Precision constrained optimization by exponential ranking

Küçük Resim Yok

Tarih

2016

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Institute of Electrical and Electronics Engineers Inc.

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Araştırma projeleri

Organizasyon Birimleri

Dergi sayısı

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

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.