Abstract
Recently, Genetic Algorithms, a new learning paradigm that models a natural evolution mechanism, have received a great deal of attention regarding their potential as optimization techniques for solving combinatorial optimization problems. In this paper, we focus on multiobjective combinatorial optimization problems as a generalization of the traditional single objective combinatorial optimization ones. By considering the imprecise nature of human judgements, we assume that the decision maker may have fuzzy goals for each of the objective functions. After eliciting the linear membership functions through the interaction with the decision maker, we adopt the minimum-operator for combining them. In order to investigate the applicability of the conventional simple Genetic Algorithms for the solution of the formulated problems, a lot of numerical simulations are performed by assuming several genetic operators. Then, instead of using the penalty function for treating the constraints, we propose three types of revised Genetic Algorithms which generate only feasible solutions. Illustrative numerical examples demonstrate the both feasibility and efficiency of the proposed methods.
