| Title |
Total Optimization of Smart City by Modified Brain Storm Optimization |
| ID_Doc |
38303 |
| Authors |
Sato, M; Fukuyama, Y |
| Title |
Total Optimization of Smart City by Modified Brain Storm Optimization |
| Year |
2018 |
| Published |
Ifac Papersonline, 51, 28 |
| DOI |
10.1016/j.ifacol.2018.11.670 |
| Abstract |
This paper proposes a total optimization method of a smart city (SC) by Modified brain storm optimization (MBSO). The method utilizes a SC model. The model includes natural gas utilities, electric power utilities, drinking and waste water treatment plants, industries, buildings, residences, and railroads. It minimizes energy cost, shifts actual electric power loads, and minimizes CO2 emission using the model. Particle Swarm Optimization (PSO), Differential Evolution (DE), and Differential evolutionary particle swarm optimization (DEEP SO) have been applied to the optimization problem. However, there is room for improving solution quality. The proposed MBSO based method is applied to a model which considers a moderately-sized city in Japan, such as Toyama city. The proposed method is compared with the conventional DEEPSO and BSO based methods with promising results. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. |
| Author Keywords |
Optimization problem; Environment engineering; Computational methods; Heuristic searches; Large-scale systems |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000453038500004 |
| WoS Category |
Automation & Control Systems |
| Research Area |
Automation & Control Systems |
| PDF |
https://doi.org/10.1016/j.ifacol.2018.11.670
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