期刊
JOURNAL OF INTELLIGENT MANUFACTURING
卷 27, 期 4, 页码 831-843出版社
SPRINGER
DOI: 10.1007/s10845-014-0918-3
关键词
Improved teaching-learning-based optimization; Differential evolution; Chaotic perturbation; Unconstrained optimization; Constrained optimization
资金
- Major State Basic Research Development Program of China [2012CB720500]
- National Natural Science Foundation of China [61333010, 61222303]
- Fundamental Research Funds for the Central Universities
- National High-Tech Research and Development Program of China [2013AA040701]
- National Key Scientific and Technical Project of China [2012BAF05B00]
- Shanghai RAMP
- D Platform Construction Program [13DZ2295300]
- State Key Laboratory of Synthetical Automation for Process Industries
The teaching-learning-based optimization (TLBO) algorithm, one of the recently proposed population-based algorithms, simulates the teaching-learning process in the classroom. This study proposes an improved TLBO (ITLBO), in which a feedback phase, mutation crossover operation of differential evolution (DE) algorithms, and chaotic perturbation mechanism are incorporated to significantly improve the performance of the algorithm. The feedback phase is used to enhance the learning style of the students and to promote the exploration capacity of the TLBO. The mutation crossover operation of DE is introduced to increase population diversity and to prevent premature convergence. The chaotic perturbation mechanism is used to ensure that the algorithm can escape the local optimal. Simulation results based on ten unconstrained benchmark problems and five constrained engineering design problems show that the ITLBO algorithm is better than, or at least comparable to, other state-of-the-art algorithms.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据