Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution

Miao Zheng; Yoshikazu Fukuyama; Mohammed El-Abd; Tatsuya Iizaka; Tetsuro Matsui

doi:10.1109/CEC48606.2020.9185789

Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution

Miao Zheng
, 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

3 Scopus citations

Abstract

This paper proposes a method for the overall optimization of smart city (SC). The proposed method is based on multi-population global-best brain storm optimization using cooperative coevolution (MP-CCGBSO). Using a SC model, energy cost, actual power loads during peak periods, and carbon dioxide emission can be minimized. For the SC problem, many researchers have proposed various evolutionary algorithms including CCGBSO, which applied cooperative coevolution to GBSO. However, there is still room to improve quality of the solution by CCGBSO. Taking Toyama city of Japan as the research object, the calculation results of original CCGBSO method and the proposed MP-CCGBSO method of 2, 4, 8 and 16 populations are compared.

Original language	English
Title of host publication	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169293
DOIs	https://doi.org/10.1109/CEC48606.2020.9185789
State	Published - Jul 2020
Event	2020 IEEE Congress on Evolutionary Computation - Duration: 1 Jan 2020 → 1 Jan 2020

Publication series

Name	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference	2020 IEEE Congress on Evolutionary Computation
Period	1/01/20 → 1/01/20

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

cooperative coevolution
global-best brain storm optimization
Large scale mixed integer nonlinear optimization problem
multi-population
reduction of CO emission
smart city

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10.1109/CEC48606.2020.9185789

https://ieeexplore.ieee.org/document/9185789

Cite this

Zheng, M., Fukuyama, Y., El-Abd, M., Iizaka, T., & Matsui, T. (2020). Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings Article 9185789 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185789

Zheng, Miao ; Fukuyama, Yoshikazu ; El-Abd, Mohammed et al. / Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution. 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

@inproceedings{d1180d6b21c74666aa22ab7c24a7901d,

title = "Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution",

abstract = "This paper proposes a method for the overall optimization of smart city (SC). The proposed method is based on multi-population global-best brain storm optimization using cooperative coevolution (MP-CCGBSO). Using a SC model, energy cost, actual power loads during peak periods, and carbon dioxide emission can be minimized. For the SC problem, many researchers have proposed various evolutionary algorithms including CCGBSO, which applied cooperative coevolution to GBSO. However, there is still room to improve quality of the solution by CCGBSO. Taking Toyama city of Japan as the research object, the calculation results of original CCGBSO method and the proposed MP-CCGBSO method of 2, 4, 8 and 16 populations are compared.",

keywords = "cooperative coevolution, global-best brain storm optimization, Large scale mixed integer nonlinear optimization problem, multi-population, reduction of CO emission, smart city",

author = "Miao Zheng and Yoshikazu Fukuyama and Mohammed El-Abd and Tatsuya Iizaka and Tetsuro Matsui",

note = "Zheng M., Fukuyama Y., El-Abd M., Iizaka T., and Matsui T. (2020). Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution. IEEE Congress on Evolutionary Computation, 1-7.; 2020 IEEE Congress on Evolutionary Computation ; Conference date: 01-01-2020 Through 01-01-2020",

year = "2020",

month = jul,

doi = "10.1109/CEC48606.2020.9185789",

language = "English",

series = "2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings",

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

booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings",

address = "United States",

}

Zheng, M, Fukuyama, Y, El-Abd, M, Iizaka, T & Matsui, T 2020, Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution. in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings., 9185789, 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Congress on Evolutionary Computation, 1/01/20. https://doi.org/10.1109/CEC48606.2020.9185789

Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution. / Zheng, Miao; Fukuyama, Yoshikazu; El-Abd, Mohammed et al.
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9185789 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

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

TY - GEN

T1 - Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution

AU - Zheng, Miao

AU - Fukuyama, Yoshikazu

AU - El-Abd, Mohammed

AU - Iizaka, Tatsuya

AU - Matsui, Tetsuro

N1 - Zheng M., Fukuyama Y., El-Abd M., Iizaka T., and Matsui T. (2020). Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution. IEEE Congress on Evolutionary Computation, 1-7.

PY - 2020/7

Y1 - 2020/7

N2 - This paper proposes a method for the overall optimization of smart city (SC). The proposed method is based on multi-population global-best brain storm optimization using cooperative coevolution (MP-CCGBSO). Using a SC model, energy cost, actual power loads during peak periods, and carbon dioxide emission can be minimized. For the SC problem, many researchers have proposed various evolutionary algorithms including CCGBSO, which applied cooperative coevolution to GBSO. However, there is still room to improve quality of the solution by CCGBSO. Taking Toyama city of Japan as the research object, the calculation results of original CCGBSO method and the proposed MP-CCGBSO method of 2, 4, 8 and 16 populations are compared.

AB - This paper proposes a method for the overall optimization of smart city (SC). The proposed method is based on multi-population global-best brain storm optimization using cooperative coevolution (MP-CCGBSO). Using a SC model, energy cost, actual power loads during peak periods, and carbon dioxide emission can be minimized. For the SC problem, many researchers have proposed various evolutionary algorithms including CCGBSO, which applied cooperative coevolution to GBSO. However, there is still room to improve quality of the solution by CCGBSO. Taking Toyama city of Japan as the research object, the calculation results of original CCGBSO method and the proposed MP-CCGBSO method of 2, 4, 8 and 16 populations are compared.

KW - cooperative coevolution

KW - global-best brain storm optimization

KW - Large scale mixed integer nonlinear optimization problem

KW - multi-population

KW - reduction of CO emission

KW - smart city

UR - https://www.scopus.com/pages/publications/85092049782

U2 - 10.1109/CEC48606.2020.9185789

DO - 10.1109/CEC48606.2020.9185789

M3 - Conference contribution

T3 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

BT - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE Congress on Evolutionary Computation

Y2 - 1 January 2020 through 1 January 2020

ER -

Zheng M, Fukuyama Y, El-Abd M, Iizaka T, Matsui T. Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9185789. (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). doi: 10.1109/CEC48606.2020.9185789

Overall Optimization of Smart City by Multi-population Global-best Brain Storm Optimization using Cooperative Coevolution

Abstract

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UN SDGs

Keywords

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