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 - http://www.scopus.com/inward/record.url?scp=85092049782&partnerID=8YFLogxK
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 -
