标题： LPBSA：通过基于学习者性能的行为和模拟退火提高优化效率 显示英文标题

标题： LPBSA: Enhancing Optimization Efficiency through Learner Performance-based Behavior and Simulated Annealing

摘要： 这项研究介绍了LPBSA，这是一种先进的优化算法，通过混合方法结合基于学习者表现的行为（LPB）和模拟退火（SA）。 强调元启发式方法，LPBSA解决了传统LPB方法相关的问题，提高了在解决复杂优化问题时的收敛性、鲁棒性和适应性。 通过使用基准测试函数进行广泛评估，LPBSA表现出优于LPB的性能，并与PSO、FDO、LEO和GA等成熟算法竞争。 实际应用证明了该算法的潜力，在两个测试场景中，LPBSA的表现优于LEO算法。 根据研究结果，许多测试函数的结果，如TF5记录的（4.76762333）以及结果部分提供的其他一些测试函数，证明了LPBSA优于流行的算法。 这项研究突显了混合方法在优化算法持续演进中的有效性，展示了LPBSA在应对多样化优化环境和解决复杂优化挑战方面的显著能力。 显示英文摘要

摘要： This study introduces the LPBSA, an advanced optimization algorithm that combines Learner Performance-based Behavior (LPB) and Simulated Annealing (SA) in a hybrid approach. Emphasizing metaheuristics, the LPBSA addresses and mitigates the challenges associated with traditional LPB methodologies, enhancing convergence, robustness, and adaptability in solving complex optimization problems. Through extensive evaluations using benchmark test functions, the LPBSA demonstrates superior performance compared to LPB and competes favorably with established algorithms such as PSO, FDO, LEO, and GA. Real-world applications underscore the algorithm's promise, with LPBSA outperforming the LEO algorithm in two tested scenarios. Based on the study results many test function results such as TF5 by recording (4.76762333) and some other test functions provided in the result section prove that LPBSA outperforms popular algorithms. This research highlights the efficacy of a hybrid approach in the ongoing evolution of optimization algorithms, showcasing the LPBSA's capacity to navigate diverse optimization landscapes and contribute significantly to addressing intricate optimization challenges.