In this paper, a new interval multi-objective optimization (MOO) method integrating with the multidimensional parallelepiped (MP) interval model has been proposed to handle the uncertain problems with dependent interval variables. The MP interval model is integrated to depict the uncertain domain of the problem, where the uncertainties are described by marginal intervals and the degree of the dependencies among the interval variables is described by correlation coefficients. Then an efficient multi-objective iterative algorithm combining the micro multi-objective genetic algorithm (MOGA) with an approximate optimization method is formulated. Three numerical examples are presented to demonstrate the efficiency of the proposed approach.

中文翻译：

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区间因变量不确定问题的区间多目标优化新方法

本文提出了一种与多维平行六面体（MP）区间模型相结合的区间多目标优化（MOO）方法来处理区间因变量的不确定性问题。MP区间模型被集成来描述问题的不确定域，其中不确定性由边际区间描述，区间变量之间的依赖程度由相关系数描述。然后提出了一种将微多目标遗传算法（MOGA）与近似优化方法相结合的高效多目标迭代算法。给出了三个数值例子来证明所提出方法的效率。