term scheduling of thermal-electric generators using Lagrangian relaxation;Bard;Oper. Res.,1988

2. Thermal unit commitment using binary/real coded artificial bee colony algorithm;Chandrasekaran;Electr. Power Syst. Res.,2012

3. A novel short-term generation scheduling technique of thermal units using ant colony search algorithms;Yu;Int. J. Electric. Power Energy Syst.,2001

4. Dynamic economic dispatch considering transmission losses using quadratically constrained quadratic program method;Zhong;IEEE Trans. Power Syst.,2013

5. Differential evolution for economic load dispatch problems;Noman;Electr. Power Syst. Res.,2008

Cited by 48 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comprehensive survey of artificial intelligence-based techniques for performance enhancement of solid oxide fuel cells: Test cases with debates;Artificial Intelligence Review;2024-02-03

2. Teaching learning based optimization-a review on background and development;AIP Conference Proceedings;2024

3. Black hole algorithm as a heuristic approach for rare event classification problem;Pakistan Journal of Statistics and Operation Research;2023-12-06

4. A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization;CAAI Artificial Intelligence Research;2023-12

5. Multi-population Black Hole Algorithm for the problem of data clustering;PLOS ONE;2023-07-05