对比四种随机优化算法在三类问题上的表现，给出选型建议。
Benchmarking Randomized Optimization Algorithms on Binary, Permutation, and Combinatorial Problem Landscapes
- 系统测试四类算法在二值、排列与组合问题上的表现。
- MIMIC和GA在二值与组合问题上解质量高，但计算开销大。
- RHC和SA虽快但复杂问题中表现有限，适合资源受限场景。
本文评估了四种随机优化算法——随机爬山（RHC）、模拟退火（SA）、遗传算法（GA）和MIMIC（互信息最大化输入聚类）——在二值、排列和组合三类问题上的表现。通过一系列基准适应度函数，系统比较了各算法在解质量、收敛速度、计算成本和鲁棒性等指标上的差异。结果表明，尽管MIMIC和GA在二值与组合问题上能生成高质量解，但其计算开销差异显著；而RHC和SA虽然计算成本较低，但在复杂问题景观中表现有限。研究揭示了不同优化策略的权衡关系，为根据问题类型、精度需求和计算约束选择合适算法提供了实用指导。
原文摘要 · Abstract (English)
In this paper, we evaluate the performance of four randomized optimization algorithms: Randomized Hill Climbing (RHC), Simulated Annealing (SA), Genetic Algorithms (GA), and MIMIC (Mutual Information Maximizing Input Clustering), across three distinct types of problems: binary, permutation, and combinatorial. We systematically compare these algorithms using a set of benchmark fitness functions that highlight the specific challenges and requirements of each problem category. Our study analyzes each algorithm's effectiveness based on key performance metrics, including solution quality, convergence speed, computational cost, and robustness. Results show that while MIMIC and GA excel in producing high-quality solutions for binary and combinatorial problems, their computational demands vary significantly. RHC and SA, while computationally less expensive, demonstrate limited performance in complex problem landscapes. The findings offer valuable insights into the trade-offs between different optimization strategies and provide practical guidance for selecting the appropriate algorithm based on the type of problems, accuracy requirements, and computational constraints.
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