Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICSET.2010.5684426
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dc.titleImproved harmony search algorithm based optimal design of the brushless DC wheel motor
dc.contributor.authorYadav, P.
dc.contributor.authorKumar, R.
dc.contributor.authorPanda, S.K.
dc.contributor.authorChang, C.S.
dc.date.accessioned2014-06-19T03:13:28Z
dc.date.available2014-06-19T03:13:28Z
dc.date.issued2010
dc.identifier.citationYadav, P.,Kumar, R.,Panda, S.K.,Chang, C.S. (2010). Improved harmony search algorithm based optimal design of the brushless DC wheel motor. 2010 IEEE International Conference on Sustainable Energy Technologies, ICSET 2010 : -. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICSET.2010.5684426" target="_blank">https://doi.org/10.1109/ICSET.2010.5684426</a>
dc.identifier.isbn9781424471935
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/70553
dc.description.abstractThe paper presents an optimal design method to optimize the efficiency of the brushless DC wheel motor. The optimal design of a brushless DC wheel motor is a nonlinear multimodal benchmark optimization problem. Hence, conventional methods fail to provide optimal solution. Recently developed, Harmony Search (HS) algorithm has been used for maximizing the efficiency of the brushless DC wheel motor by optimally designing the design parameters. HS algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continues to polish the pitches in order to obtain better harmony. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The numerical results obtained show that the efficiency of the brushless DC wheel motor is more for IHS algorithm when compared to HS algorithm and GA. However, the motor based on the design parameters obtained using IHS is subjected to lesser temperature rise for the same efficiency as compared to that of Ant Colony Optimization (ACO) and also IHS algorithm converges faster than other meta-heuristic approaches adopted in this paper. ©2010 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICSET.2010.5684426
dc.sourceScopus
dc.subjectAnt Colony Optimization (ACO)
dc.subjectBrushless DC motor
dc.subjectHarmony search (HS)
dc.subjectMeta-heuristic
dc.subjectOptimization
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ICSET.2010.5684426
dc.description.sourcetitle2010 IEEE International Conference on Sustainable Energy Technologies, ICSET 2010
dc.description.page-
dc.identifier.isiutNOT_IN_WOS
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