Precision constrained optimization by exponential ranking
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
Demonstrative results of a probabilistic constraint handling approach that is exclusively using evolutionary computation are presented. In contrast to other works involving the same probabilistic considerations, in this study local search has been omitted, in order to assess the necessity of this deterministic local search procedure in connection with the evolutionary one. The precision stems from the non-linear probabilistic distance measure that maintains stable evolutionary selection pressure towards the feasible region throughout the search, up to micro level in the range of 10 -10 or beyond. The details of the theory are revealed in another paper [1]. In this paper the implementation results are presented, where the non-linear distance measure is used in the ranking of the solutions for effective tournament selection. The test problems used are selected from the existing literature. The evolutionary implementation without local search turns out to be already competitively accurate with sophisticated and accurate state-of-the-art constrained optimization algorithms. This indicates the potential for enhancement of the sophisticated algorithms, as to their precision and accuracy, by the integration of the proposed approach. © 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
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Bittermann, M. S. ve Çiftçioğlu, Ö. (2016). Precision constrained optimization by exponential ranking. 2016 IEEE Congress on Evolutionary Computation, CEC 2016. s. 2296-2305.
