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首页> 外文期刊>計測自動制御学会論文集 >Multi-objective optimization for mixed-integer programming problems through extending hybrid genetic algorithm with niche method
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Multi-objective optimization for mixed-integer programming problems through extending hybrid genetic algorithm with niche method

机译：扩展混合遗传算法与小生境方法的混合整数规划问题多目标优化

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摘要

With a point of view that complete mathematical models is seldom available in practical optimization, we aim at developing a method that can generate an appropriate number of candidate solutions than a unique rigid one. Especially, in this paper, we focussed on multi-objective mixed-integer programs which involve qualitative expression regarding both objective functions and system constraints. To cope with such an ill-posed problem, we have extended the hybrid genetic algorithm with value function modeled by neural networks (HybGA/MOR). The idea is realized by separating the qualitative factors from quantitative ones in the hierarchical framework. In addition, we incorporate into the algorithm of HybGA/MOR a niche method to derive several solutions running as candidates for final selection, and a penalty function approach to handle with the qualitative constraints. Effectiveness of the method has been verified through numerical experiments taking an example in site location of hazardous wastes disposal.
机译：考虑到实际优化中很少有完整的数学模型，我们的目标是开发一种方法，该方法可以生成比唯一的刚性模型合适数量的候选解决方案。特别是，在本文中，我们着重于涉及目标函数和系统约束的定性表达的多目标混合整数程序。为了解决这种不适的问题，我们扩展了混合遗传算法的神经网络（HybGA / MOR）建模的价值函数。这个想法是通过在层次结构框架中将定性因素与定量因素分离而实现的。此外，我们在HybGA / MOR算法中结合了一种小生境方法，以推导多个解决方案作为最终选择的候选方案，以及一种惩罚函数方法来处理定性约束。通过数值实验证明了该方法的有效性，并以危险废物处置现场的位置为例。

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