Yang, S. Y.Ho, S. L.Ni, G. Z.Machado, Jose MarcioWong, K. F.2014-05-202014-05-202007-04-01IEEE Transactions on Magnetics. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc., v. 43, n. 4, p. 1601-1604, 2007.0018-9464http://hdl.handle.net/11449/38540To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm.1601-1604enggenetic algorithm (GA)global optimizationinverse problempopulation based incremental learning (PBIL) methodArtigo10.1109/TMAG.2006.892112WOS:000245327200114Acesso restrito