为复杂土地利用问题设计新依赖关系，提升优化效率
Seeking and leveraging alternative variable dependency concepts in gray-box-elusive bimodal land-use allocation problems
- 提出面向特定问题的变量依赖新定义
- 构建三种新型交叉算子，使优化效果显著提升
- 适用于难以用传统方法解决的多目标规划场景
解决土地利用分配问题有助于应对全球紧迫的环境挑战。由于此类问题属于NP难，需高效优化器处理。变量依赖知识可帮助设计有效工具，但在本研究中，面对一个真实世界多目标问题，标准依赖发现技术失效，基于关联的变异算子无法使用。为此，我们提出问题专用的变量依赖定义，并据此生成依赖变量掩码，构建了三种新型交叉算子。在真实案例测试中，将这些方法引入两个知名多目标优化器（NSGA-II、MOEA/D）后，显著提升了其有效性。
原文摘要 · Abstract (English)
Solving land-use allocation problems can help us to deal with some of the most urgent global environmental issues. Since these problems are NP-hard, effective optimizers are needed to handle them. The knowledge about variable dependencies allows for proposing such tools. However, in this work, we consider a real-world multi-objective problem for which standard variable dependency discovery techniques are inapplicable. Therefore, using linkage-based variation operators is unreachable. To address this issue, we propose a definition of problem-dedicated variable dependency. On this base, we propose obtaining masks of dependent variables. Using them, we construct three novel crossover operators. The results concerning real-world test cases show that introducing our propositions into two well-known optimizers (NSGA-II, MOEA/D) dedicated to multi-objective optimization significantly improves their effectiveness.
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