Unsatisfying functions and multiobjective fuzzy satisficing design using genetic algorithms

This paper describes a new fuzzy satisficing method using genetic algorithms (GAs) 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 satisficing 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 satisficing solution by modifying parameters interactively according to preferences.