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This paper presents the stages for solving fuzzy multi-objective optimization problems using genetic algorithm approach. Before applying non-dominated sorting genetic algorithm II (NSGA II) techniques to obtain optimal solution, first multi-objective possibilistic (fuzzy) programming was converted into an equivalent auxiliary crisp model to form deterministic programming model. To determine the best...
During deterministic optimization, only the decision variables are varied whereas all other parameters appearing in the given optimization framework are kept constant. However, some of these parameters are subjected to real life uncertainty and assuming them as constants during the course of optimization leads to suboptimal or infeasible solutions. This paper presents intuitionistic fuzzy expected...
Multiobjective site selection is a class complicated spatial analysis problem which can hardly be solved with traditional methods of Geographical Information System (GIS). In this paper we described an approach based on the gene expression programming (GEP) algorithm, with which the multiobjective site-search problems can be resolved. The validity of this method is verified by using MOP2 function,...
Genetic algorithms, owning to their characteristics of intrinsic parallel mechanism and full optimization, can be used to search for solutions for objective programming. In this study, empirical study method was combined with the multi-objective optimization to develop a multi-objective forest harvest adjustment model by introducing Pareto multi-objective genetic algorithm into optimization model...
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