通过分析景观特征，揭示多目标优化算法性能差异原因
Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance
- 基于压缩帕累托局部最优网络模型提取景观特征
- 发现特定特征组合显著影响三类算法性能表现
- 适合研究优化算法机制或设计新算法的学者
我们分析了用于预测多目标组合优化算法性能的景观特征。采用近期提出的压缩帕累托局部最优解网络（C-PLOS-net）模型提取的特征，以2至3个目标、不同崎岖度和目标相关性的rmnk-landscapes为基准实例。评估了帕累托局部搜索（PLS）、全局简单EMO优化器（GSEMO）和非支配排序遗传算法（NSGA-II）三种算法在分辨率与超体积指标下的表现。定制化分析揭示了特定景观下影响算法性能的关键特征组合。本研究深化了对特征重要性的理解，且针对特定rmnk-landscapes与算法进行了个性化分析。
原文摘要 · Abstract (English)
We present an analysis of landscape features for predicting the performance of multi-objective combinatorial optimization algorithms. We consider features from the recently proposed compressed Pareto Local Optimal Solutions Networks (C-PLOS-net) model of combinatorial landscapes. The benchmark instances are a set of rmnk-landscapes with 2 and 3 objectives and various levels of ruggedness and objective correlation. We consider the performance of three algorithms -- Pareto Local Search (PLS), Global Simple EMO Optimizer (GSEMO), and Non-dominated Sorting Genetic Algorithm (NSGA-II) - using the resolution and hypervolume metrics. Our tailored analysis reveals feature combinations that influence algorithm performance specific to certain landscapes. This study provides deeper insights into feature importance, tailored to specific rmnk-landscapes and algorithms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。