Exploiting Problem Decomposition in Multi-objective Constraint Optimization
Exploiting Problem Decomposition in Multi-objective Constraint Optimization
复制标题
在多目标约束优化中利用问题分解
DOI：
10.1007/978-3-642-04244-7_47
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Radu Marinescu
中科院分区：
文献类型：
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作者：
Radu Marinescu
多目标优化涉及涉及多个性能指标的问题，这些指标应同时优化。本文将与或分支定界（AOBB）算法从单目标优化推广到多目标优化。新算法MO-AOBB通过遍历AND/OR搜索树有效地利用了问题结构，并使用静态和动态迷你桶启发式算法来指导搜索。我们表明，MO-AOBB显着提高了传统的OR搜索方法，在多目标优化的各种基准。
Multi-objective optimization is concerned with problems involving multiple measures of performance which should be optimized simultaneously. In this paper, we extend AND/OR Branch-and-Bound (AOBB), a well known search algorithm, from mono-objective to multi-objective optimization. The new algorithm MO-AOBB exploits efficiently the problem structure by traversing an AND/OR search tree and uses static and dynamic mini-bucket heuristics to guide the search. We show that MO-AOBB improves dramatically over the traditional OR search approach, on various benchmarks for multi-objective optimization.