交互满意方法在多目标模糊非线性规划中应用

An Interactive Satisfying Method Used in Multi-objective Fuzzy Nonlinear Programming

在运用浮点遗传算法的多目标全系数模糊非线性规划中,提出交互模糊满意方法.引入α-水平集把MOFNLP转化为α-MONLP.通过线性隶属函数量化.给出具体的α水平度和参考隶属度,利用浮点遗传算法解最小值问题得到Pareto最优解,并通过调整α水平度和参考隶属度使决策者从多个Pareto最优解中得到满意解.

we focus on multi-objective full coefficient fuzzy nonlinear programming problem and an interactive satifying method is presented by using floating point genetic algorithms. With α-level sets of fuzzy numbers introduced, the corresponding non-fuzzy αprogramming problem is introduced. The fuzzy goals of decision marker for a objective function are quantified by eliciting the corresponding linear membership function . If the DM specifies the degree αand reference membership values, the Correspondingly extended Pareto optimal solution can be obtained by solving the minimum problem through genetic algorithms with float points.