New multiobjective fuzzy optimization method and its application

Abstract

This paper proposes a new multiobjective fuzzy optimization method. First, the unsatisfying function, which is more useful and effective as the expression of fuzziness for optimization problems than the membership function, is introduced. The multiobjective optimization problem is transformed into a satisficing problem by using aspiration levels, and the fuzzy satisficing problem is formulated. Then, the interactive design method to minimize the maximum unsatisfaction rating by Genetic Algorithm is proposed. The effectiveness of the proposed method is demonstrated by the design example of an active suspension system. The trade-off graph is used in order to seek a satisficing solution, which reflects the designer's preference, more interactively and graphically.

Parameter Adjustment of Control Systems by Multiobjective Fuzzy Satisficing Approach

“…In conventional fuzzy optimization methods, fuzziness is expressed by the membership function (Sakawa, 1993;Trebi-Ollennu and White, 1997). On the contrary, in this paper, fuzziness is defined by the unsatisfying function, which has a one-to-one correspondence with the membership function (Kiyota et al, 2000;Tsuji et al, 2000).…”

“…for H ∞ control systems (Kristinson and Dumont, 1992;Tan and Li, 1997;Chen and Cheng, 1998;Man et al, 1999;Griffin et al, 2000) and a nonlinear system (Trebi-Ollennu and White, 1997). Kiyota et al (2000) and have proposed to transform the formulated multiobjective problem as a fuzzy satisficing problem and solve it by a GA.…”

Taximeter verification using imprecise data from GPS

“…In Youssef and Khan (2000) the use of fuzzy rules for determining the dominance of one individual with regard to another is proposed. Similar works are documented in Trebi-Ollennu and White (1997) and Kiyota et al (2000). In this work we have decided to use our own implementation of the NSGA-2 algorithm for fuzzy data, which is described in Sá nchez et al (2007) and Sá nchez et al (submitted).…”

Taximeter verification with GPS and soft computing techniques

“…In the use of fuzzy rules (Youssef et al 2000) for determining the dominance of one individual with regard to another is proposed. Similar works are documented in (Trebi-Ollennu and White 1997; Kiyota et al 2000). In this work we have decided to use our own implementation of the NSGA-2 algorithm for fuzzy data, which is described in , Sánchez and Couso et al (2009).…”