Quantum particle swarm optimization for multiobjective combined economic emission dispatch problem using cubic criterion function
In this research, quantum particle swarm optimization (QPSO) is utilized to solve multiobjective combined economic emission dispatch (CEED) problem formulated using cubic criterion function considering a uni wise max/max price penalty factor. QPSO is implemented on a 6-unit power generation system a...
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| Main Authors: | , , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers Inc.
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018175475&doi=10.1109%2fICIVPR.2017.7890879&partnerID=40&md5=c5160121a35b2daaaf3ebd397bb4fd09 http://eprints.utp.edu.my/20112/ |
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| Summary: | In this research, quantum particle swarm optimization (QPSO) is utilized to solve multiobjective combined economic emission dispatch (CEED) problem formulated using cubic criterion function considering a uni wise max/max price penalty factor. QPSO is implemented on a 6-unit power generation system and compared with Lagrangian relaxation, particle swarm optimization (PSO) and simulated annealing (SA). The obtained results verified the effectiveness and demonstrate the robustness of QPSO method. This research suggests that QPSO can be used as an effective and robust tool in other power dispatch problems. © 2017 IEEE. |
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