Local and global search based PSO algorithm

Yanxia Sun; Zenghui Wang; Barend Jacobus Van Wyk

doi:10.1007/978-3-642-38703-6_15

Local and global search based PSO algorithm

Yanxia Sun, Zenghui Wang, Barend Jacobus Van Wyk

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

5 Citations (Scopus)

Abstract

In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.

Original language	English
Title of host publication	Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
Pages	129-136
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-38703-6_15
Publication status	Published - 2013
Externally published	Yes
Event	4th International Conference on Advances in Swarm Intelligence, ICSI 2013 - Harbin, China Duration: 12 Jun 2012 → 15 Jun 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7928 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Advances in Swarm Intelligence, ICSI 2013
Country/Territory	China
City	Harbin
Period	12/06/12 → 15/06/12

Keywords

Local search
global search
particle swarm optimisation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-38703-6_15

Cite this

Sun, Y., Wang, Z., & Van Wyk, B. J. (2013). Local and global search based PSO algorithm. In Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings (PART 1 ed., pp. 129-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7928 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-38703-6_15

@inproceedings{c4633472371e4c95aec1e25889b38b6c,

title = "Local and global search based PSO algorithm",

abstract = "In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.",

keywords = "Local search, global search, particle swarm optimisation",

author = "Yanxia Sun and Zenghui Wang and {Van Wyk}, {Barend Jacobus}",

year = "2013",

doi = "10.1007/978-3-642-38703-6_15",

language = "English",

isbn = "9783642387029",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "129--136",

booktitle = "Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings",

edition = "PART 1",

note = "4th International Conference on Advances in Swarm Intelligence, ICSI 2013 ; Conference date: 12-06-2012 Through 15-06-2012",

}

Sun, Y, Wang, Z & Van Wyk, BJ 2013, Local and global search based PSO algorithm. in Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7928 LNCS, pp. 129-136, 4th International Conference on Advances in Swarm Intelligence, ICSI 2013, Harbin, China, 12/06/12. https://doi.org/10.1007/978-3-642-38703-6_15

Local and global search based PSO algorithm. / Sun, Yanxia; Wang, Zenghui; Van Wyk, Barend Jacobus.
Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 1. ed. 2013. p. 129-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7928 LNCS, No. PART 1).

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

TY - GEN

T1 - Local and global search based PSO algorithm

AU - Sun, Yanxia

AU - Wang, Zenghui

AU - Van Wyk, Barend Jacobus

PY - 2013

Y1 - 2013

N2 - In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.

AB - In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.

KW - Local search

KW - global search

KW - particle swarm optimisation

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U2 - 10.1007/978-3-642-38703-6_15

DO - 10.1007/978-3-642-38703-6_15

M3 - Conference contribution

AN - SCOPUS:84884897417

SN - 9783642387029

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 129

EP - 136

BT - Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings

T2 - 4th International Conference on Advances in Swarm Intelligence, ICSI 2013

Y2 - 12 June 2012 through 15 June 2012

ER -

Local and global search based PSO algorithm

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