Some experiments in Tchebycheff-based approaches for interactive multiple objective decision making
Computers and Operations Research
Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments
Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy global optimization of complex system reliability
IEEE Transactions on Fuzzy Systems
The Journal of Supercomputing
The Journal of Supercomputing
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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In this paper, an interactive approach based method is proposed for solving multi-objective optimization problems. The proposed method can be used to obtain those Pareto-optimal solutions of the mathematical models of linear as well as nonlinear multi-objective optimization problems modeled in fuzzy or crisp environment which reasonably meet users aspirations. In the proposed method the objectives are treated as fuzzy goals and the satisfaction of constraints is considered at different @a-level sets of the fuzzy parameter used. Product operator is used to aggregate the membership functions of the objectives. To initiate the algorithm, the decision maker has to specify his(er) preferences for the desired values of the objectives in the form of reference levels in the membership space. In each iterative phase, a single objective nonlinear (usually nonconvex) optimization problem has to be solved. It is solved using real coded genetic algorithm, MI-LXPM. Based on its outcomes, the decision maker has the option to modify, if felt necessary, some or all of the reference levels in the membership function space before initiating the next iterative phase. The algorithm is stopped where user's aspirations are reasonably met.