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Genetic algorithm based on primal and dual theory for solving multiobjective bilevel linear programming

机译:基于原始和对偶理论的遗传算法求解多目标双层线性规划

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The multiobjective bilevel linear programming (MBLP) is a hierarchical optimization problem involving two levels, and at least one level has multiple objectives. This paper mainly studies a special kind of MBLP with one objective at the lower level. With primal and dual theory, the lower level problem is transformed into a part of constraints of the upper level problem, then by handling the feasible set of the transformed problem, several equivalent problems of MBLP are obtained. Furthermore, by designing three feasible genetic operators, a new genetic algorithm for solving MBLP is presented. The simulations on several designed multiobjective bilevel linear programming problems are made, and the performance of the proposed algorithm is verified by comparing with the existing algorithms. The results show that the proposed algorithm is effective for MBLP.
机译:多目标双层线性规划(MBLP)是涉及两个级别的层次优化问题,并且至少一个级别具有多个目标。本文主要研究一种特殊的MBLP,其目标较低。利用原始理论和对偶理论,将下层问题转化为上层问题的约束条件的一部分,然后通过处理该转化问题的可行集,获得了几个等价的MBLP问题。此外,通过设计三个可行的遗传算子,提出了一种新的求解MBLP的遗传算法。对几种设计的多目标双层线性规划问题进行了仿真,并与现有算法进行了比较,验证了所提算法的性能。结果表明,该算法对MBLP算法是有效的。

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