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A proposal on a decomposition-based evolutionary multiobjective optimization for large scale vehicle routing problems

机译:大规模车辆路径问题的基于分解的进化多目标优化建议

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A proposed approach is specialized for large scale vehicle routing problems(VRPs) and based on area segmentation and gradual area integration mechanisms so as to avoid combinatorial explosion. The purpose of the proposed approach is to deconstruct large scale problem into small size sub-problems and gradually restore these to original state. Firstly, an original large scale problem is divided into some small sub-areas and optimal solutions in each sub-area are derived. When a best incumbent solution remains unchanged for a certain period, subareas are gradually integrated and new optimal solutions in a new integrated sub-area are newly searched through use of the obtained solutions in previous sub area. This gradual integration and optimization are iterated until every sub-area are integrated into the one (the original problem), and the optimal solution of original problem can be obtained at this time. The proposed approach aims to deconstruct large scale problem into small size sub-problems and perform more efficient search. Through some typical test problems, it was demonstrated that our approach could derive better results more effectively than conventional approach.
机译:提出的方法专门针对大型车辆路径问题(VRP),并基于区域分割和渐进区域集成机制,以避免组合爆炸。提出的方法的目的是将大规模问题分解为小规模的子问题,并将其逐步恢复为原始状态。首先,将原始的大规模问题划分为一些小子区域,并得出每个子区域的最优解。当最佳现有解决方案在一定时期内保持不变时,将逐步合并子区域,并通过使用先前子区域中获得的解决方案来在新的集成子区域中重新搜索新的最佳解决方案。逐步进行这种逐步的集成和优化,直到将每个子区域都集成到一个分区中(原始问题)为止,此时可以获取原始问题的最佳解决方案。所提出的方法旨在将大规模问题分解为小尺寸的子问题,并执行更有效的搜索。通过一些典型的测试问题,证明了我们的方法可以比传统方法更有效地得出更好的结果。

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