| Title |
Overall Optimization Of Smart City By Multi-Population Global-Best Brain Storm Optimization Using Cooperative Coevolution |
| ID_Doc |
41100 |
| Authors |
Zheng M.; Fukuyama Y.; El-Abd M.; Iizaka T.; Matsui T. |
| Year |
2020 |
| Published |
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings |
| DOI |
http://dx.doi.org/10.1109/CEC48606.2020.9185789 |
| 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. © 2020 IEEE. |
| Author Keywords |
cooperative coevolution; global-best brain storm optimization; Large scale mixed integer nonlinear optimization problem; multi-population; reduction of CO emission; smart city |
