使用模拟退火优化LPB算法
Optimizing LPB Algorithms using Simulated Annealing
AI总结：
提出LPBSA算法，通过在LPB算法的交叉和变异步骤间应用模拟退火及Metropolis接受准则筛选最优个体，显著提升了种群质量和求解效率。
摘要：
基于学习者性能的行为使用模拟退火（LPBSA）是 Learner Performance-based Behavior (LPB) 算法的改进。与 LPB 一样，LPBSA 已被证明能够处理单一和复杂问题。模拟退火（SA）已被用作优化 LPB 的强大技术。LPBSA 提供的结果优于遗传算法（GA）、粒子群优化（PSO）甚至 LPB 等流行算法。本研究概述了该改进算法的工作流程：提供一个主种群并将其划分为 Good 和 Bad 种群，然后应用交叉和变异算子。当某些个体在交叉阶段诞生时，它们必须经历变异过程。在这两个步骤之间，我们应用了使用 Metropolis 接受准则（MAC）的 SA，仅接受最好且最有用的个体用于下一次迭代。最终结果表明种群得到了增强，从而提高了效率并验证了 LPBSA 的性能。
英文摘要：
Learner Performance-based Behavior using Simulated Annealing (LPBSA) is an improvement of the Learner Performance-based Behavior (LPB) algorithm. LPBSA, like LPB, has been proven to deal with single and complex problems. Simulated Annealing (SA) has been utilized as a powerful technique to optimize LPB. LPBSA has provided results that outperformed popular algorithms, like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and even LPB. This study outlines the improved algorithm's working procedure by providing a main population and dividing it into Good and Bad populations and then applying crossover and mutation operators. When some individuals are born in the crossover stage, they have to go through the mutation process. Between these two steps, we have applied SA using the Metropolis Acceptance Criterion (MAC) to accept only the best and most useful individuals to be used in the next iteration. Finally, the outcomes demonstrate that the population is enhanced, leading to improved efficiency and validating the performance of LPBSA.
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