Unsatisfying functions and multiobjective fuzzy satisfaction design using genetic algorithms

Authors:
T. Kiyota;Y. Tsuji;E. Kondo
Affiliations:
Dept. of Mech. Syst. & Environ. Eng., Univ. of Kitakyushu, Fukuoka, Japan;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2003

Citing 0
Cited 2

A novel approach to developing and evaluating regulation strategies for urban transport disrupted networks

International Journal of Computer Integrated Manufacturing - Industrial Engineering and Systems Management
Application of weighted ideal point method to environmental/economic load dispatch

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes a new fuzzy satisfaction method using genetic algorithms (GA) for multiobjective problems. First, an unsatisfying function, which has a one-to-one correspondence with the membership function, is introduced for expressing "fuzziness". Next, the multiobjective design problem is transformed into a satisfaction problem of constraints by introducing an aspiration level for each objective. Here, in order to handle the fuzziness involved in aspiration levels and constraints, the unsatisfying function is used, and the problem is formulated as a multiobjective minimization problem of unsatisfaction ratings. Then, a GA is employed to solve the problem, and a new strategy is proposed to obtain a group of Pareto-optimal solutions in which the decision maker (DM) is interested. The DM can then seek a satisfaction solution by modifying parameters interactively according to preferences.