版权说明 操作指南
首页 > 成果 > 详情

Particle Swarm Optimization with a Simulated Binary Crossover Operator

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文、会议论文
作者:
Yang, Lei;Yang, Caixia*;Yuliu
通讯作者:
Yang, Caixia
作者机构:
[Yang, Caixia] Wuhan Polytech Univ, Dept Elect Informat Engn, Wuhan 430023, Peoples R China.
AVIC Shanghai AERO Measurement Controlling Res In, Shanghai 201601, Peoples R China.
通讯机构:
[Yang, Caixia] W
Wuhan Polytech Univ, Dept Elect Informat Engn, Wuhan 430023, Peoples R China.
语种:
英文
关键词:
particle swarm optimization;evolutionary algorithms;swarm intelligence;global optimization
期刊:
Indonesian Journal of Electrical Engineering and Computer Science
ISSN:
2502-4760
年:
2014
卷:
12
期:
12
页码:
8286-8291
会议名称:
5th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)
会议时间:
JUN 15-16, 2014
会议地点:
Zhangjiajie, PEOPLES R CHINA
会议主办单位:
[Yang, Caixia] Wuhan Polytech Univ, Dept Elect Informat Engn, Wuhan 430023, Peoples R China.^AVIC Shanghai AERO Measurement Controlling Res In, Shanghai 201601, Peoples R China.
会议赞助商:
Xian Shiyou Univ, Hunan Inst Engn, Cent S Univ, St Johns Univ, Natl Univ Defense Technol, Dept Elect Sci & Technol
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-4799-4262-6
机构署名:
本校为第一且通讯机构
院系归属:
电气与电子工程学院
摘要:
Particle swarm optimization (PSO) is a new intelligent search technique, which is inspired by swarm intelligence. Although PSO has shown good performance in many benchmark optimization problems, it suffers from premature convergence in solving complex multimodal problems. In this paper, we propose a novel PSO algorithm, called PSO with a simulated binary crossover operator (SCPSO), to improve the performance of PSO. Experimental results on several benchmark problems sh...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com