Further note on the probabilistic constraint handling
Küçük Resim Yok
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
A robust probabilistic constraint handling approach in the framework of joint evolutionary-classical optimization has been presented earlier. In this work, the theoretical foundations of the method are presented in detail. The method is known as bi-objective method, where the conventional penalty function approach is implemented. The present work highlights the dynamic variation of the commensurate penalty parameter for each objective treated as constraint. It is shown that the constraint parameters collectively define the right slope of the tangent as to the optimal front during the search. The robust and sustained convergence throughout the search up to micro level in the range of 10 -10 or beyond is explained. The work here is presented as a further note in connection with the previous publication, where the subtle theoretical considerations and their details had been omitted for the sake of detailed results of the experiments demonstrating the effective working of the approach. In contrast to the implementation-centered reporting of the previous work, this work can be considered as a description of the detailed probabilistic basis underlying the previous work. Therefore, this study is of great importance to let the researchers conveniently gain the insight into the work and its implications reported earlier. © 2016 IEEE.
Açıklama
IEEE Computational Intelligence Society (CIS)
2016 IEEE Congress on Evolutionary Computation, CEC 2016 -- 24 July 2016 through 29 July 2016 -- -- 124911
2016 IEEE Congress on Evolutionary Computation, CEC 2016 -- 24 July 2016 through 29 July 2016 -- -- 124911
Anahtar Kelimeler
Constrained optimization, Evolutionary algorithm, Multiobjective optimization, Probabilistic modeling
Kaynak
2016 IEEE Congress on Evolutionary Computation, CEC 2016
