Teaching-Learning-Based Optimization Enhanced With Multiobjective Sorting Based and Cooperative Learning

Teaching-learning-based optimization (TLBO) algorithm has been shown to be an effective optimization algorithm. However, it is easily trapped into local optima when the global optimal solution of the function to be optimized is at the original dot or around the original dot. This paper presents a novel TLBO variant by incorporating multiobjective sorting-based mechanism and cooperative learning strategy to alleviate this problem. Taking advantages of multiobjective optimization in maintaining good population diversity, several teachers are selected based on non-dominated sorting, so as to guide learners to learn more effectively. In addition, the proposed algorithm adopts cooperative learning, including learning within and between groups, to improve the search ability of the algorithm. Experimental and statistical analyses are performed on CEC2014 benchmark functions. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison with other variants of TLBO and other state-of-the-art optimization algorithms.