Authors:
Mayuko Sato
1
;
Yoshikazu Fukuyama
1
;
Tatsuya Iizaka
2
and
Tetsuro Matsui
2
Affiliations:
1
Graduate School of Advanced Mathematical Sciences, Meiji University, 4-21-1, Nakano, Nakano-ku, Tokyo, Japan
;
2
Fuji Electric, Co. Ltd., No.1, Fuji-machi, Hino, Tokyo, Japan
Keyword(s):
Smart City, CO2 Emission Reduction, Large-Scale Mixed-Integer Nonlinear Optimization Problem, Evolutionary Computation, Brain Storm Optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
This paper proposes a total optimization method of a smart city (SC) by Global-best Modified Brain Storm
Optimization (GMBSO). Almost all countries have a goal to reduce CO2 emission as the countermeasures of
global warming. In addition, these countries have conducted SC demonstration projects. The problem of the
paper considers CO2 emission, energy cost, and electric power load at peak load hours. In order to solve the
problem, Differential Evolutionary Particle Swarm Optimization (DEEPSO), Modified Brain Storm
Optimization (MBSO), and Global-best Brain Storm Optimization (GBSO) have been applied to the problem.
This paper proposes a novel evolutionary computation method, called Global-best Modified Brain Storm
Optimization (GMBSO), which is a combined method of GBSO and MBSO in order to obtain better results.
The total optimization of SC is solved by the proposed GMBSO based method. The results by the proposed
GMBSO based method is compared with those by conventional DEEPS
O, BSO, only GBSO, and only MBSO
based methods
(More)