Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization

Mayuko Sato; Yoshikazu Fukuyama; Mohammed El-Abd; Tatsuya Iizaka; Tetsuro Matsui

doi:10.1109/CEC.2019.8790288

Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization

Mayuko Sato, Yoshikazu Fukuyama, Mohammed El-Abd, Tatsuya Iizaka, Tetsuro Matsui

Electrical and Computer Engineering

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

4 Scopus citations

Abstract

This paper proposes total optimization of energy networks in a smart city (SC) by cooperative coevolution using global-best brain storm optimization (CCGBSO). The smart city problem is one of mixed integer nonlinear programming (MINLP) problems. Therefore, various evolutionary computation methods such as differential evolutionary particle swarm optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (GBSO) have been applied to the problem. However, quality of solution is still required to be improved. Cooperative Cooperation has a possibility to improve solution quality of large scale optimization problems such as the SC problem and this paper proposes a new cooperative coevolution algorithm, CCGBSO. The results of the proposed CCGBSO based method are verified to be the most improved comparing with those of the conventional DEEPSO, BSO, MBSO, and GBSO based methods.

Original language	English
Title of host publication	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	681-688
Number of pages	8
ISBN (Electronic)	9781728121536
DOIs	https://doi.org/10.1109/CEC.2019.8790288
State	Published - Jun 2019
Event	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019

Publication series

Name	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference	2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/Territory	New Zealand
City	Wellington
Period	10/06/19 → 13/06/19

Keywords

cooperative coevolution
cooperative coevolution global-best brain storm optimization
global-best brain storm optimization
reduction of CO emission
smart city

UN SDGs

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

Access to Document

10.1109/CEC.2019.8790288

Cite this

Sato, M., Fukuyama, Y., El-Abd, M., Iizaka, T., & Matsui, T. (2019). Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 681-688). Article 8790288 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8790288

Sato, Mayuko ; Fukuyama, Yoshikazu ; El-Abd, Mohammed et al. / Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 681-688 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

@inproceedings{2feca8a319714f76b41b7e70a32b0080,

title = "Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization",

abstract = "This paper proposes total optimization of energy networks in a smart city (SC) by cooperative coevolution using global-best brain storm optimization (CCGBSO). The smart city problem is one of mixed integer nonlinear programming (MINLP) problems. Therefore, various evolutionary computation methods such as differential evolutionary particle swarm optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (GBSO) have been applied to the problem. However, quality of solution is still required to be improved. Cooperative Cooperation has a possibility to improve solution quality of large scale optimization problems such as the SC problem and this paper proposes a new cooperative coevolution algorithm, CCGBSO. The results of the proposed CCGBSO based method are verified to be the most improved comparing with those of the conventional DEEPSO, BSO, MBSO, and GBSO based methods.",

keywords = "cooperative coevolution, cooperative coevolution global-best brain storm optimization, global-best brain storm optimization, reduction of CO emission, smart city",

author = "Mayuko Sato and Yoshikazu Fukuyama and Mohammed El-Abd and Tatsuya Iizaka and Tetsuro Matsui",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Congress on Evolutionary Computation, CEC 2019 ; Conference date: 10-06-2019 Through 13-06-2019",

year = "2019",

month = jun,

doi = "10.1109/CEC.2019.8790288",

language = "English",

series = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",

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

pages = "681--688",

booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",

address = "United States",

}

Sato, M, Fukuyama, Y, El-Abd, M, Iizaka, T & Matsui, T 2019, Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings., 8790288, 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 681-688, 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 10/06/19. https://doi.org/10.1109/CEC.2019.8790288

Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization. / Sato, Mayuko; Fukuyama, Yoshikazu; El-Abd, Mohammed et al.
2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 681-688 8790288 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

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

TY - GEN

T1 - Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization

AU - Sato, Mayuko

AU - Fukuyama, Yoshikazu

AU - El-Abd, Mohammed

AU - Iizaka, Tatsuya

AU - Matsui, Tetsuro

PY - 2019/6

Y1 - 2019/6

N2 - This paper proposes total optimization of energy networks in a smart city (SC) by cooperative coevolution using global-best brain storm optimization (CCGBSO). The smart city problem is one of mixed integer nonlinear programming (MINLP) problems. Therefore, various evolutionary computation methods such as differential evolutionary particle swarm optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (GBSO) have been applied to the problem. However, quality of solution is still required to be improved. Cooperative Cooperation has a possibility to improve solution quality of large scale optimization problems such as the SC problem and this paper proposes a new cooperative coevolution algorithm, CCGBSO. The results of the proposed CCGBSO based method are verified to be the most improved comparing with those of the conventional DEEPSO, BSO, MBSO, and GBSO based methods.

AB - This paper proposes total optimization of energy networks in a smart city (SC) by cooperative coevolution using global-best brain storm optimization (CCGBSO). The smart city problem is one of mixed integer nonlinear programming (MINLP) problems. Therefore, various evolutionary computation methods such as differential evolutionary particle swarm optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (GBSO) have been applied to the problem. However, quality of solution is still required to be improved. Cooperative Cooperation has a possibility to improve solution quality of large scale optimization problems such as the SC problem and this paper proposes a new cooperative coevolution algorithm, CCGBSO. The results of the proposed CCGBSO based method are verified to be the most improved comparing with those of the conventional DEEPSO, BSO, MBSO, and GBSO based methods.

KW - cooperative coevolution

KW - cooperative coevolution global-best brain storm optimization

KW - global-best brain storm optimization

KW - reduction of CO emission

KW - smart city

UR - http://www.scopus.com/inward/record.url?scp=85071339766&partnerID=8YFLogxK

U2 - 10.1109/CEC.2019.8790288

DO - 10.1109/CEC.2019.8790288

M3 - Conference contribution

AN - SCOPUS:85071339766

T3 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

SP - 681

EP - 688

BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019

Y2 - 10 June 2019 through 13 June 2019

ER -

Sato M, Fukuyama Y, El-Abd M, Iizaka T, Matsui T. Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 681-688. 8790288. (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). doi: 10.1109/CEC.2019.8790288

Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this