CLPB: Chaotic Learner Performance based Behaviour

Abstract Learner Performance-based Behavior (LPB) is a recent metaheuristic algorithm that is inspired by the process of accepting students who have recently graduated from high schools into various departments at university according to the student’s GPA (Grade Point Average). Two versions of the algorithm are available; single and multi-objective versions. The exploitation phase, convergence speed, and processing time (PT) of the LPB are not according to the required level.This paper aims to improve the performance of a single objective LPB through modification which helps in reducing its PT, getting closer to the global optima, and bypassing the local optima with the best convergence speed. For that, ten chaos theory maps are used within LPB to propose Chaotic LPB (CLPB) to consider which of the ten maps works well in case of performance. Another modification that has been made to LPB 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 DA, GA, and PSO. Finally, the numerical results show that CLPB has been improved with chaotic maps. Furthermore, it is verified that CLPB has a great ability in dealing with large optimization problems compared to LPB, GA, DA, and PSO. Overall, Gauss and Tent maps both have a great impact on improving CLPB.