Hybrid Particle Swarm Optimization with Bat Algorithm

Tien Szu Pan; Thi Kien Dao; Trong The Nguyen; Shu Chuan Chu

doi:10.1007/978-3-319-12286-1_5

Hybrid Particle Swarm Optimization with Bat Algorithm

Tien Szu Pan, Thi Kien Dao, Trong The Nguyen, Shu Chuan Chu

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

42 Citations (Scopus)

Abstract

In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

Original language	English
Title of host publication	Genetic and Evolutionary Computing
Subtitle of host publication	Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China
Editors	Hui Sun, Ching-Yu Yang, Chun-Wei Lin, Jeng-Shyang Pan, Vaclav Snasel, Ajith Abraham
Place of Publication	Switzerland
Publisher	Springer-Verlag
Pages	37-47
Number of pages	11
ISBN (Electronic)	9783319122861
ISBN (Print)	9783319122854
DOIs	https://doi.org/10.1007/978-3-319-12286-1_5
Publication status	Published - 2015
Event	8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014 - Nanchang, China Duration: 18 Oct 2014 → 20 Oct 2014 Conference number: 8

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	329
ISSN (Print)	2194-5357

Conference

Conference	8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014
Country/Territory	China
City	Nanchang
Period	18/10/14 → 20/10/14

Keywords

Bat Algorithm Optimizations
Hybrid Particle Swarm Optimization with Bat Algorithm
Particle Swarm Optimization Algorithm
Swarm Intelligence

Access to Document

10.1007/978-3-319-12286-1_5Licence: In Copyright

https://rdcu.be/dBeVzLicence: In Copyright

Cite this

Pan, T. S., Dao, T. K., Nguyen, T. T., & Chu, S. C. (2015). Hybrid Particle Swarm Optimization with Bat Algorithm. In H. Sun, C.-Y. Yang, C.-W. Lin, J.-S. Pan, V. Snasel, & A. Abraham (Eds.), Genetic and Evolutionary Computing : Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China (pp. 37-47). (Advances in Intelligent Systems and Computing; Vol. 329). Springer-Verlag. https://doi.org/10.1007/978-3-319-12286-1_5

Pan, Tien Szu ; Dao, Thi Kien ; Nguyen, Trong The et al. / Hybrid Particle Swarm Optimization with Bat Algorithm. Genetic and Evolutionary Computing : Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China. editor / Hui Sun ; Ching-Yu Yang ; Chun-Wei Lin ; Jeng-Shyang Pan ; Vaclav Snasel ; Ajith Abraham. Switzerland : Springer-Verlag, 2015. pp. 37-47 (Advances in Intelligent Systems and Computing).

@inproceedings{f6c9e5cb4e914291b204614679034a1a,

title = "Hybrid Particle Swarm Optimization with Bat Algorithm",

abstract = "In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.",

keywords = "Bat Algorithm Optimizations, Hybrid Particle Swarm Optimization with Bat Algorithm, Particle Swarm Optimization Algorithm, Swarm Intelligence",

author = "Pan, {Tien Szu} and Dao, {Thi Kien} and Nguyen, {Trong The} and Chu, {Shu Chuan}",

year = "2015",

doi = "10.1007/978-3-319-12286-1_5",

language = "English",

isbn = "9783319122854",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer-Verlag",

pages = "37--47",

editor = "Hui Sun and Ching-Yu Yang and Chun-Wei Lin and Jeng-Shyang Pan and Vaclav Snasel and Ajith Abraham",

booktitle = "Genetic and Evolutionary Computing",

note = "8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014 ; Conference date: 18-10-2014 Through 20-10-2014",

}

Pan, TS, Dao, TK, Nguyen, TT & Chu, SC 2015, Hybrid Particle Swarm Optimization with Bat Algorithm. in H Sun, C-Y Yang, C-W Lin, J-S Pan, V Snasel & A Abraham (eds), Genetic and Evolutionary Computing : Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China. Advances in Intelligent Systems and Computing, vol. 329, Springer-Verlag, Switzerland, pp. 37-47, 8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014, Nanchang, China, 18/10/14. https://doi.org/10.1007/978-3-319-12286-1_5

Hybrid Particle Swarm Optimization with Bat Algorithm. / Pan, Tien Szu; Dao, Thi Kien; Nguyen, Trong The et al.
Genetic and Evolutionary Computing : Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China. ed. / Hui Sun; Ching-Yu Yang; Chun-Wei Lin; Jeng-Shyang Pan; Vaclav Snasel; Ajith Abraham. Switzerland: Springer-Verlag, 2015. p. 37-47 (Advances in Intelligent Systems and Computing; Vol. 329).

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

TY - GEN

T1 - Hybrid Particle Swarm Optimization with Bat Algorithm

AU - Pan, Tien Szu

AU - Dao, Thi Kien

AU - Nguyen, Trong The

AU - Chu, Shu Chuan

N1 - Conference code: 8

PY - 2015

Y1 - 2015

N2 - In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

AB - In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

KW - Bat Algorithm Optimizations

KW - Hybrid Particle Swarm Optimization with Bat Algorithm

KW - Particle Swarm Optimization Algorithm

KW - Swarm Intelligence

UR - http://www.scopus.com/inward/record.url?scp=84910651523&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-12286-1_5

DO - 10.1007/978-3-319-12286-1_5

M3 - Conference contribution

AN - SCOPUS:84910651523

SN - 9783319122854

T3 - Advances in Intelligent Systems and Computing

SP - 37

EP - 47

BT - Genetic and Evolutionary Computing

A2 - Sun, Hui

A2 - Yang, Ching-Yu

A2 - Lin, Chun-Wei

A2 - Pan, Jeng-Shyang

A2 - Snasel, Vaclav

A2 - Abraham, Ajith

PB - Springer-Verlag

CY - Switzerland

T2 - 8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014

Y2 - 18 October 2014 through 20 October 2014

ER -

Pan TS, Dao TK, Nguyen TT, Chu SC. Hybrid Particle Swarm Optimization with Bat Algorithm. In Sun H, Yang CY, Lin CW, Pan JS, Snasel V, Abraham A, editors, Genetic and Evolutionary Computing : Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China. Switzerland: Springer-Verlag. 2015. p. 37-47. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-12286-1_5

Hybrid Particle Swarm Optimization with Bat Algorithm

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this