Computer Science ›› 2016, Vol. 43 ›› Issue (9): 250-254.doi: 10.11896/j.issn.1002-137X.2016.09.050

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Improved Intuitionistic Fuzzy Genetic Algorithm for Nonlinear Programming Problems

MEI Hai-tao, HUA Ji-xue and WANG Yi   

  • Online:2018-12-01 Published:2018-12-01

Abstract: To solve the multidimensional nonlinear programming problem,an improved intuitionistic fuzzy genetic algorithm(IFGA) was proposed.The membership and nonmembership degrees of individuals are defined by the individual fitness of the genetic algorithm in each iteratine optimization,and the problem is transformed to the intuitionistic fuzzy nonlinear programming problem to adjust crossover and mutation rates.And the paper proposed an improved selection operator.The individuals are divided into four same size groups,and the group with poor fitness is selected and copied randomly to increase the diversity and competitiveness,because the implicit optimization information of non-feasible solution is reserved.The simulation results indicate the IFGA is feasible and effective.

Key words: Nonlinear programming,Genetic algorithm,Constraint function,Intuitionistic fuzzy set,Optimal solution

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