A Teaching-Learning-Based Optimization with Uniform Design for Solving Constrained Optimization Problems

As a newly developed population-based metaheuristic algorithm, teaching-learning-based optimization (TBLO) has been gained extensively attention since it was proposed in 2011. It has been applied to many optimal problems and a lot of algorithms have also been designed to solve these real-world problems. In this paper, TBLO with uniform design is proposed for solving constrained optimization problems. The performance of the proposed algorithm is checked by experiments with two type of different benchmark problems under the criteria of best, mean, worst, function evaluations and ratio of feasible search space. Compared results are given to illustrate the efficiency of the proposed algorithm.