Differential evolution algorithm with dynamic multi-population applied to flexible job shop schedule-Reference-Cited by-同舟云学术

Differential evolution algorithm with dynamic multi-population applied to flexible job shop schedule

Published:2021-02-22 Issue: Volume: Page:1-22
ISSN:0305-215X
Container-title:Engineering Optimization
language:en
Short-container-title:Engineering Optimization

Author:

Cao Yang¹²³⁴⁵,Shi Haibo²³⁵,Chang DaLiang²³⁴⁵

Affiliation:

1. College of Information Science and Engineering, Northeastern University, Shenyang, People’s Republic of China

2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, People’s Republic of China

3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, People’s Republic of China

4. University of Chinese Academy of Sciences, Beijing, People’s Republic of China

5. Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, People’s Republic of China

Funder

Foundation of LiaoNing Revitalization Talents Program, China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Industrial and Manufacturing Engineering,Management Science and Operations Research,Control and Optimization,Computer Science Applications

Link

https://www.tandfonline.com/doi/pdf/10.1080/0305215X.2021.1872067

Reference46 articles.

1. PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems

2. Chaotic bee colony algorithms for global numerical optimization

3. Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems

4. Differential Evolution With Neighborhood and Direction Information for Numerical Optimization

5. Differential Evolution: A Survey of the State-of-the-Art

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