Precision Constrained Optimization by Exponential Ranking

Precision Constrained Optimization by Exponential Ranking

Bittermann, M.S. (Maltepe University)
Ciftcioglu, O. (TU Delft Design Informatics)

2016

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.

evolutionary algorithm
multiobjective optimization
constrained optimization
probabilistic modeling

http://resolver.tudelft.nl/uuid:5120d4bc-e650-4dc2-9796-aa62730602a9

https://doi.org/10.1109/CEC.2016.7744072

IEEE

978-1-5090-0622-9

Proceedings 2016 IEEE Congress on Evolutionary Computation (CEC)

2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016-07-24 → 2016-07-29, Vancouver, Canada

Accepted Author Manuscript

Institutional Repository

conference paper

© 2016 M.S. Bittermann, O. Ciftcioglu