期刊论文、会议论文

Wu, Daqing

英文

Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015

1948-9439

2015

1975-1979

27th Chinese Control and Decision Conference (CCDC)

Chinese Control and Decision Conference

MAY 23-25, 2015

Qingdao, PEOPLES R CHINA

[Wu, Daqing] Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Anhui, Peoples R China.^[Wu, Daqing;Liu, Li;Gong, XiangJian] Univ South China, Comp Sci & Technol Inst, Hengyang 421001, Hunan, Peoples R China.^[Wu, Daqing;Deng, Li] DongHua Univ, Glorious Sun Sch Business & Management, Shanghai 200051, Peoples R China.

IEEE Ind Elect Chapter, Qingdao Univ, IEEE Control Syst Soc, Syst Engn Soc China, Chinese Assoc Artificial Intelligence, Chinese Assoc Automat, Tech Comm Control Theory, Northeastern Univ

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-4799-7016-2

10.1109/CCDC.2015.7162244

supported by the project of ministry of education of Hunan Province under Grant.13C818;the Industrial Science and Technology Support Project of Hunan Province;Hengyang under Grant.2013KG63;The Open Project Program of Key Laboratory of Intelligent Computing&Signal Processing;Ministry of Education;Anhui University;China under Grant.2014C16304

本校为其他机构

An efficient co-evolutionary multi-objective particle swarm optimizer named ECMPSO was proposed. ECMPSO uses dynamic multiple swarms to deal with multiple objectives, taking one objective is optimized by each swarm into account, and maintains diversity of new found non-dominated solutions via adopts a three-level particle swarm optimization(PSO) updating rule wherein the particles learn their experiences based on personal, neighborhood, and external archive. To prove the validity of the ECMPSO algorithm for solving multi-objective problems, some benchmark problems and one real-life problem are...