论文标题
一种新的进化算法：基于学习者绩效的行为算法
A new evolutionary algorithm: Learner performance based behavior algorithm
论文作者
论文摘要
本文提出了一种新型的进化算法，称为基于学习者的行为算法（LPB）。 LPB的基本灵感来自于接受大学不同系的高中接受毕业学习者的过程。此外，这些学习者在学习行为中应该做的改变，以提高大学的学习水平。优化的最重要阶段；通过设计接受从高中到大学的毕业学习者的过程以及改善大学学习行为以提高他们的学习水平的过程，概述了剥削和探索的过程。为了显示所提出算法的准确性，可以根据许多测试功能进行评估，例如传统的基准功能，CEC-C06 2019测试功能和现实世界中的案例研究问题。然后将所提出的算法的结果与DA，GA和PSO进行比较。在大多数情况下，提出的算法在其他一些情况下产生了较高的结果，而在其他一些情况下则产生了比较。事实证明，该算法具有与DA，GA和PSO相比的大型优化问题的出色能力。总体结果证明了LPB在改善初始人群并融入全球最佳状态的能力。此外，拟议工作的结果在统计上得到了证明。
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. 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.
