A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization; exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner’s studying behavior at university to improve the level of their study, respectively. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically.

本文提出了一种新的进化算法，称为基于学习器性能的行为算法（LPB）。LPB的基本灵感来源于大学不同院系接收高中毕业生的过程。此外，学习者应该在学习行为上做一些改变，以提高他们在大学的学习水平。最重要的优化阶段；开发和探索分别通过设计从高中到大学接受毕业学习者的过程和改善学习者在大学的学习行为以提高他们的学习水平的过程来概述。为了显示所提出算法的准确性，它针对许多测试函数进行了评估，例如传统的基准函数、CEC-C06 2019 测试函数、和一个现实世界的案例研究问题。然后将所提出算法的结果与 DA、GA 和 PSO 进行比较。所提出的算法在大多数情况下产生了优异的结果，在其他一些情况下产生了比较。证明该算法与DA、GA和PSO相比，在处理大型优化问题方面具有很强的能力。总体结果证明了LPB在改善初始种群和向全局最优收敛方面的能力。此外，建议工作的结果在统计上得到了证明。证明该算法与DA、GA和PSO相比，在处理大型优化问题方面具有很强的能力。总体结果证明了LPB在改善初始种群和向全局最优收敛方面的能力。此外，建议工作的结果在统计上得到了证明。证明该算法与DA、GA和PSO相比，在处理大型优化问题方面具有很强的能力。总体结果证明了LPB在改善初始种群和向全局最优收敛方面的能力。此外，建议工作的结果在统计上得到了证明。