Abstract
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.