An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization. (14th January 2016)

• Record Type: Journal Article
• Authors: Zou, Feng; Chen, Debao; Wang, Jiangtao
• Other Names: Conforto Silvia Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2016(2016)
• Publication Date: 2016-01-14
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2016/4561507
• Languages: English
• ISSNs: 1687-5265

Abstract

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.