title: An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization.
abstract: An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.
authors.affiliation: School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Zou
authors.affiliation: School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China.
authors.forename: Debao
authors.initials: D
authors.lastname: Chen
authors.affiliation: School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China.
authors.forename: Jiangtao
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007858:Learning; D013663:Teaching