  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.
