A self-adaptive teaching-and-learning-based optimization algorithm with a mixed strategy

被引：0

作者：

Bi X. ^{[1
]}

Li Y. ^{[1
]}

Chen C. ^{[1
]}

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2016年 / 37卷 / 06期

关键词：

Comprehensive cross learning; Disturbance strategy; Mixed-strategy; Self-adaptation; Teaching-and-learning based optimization;

D O I：

10.11990/jheu.201505076

中图分类号：

学科分类号：

摘要：

In order to improve the overall performance of teaching-and-learning-based optimization (TLBO), in this paper, we propose a new self-adaptive teaching-and-learning-based optimization algorithm that uses a mixed strategy (MSTLBO). This strategy combines adaptive integrated cross learning with random and directional learning. These learning methods are chosen adaptively to enhance the searching ability for different evolutionary stages. We adopt a kind of directional disturbance strategy to increase the population diversity, and to avoid the possibility of the population falling into a local optimum. The experimental results on the benchmark functions show that the MSTLBO exhibits good performance in avoiding premature convergence, and both the convergence accuracy and convergence rate are significantly improved. © 2016, Editorial Department of Journal of HEU. All right reserved.

引用

页码：842 / 848

页数：6

共 12 条

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