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Fuzzy optimization with multi-objective evolutionary algorithms: A case study

P., Vasant and F., Jimenez and G., Sanchez (2007) Fuzzy optimization with multi-objective evolutionary algorithms: A case study. In: 1st IEEE Symposium of Computational Intelligence in Multicriteria Decision Making, MCDM 2007, 1 April 2007 through 5 April 2007, Honolulu, HI.

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Abstract

This paper outlines a real-world industrial problem for product-mix selection involving 8 decision variables and 21 constraints with fuzzy coefficients. On one hand, a multi-objective optimization approach to solve the fuzzy problem is proposed. Modified S-curve membership functions are considered. On the other hand, an ad hoc Pareto-based multi-objective evolutionary algorithm to capture multiple non dominated solutions in a single run of the algorithm is described. Solutions in the Pareto front corresponds with the fuzzy solution of the former fuzzy problem expressed in terms of the group of three (x→, μ, α), i.e., optimal solution - level of satisfaction - vagueness factor. Decision-maker could choose, in a posteriori decision environment, the most convenient optimal solution according to his level of satisfaction and vagueness factor. The proposed algorithm has been evaluated with the existing methodologies in the field and the results have been compared with the well-known multi-objective evolutionary algorithm NSGA-II. © 2007 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Decision making; Decision theory; Evolutionary algorithms; Multiobjective optimization; Pareto principle; Decision variables; NSGA-II; Vagueness factor; Fuzzy systems
Subjects:Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:137
Deposited By: Mr Helmi Iskandar Suito
Deposited On:02 Mar 2010 01:19
Last Modified:19 Jan 2017 08:27

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