@inproceedings{scholars8766,
            note = {cited By 17; Conference of 2017 IEEE International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2017 ; Conference Date: 13 February 2017; Conference Code:127153},
           title = {Quantum particle swarm optimization for multiobjective combined economic emission dispatch problem using cubic criterion function},
             doi = {10.1109/ICIVPR.2017.7890879},
         journal = {2017 IEEE International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2017},
            year = {2017},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018175475&doi=10.1109\%2fICIVPR.2017.7890879&partnerID=40&md5=c5160121a35b2daaaf3ebd397bb4fd09},
        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. {\^A}{\copyright} 2017 IEEE.},
          author = {Mahdi, F. P. and Vasant, P. and Rahman, M. M. and Abdullah-Al-Wadud, M. and Watada, J. and Kallimani, V.},
        keywords = {Electric load dispatching; Multiobjective optimization; Pattern recognition; Simulated annealing, Combined economic emission dispatch; Combined economic emission dispatches (CEED); Cubic function; LaGrangian relaxation; Penalty factor; Power generation systems; Price penalty factor; Quantum particle swarm optimization, Particle swarm optimization (PSO)},
            isbn = {9781509060030}
}