Fahad Parvez Mahdi, P. Vasant, M. Rahman, M. Abdullah-Al-Wadud, J. Watada, V. Kallimani
{"title":"Quantum particle swarm optimization for multiobjective combined economic emission dispatch problem using cubic criterion function","authors":"Fahad Parvez Mahdi, P. Vasant, M. Rahman, M. Abdullah-Al-Wadud, J. Watada, V. Kallimani","doi":"10.1109/ICIVPR.2017.7890879","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVPR.2017.7890879","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
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.