  This paper presents an enhanced version of the Learner Performance-based
Behavior (LPB), a novel metaheuristic algorithm inspired by the process of
accepting high-school students into various departments at the university. The
performance of the LPB is not according to the required level. This paper aims
to improve the performance of a single objective LPB by embedding ten chaotic
maps within LPB to propose Chaotic LPB (CLPB). The proposed algorithm helps in
reducing the Processing Time (PT), getting closer to the global optima, and
bypassing the local optima with the best convergence speed. Another improvement
that has been made in CLPB is that the best individuals of a sub-population are
forced into the interior crossover to improve the quality of solutions. CLPB is
evaluated against multiple well-known test functions such as classical
(TF1_TF19) and (CEC_C06 2019). Additionally, the results have been compared to
the standard LPB and several well-known metaheuristic algorithms such as Dragon
Fly Algorithm (DA), Genetic Algorithm (GA), and Particle Swarm Optimization
(PSO). Finally, the numerical results show that CLPB has been improved with
chaotic maps. Furthermore, it is verified that CLPB has a great ability to deal
with large optimization problems compared to LPB, GA, DA, and PSO. Overall,
Gauss and Tent maps both have a great impact on improving CLPB.
