An interactive fuzzy satisficing method for multiobjective nonconvex programming problems through floating-point genetic algorithms

✍ Scribed by Masatoshi Sakawa; Katsuhiro Yauchi

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 153 KB
• Volume: 83
• Category: Article
• ISSN: 1042-0967
• DOI: 10.1002/(sici)1520-6440(200006)83:6<10::aid-ecjc2>3.0.co;2-k

✦ Synopsis

This article focuses on the multiobjective nonconvex nonlinear programming problem. The following interactive fuzzy satisficing method is proposed using the floatingpoint genetic algorithm. The fuzzy goal of the decisionmaker for each objective function is specified by the membership function. The Pareto optimal solution is derived, which is close to the reference membership value set by the decision-maker, in the sense of the augmented min max criterion. If the decision-maker is not satisfied with the solution, the reference membership value is interactively updated to derive the satisficing solution for the decisionmaker from the set of Pareto optimal solutions. In the derivation of the Pareto optimal solution for the augmented minmax problem, GENOCOP III proposed by Michalewicz and colleagues is not used. Instead, a more efficient method is proposed, where the improved GENOCOP III is applied to cope with the problems in GENOCOP III, by introducing the efficient search of the initial feasible solution, and the search of the feasible solution by bisection method. The validity of the proposed method is shown through numerical examples.