Development and Applications of a New Optimization Algorithm

Abstract

All the nature-inspired algorithms such as genetic algorithm (GA), PSO, BBO, ABC, DE, etc. require algorithm parameters to be set for their proper working. Proper selection of parameters is essential for the searching of the optimum solution by these algorithms. A change in the algorithm parameters changes the effectiveness of the algorithms. To avoid this difficulty, an optimization method named ‘Teaching–Learning-Based Optimization (TLBO)’ is presented in this chapter. This method works on the effect of influence of a teacher on learners. The performance of the proposed TLBO method is checked with the recent and well-known optimization algorithms such as GA, ABC, PSO, HS, DE and hybrid algorithms by experimenting with different constrained and unconstrained benchmark problems and mechanical element design optimization problems with different characteristics. The effectiveness of TLBO method is also checked for different performance criteria, like success rate, mean solution, average function evaluations required, convergence rate, etc. The results show better performance of TLBO method over other natured-inspired optimization methods for the considered benchmark functions and mechanical element design optimization problems. Also, the TLBO method shows better performance with less computational effort for the large-scale problems, i.e. problems with high dimensions.