Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization

Joon Woo Lee; Taeyong Choi; Hyunmin Do; Dongil Park; Chanhun Park; Young Su Son

doi:10.1109/CEC.2015.7257128

Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization

Joon Woo Lee, Taeyong Choi, Hyunmin Do, Dongil Park, Chanhun Park, Young Su Son

Department of Electrical Engineering

Korea Institute of Machinery and Materials

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

4 Scopus citations

Abstract

Many optimization problems in recent engineering are complex and high-dimensional problems, a so-called Large-Scale Global Optimization (LSGO) problem, due to the increasing requirements for multidisciplinary approach. This paper proposes a novel Bare Bones Particle Swarm Optimization (BBPSO) algorithm to solve LSGO problems. The BBPSO is a variant of a Particle Swarm Optimization (PSO) and is based on Gaussian distribution. The BBPSO does not consider the selection of controllable parameters of the PSO and is a simple but powerful optimizer. This algorithm, however, is vulnerable to LSGO problems. This study has improved its performance for LSGO problems by combining the heterogeneous cooperation based on the information exchange between particles and the Gaussian jump strategy to avoid local optima. The CEC'2015 Special Session on Large-Scale Global Optimization has given 15 benchmark problems to provide convenience and flexibility for comparing various optimization algorithms specifically designed for large-scale global optimization. Simulations performed with those benchmark problems have verified the performance of the proposed optimizer and compared with the reference algorithm DECC-G of the CEC'2015 special session on LSGO.

Original language	English
Title of host publication	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1979-1985
Number of pages	7
ISBN (Electronic)	9781479974924
DOIs	https://doi.org/10.1109/CEC.2015.7257128
State	Published - 10 Sep 2015
Event	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 25 May 2015 → 28 May 2015

Publication series

Name	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Conference

Conference	IEEE Congress on Evolutionary Computation, CEC 2015
Country/Territory	Japan
City	Sendai
Period	25/05/15 → 28/05/15

Access to Document

10.1109/CEC.2015.7257128

Cite this

Lee, J. W., Choi, T., Do, H., Park, D., Park, C., & Son, Y. S. (2015). Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 1979-1985). Article 7257128 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2015.7257128

Lee, Joon Woo ; Choi, Taeyong ; Do, Hyunmin et al. / Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization. 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1979-1985 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

@inproceedings{becc34ed8bdf44068f658ff26f9706e3,

title = "Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization",

abstract = "Many optimization problems in recent engineering are complex and high-dimensional problems, a so-called Large-Scale Global Optimization (LSGO) problem, due to the increasing requirements for multidisciplinary approach. This paper proposes a novel Bare Bones Particle Swarm Optimization (BBPSO) algorithm to solve LSGO problems. The BBPSO is a variant of a Particle Swarm Optimization (PSO) and is based on Gaussian distribution. The BBPSO does not consider the selection of controllable parameters of the PSO and is a simple but powerful optimizer. This algorithm, however, is vulnerable to LSGO problems. This study has improved its performance for LSGO problems by combining the heterogeneous cooperation based on the information exchange between particles and the Gaussian jump strategy to avoid local optima. The CEC'2015 Special Session on Large-Scale Global Optimization has given 15 benchmark problems to provide convenience and flexibility for comparing various optimization algorithms specifically designed for large-scale global optimization. Simulations performed with those benchmark problems have verified the performance of the proposed optimizer and compared with the reference algorithm DECC-G of the CEC'2015 special session on LSGO.",

author = "Lee, {Joon Woo} and Taeyong Choi and Hyunmin Do and Dongil Park and Chanhun Park and Son, {Young Su}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Congress on Evolutionary Computation, CEC 2015 ; Conference date: 25-05-2015 Through 28-05-2015",

year = "2015",

month = sep,

day = "10",

doi = "10.1109/CEC.2015.7257128",

language = "English",

series = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1979--1985",

booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

address = "United States",

}

Lee, JW, Choi, T, Do, H, Park, D, Park, C & Son, YS 2015, Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7257128, 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1979-1985, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 25/05/15. https://doi.org/10.1109/CEC.2015.7257128

Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization. / Lee, Joon Woo; Choi, Taeyong; Do, Hyunmin et al.
2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1979-1985 7257128 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

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

TY - GEN

T1 - Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization

AU - Lee, Joon Woo

AU - Choi, Taeyong

AU - Do, Hyunmin

AU - Park, Dongil

AU - Park, Chanhun

AU - Son, Young Su

PY - 2015/9/10

Y1 - 2015/9/10

N2 - Many optimization problems in recent engineering are complex and high-dimensional problems, a so-called Large-Scale Global Optimization (LSGO) problem, due to the increasing requirements for multidisciplinary approach. This paper proposes a novel Bare Bones Particle Swarm Optimization (BBPSO) algorithm to solve LSGO problems. The BBPSO is a variant of a Particle Swarm Optimization (PSO) and is based on Gaussian distribution. The BBPSO does not consider the selection of controllable parameters of the PSO and is a simple but powerful optimizer. This algorithm, however, is vulnerable to LSGO problems. This study has improved its performance for LSGO problems by combining the heterogeneous cooperation based on the information exchange between particles and the Gaussian jump strategy to avoid local optima. The CEC'2015 Special Session on Large-Scale Global Optimization has given 15 benchmark problems to provide convenience and flexibility for comparing various optimization algorithms specifically designed for large-scale global optimization. Simulations performed with those benchmark problems have verified the performance of the proposed optimizer and compared with the reference algorithm DECC-G of the CEC'2015 special session on LSGO.

AB - Many optimization problems in recent engineering are complex and high-dimensional problems, a so-called Large-Scale Global Optimization (LSGO) problem, due to the increasing requirements for multidisciplinary approach. This paper proposes a novel Bare Bones Particle Swarm Optimization (BBPSO) algorithm to solve LSGO problems. The BBPSO is a variant of a Particle Swarm Optimization (PSO) and is based on Gaussian distribution. The BBPSO does not consider the selection of controllable parameters of the PSO and is a simple but powerful optimizer. This algorithm, however, is vulnerable to LSGO problems. This study has improved its performance for LSGO problems by combining the heterogeneous cooperation based on the information exchange between particles and the Gaussian jump strategy to avoid local optima. The CEC'2015 Special Session on Large-Scale Global Optimization has given 15 benchmark problems to provide convenience and flexibility for comparing various optimization algorithms specifically designed for large-scale global optimization. Simulations performed with those benchmark problems have verified the performance of the proposed optimizer and compared with the reference algorithm DECC-G of the CEC'2015 special session on LSGO.

UR - http://www.scopus.com/inward/record.url?scp=84963571333&partnerID=8YFLogxK

U2 - 10.1109/CEC.2015.7257128

DO - 10.1109/CEC.2015.7257128

M3 - Conference contribution

AN - SCOPUS:84963571333

T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

SP - 1979

EP - 1985

BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Congress on Evolutionary Computation, CEC 2015

Y2 - 25 May 2015 through 28 May 2015

ER -

Lee JW, Choi T, Do H, Park D, Park C, Son YS. Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1979-1985. 7257128. (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). doi: 10.1109/CEC.2015.7257128

Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for Large Scale Global Optimization

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this