标题： 在二进制、排列和组合问题景观上对随机优化算法进行基准测试 显示英文标题

标题： Benchmarking Randomized Optimization Algorithms on Binary, Permutation, and Combinatorial Problem Landscapes

摘要： 在本文中，我们评估了四种随机优化算法：随机爬山法（RHC）、模拟退火（SA）、遗传算法（GA）和MIMIC（最大互信息输入聚类）在三种不同类型问题上的性能：二进制、排列和组合问题。我们使用一组基准适应度函数系统地比较这些算法，这些函数突出了每种问题类别的特定挑战和要求。我们的研究基于关键性能指标分析了每种算法的有效性，包括解决方案质量、收敛速度、计算成本和鲁棒性。结果表明，虽然MIMIC和GA在二进制和组合问题上能够产生高质量的解决方案，但它们的计算需求差异显著。RHC和SA虽然计算成本较低，但在复杂的问题环境中表现出有限的性能。研究结果为不同优化策略之间的权衡提供了有价值的见解，并根据问题类型、精度要求和计算约束提供了选择适当算法的实用指导。 显示英文摘要

摘要： 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.