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Optimization of linear objective function with max-t fuzzy relation equations
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DOI：10.1016/j.asoc.2009.02.004.png)
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An optimization model with a linear objective function subject to max-t fuzzy relation equations as constraints is presented, where t is an Archimedean t-norm. Since the non-empty solution set of the fuzzy relation equations is in general a non-convex set, conventional linear programming methods are not suitable for solving such problems. The concept of covering problem is applied to establish 0-1 integer programming problem equivalent to linear programming problem and a binary coded genetic algorithm is proposed to obtain the optimal solution. An example is given for illustration of the method. (C) 2009 Elsevier B.V. All rights reserved.
Keyword:
Genetic algorithms
Fuzzy relation equations
t-norms
Linear optimization
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6.6
论文数:
1.4W
被引数:
4.8W
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