City group optimization

Yijun Yang; Haibin Duan

doi:10.1109/ChiCC.2015.7260034

City group optimization

Yijun Yang
, Haibin Duan

School of Automation Science and Electrical Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we propose a novel swarm intelligence optimization algorithm-city group optimization (CGO). CGO loosely mimics the evolution of city group. The basic components of CGO include road network, position updating rules, and transportation hub updating. These components are inspired by the evolutionary phenomena in city group. The detailed implementation procedure is also given. Series of comparative experiments on six benchmark functions with particle swarm optimization (PSO) are conducted, and the results verify the feasibility and effectiveness of our proposed CGO in solving continuous optimization problems.

Original language	English
Title of host publication	Proceedings of the 34th Chinese Control Conference, CCC 2015
Editors	Qianchuan Zhao, Shirong Liu
Publisher	IEEE Computer Society
Pages	2568-2572
Number of pages	5
ISBN (Electronic)	9789881563897
DOIs	https://doi.org/10.1109/ChiCC.2015.7260034
State	Published - 11 Sep 2015
Event	34th Chinese Control Conference, CCC 2015 - Hangzhou, China Duration: 28 Jul 2015 → 30 Jul 2015

Publication series

Name	Chinese Control Conference, CCC
Volume	2015-September
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	34th Chinese Control Conference, CCC 2015
Country/Territory	China
City	Hangzhou
Period	28/07/15 → 30/07/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

City group optimization (CGO)
Continuous problems
Swarm intelligence

Access to Document

10.1109/ChiCC.2015.7260034

Cite this

@inproceedings{54205e0c53c54926a0bd3c54a94e3d81,

title = "City group optimization",

abstract = "In this paper, we propose a novel swarm intelligence optimization algorithm-city group optimization (CGO). CGO loosely mimics the evolution of city group. The basic components of CGO include road network, position updating rules, and transportation hub updating. These components are inspired by the evolutionary phenomena in city group. The detailed implementation procedure is also given. Series of comparative experiments on six benchmark functions with particle swarm optimization (PSO) are conducted, and the results verify the feasibility and effectiveness of our proposed CGO in solving continuous optimization problems.",

keywords = "City group optimization (CGO), Continuous problems, Swarm intelligence",

author = "Yijun Yang and Haibin Duan",

note = "Publisher Copyright: {\textcopyright} 2015 Technical Committee on Control Theory, Chinese Association of Automation.; 34th Chinese Control Conference, CCC 2015 ; Conference date: 28-07-2015 Through 30-07-2015",

year = "2015",

month = sep,

day = "11",

doi = "10.1109/ChiCC.2015.7260034",

language = "英语",

series = "Chinese Control Conference, CCC",

publisher = "IEEE Computer Society",

pages = "2568--2572",

editor = "Qianchuan Zhao and Shirong Liu",

booktitle = "Proceedings of the 34th Chinese Control Conference, CCC 2015",

address = "美国",

}

TY - GEN

T1 - City group optimization

AU - Yang, Yijun

AU - Duan, Haibin

PY - 2015/9/11

Y1 - 2015/9/11

N2 - In this paper, we propose a novel swarm intelligence optimization algorithm-city group optimization (CGO). CGO loosely mimics the evolution of city group. The basic components of CGO include road network, position updating rules, and transportation hub updating. These components are inspired by the evolutionary phenomena in city group. The detailed implementation procedure is also given. Series of comparative experiments on six benchmark functions with particle swarm optimization (PSO) are conducted, and the results verify the feasibility and effectiveness of our proposed CGO in solving continuous optimization problems.

AB - In this paper, we propose a novel swarm intelligence optimization algorithm-city group optimization (CGO). CGO loosely mimics the evolution of city group. The basic components of CGO include road network, position updating rules, and transportation hub updating. These components are inspired by the evolutionary phenomena in city group. The detailed implementation procedure is also given. Series of comparative experiments on six benchmark functions with particle swarm optimization (PSO) are conducted, and the results verify the feasibility and effectiveness of our proposed CGO in solving continuous optimization problems.

KW - City group optimization (CGO)

KW - Continuous problems

KW - Swarm intelligence

UR - https://www.scopus.com/pages/publications/84946553156

U2 - 10.1109/ChiCC.2015.7260034

DO - 10.1109/ChiCC.2015.7260034

M3 - 会议稿件

AN - SCOPUS:84946553156

T3 - Chinese Control Conference, CCC

SP - 2568

EP - 2572

BT - Proceedings of the 34th Chinese Control Conference, CCC 2015

A2 - Zhao, Qianchuan

A2 - Liu, Shirong

PB - IEEE Computer Society

T2 - 34th Chinese Control Conference, CCC 2015

Y2 - 28 July 2015 through 30 July 2015

ER -

City group optimization

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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