Solving Composite Test Functions Using Teaching-Learning-Based Optimization Algorithm

Abstract

Multimodal function optimization has attracted a growing interest especially in the evolutionary computation research community. Multimodal optimization deals with optimization tasks that involve finding all or most of the multiple solutions (as opposed to a single best solution). The challenge is to identify as many optima as possible to provide a choice of good solutions to the designers. A composite function is a combination of the two or more functions. The Teaching-Learning-Based Optimization (TLBO) algorithm is a teaching-learning process inspired algorithm based on the effect of influence of a teacher on the output of learners in a class. In this paper, the TLBO algorithm has been tested on six composite test functions for numerical global optimization. The TLBO algorithm has outperformed the other six algorithms for the composite test problems considered.