Effective hybridization of JAYA and teaching–learning-based optimization algorithms for numerical function optimization

The JAYA is classified as the state-of-the-art population-oriented algorithm for the optimization of diverse problems, both discrete and continuous. The concept behind this algorithm is to present a solution by means of the best and worst individuals in the population. On the other hand, teaching–learning-based optimization algorithm cooperation of a teacher on students’ learning process. Due to each one having some benefits and drawbacks, combining those leads to better exploring the problem. Consequently, this investigation exploits the hybridization of both mentioned algorithms, and a novel algorithm is made named H-JTLBO (hybridization of JAYA and teaching learning-based optimization). The proposed approach is then evaluated using different test functions used frequently in the literate. Finally, the results of such functions are compared with other optimization algorithms which have recently been introduced in the literature, such as Sine Cosine Algorithm (SCA), Grasshopper Optimization Algorithm (GOA), Moth-flame optimization (MFO), and JAYA algorithm. In addition, the statistical test is used to evaluate the proposed method. Through the results, H-JTLBO outperforms all mentioned algorithms in terms of convergence and solution quality.