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Correction published on 21 June 2019, see Algorithms 2019, 12(6), 125.
Article

Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration

1
Graduate School of Advanced Mathematical Sciences, Meiji University, Tokyo 164-8525, Japan
2
Fuji Electric CO., Ltd., Tokyo 141-0032, Japan
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(1), 15; https://doi.org/10.3390/a12010015
Received: 30 November 2018 / Revised: 25 December 2018 / Accepted: 30 December 2018 / Published: 7 January 2019
(This article belongs to the Special Issue Algorithms for Decision Making)
This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters. View Full-Text
Keywords: global-best modified brain storm optimization; smart city; multi-population evolutionary computation; reduction of CO2 emission; efficient utilization of energy global-best modified brain storm optimization; smart city; multi-population evolutionary computation; reduction of CO2 emission; efficient utilization of energy
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MDPI and ACS Style

Sato, M.; Fukuyama, Y.; Iizaka, T.; Matsui, T. Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration. Algorithms 2019, 12, 15. https://doi.org/10.3390/a12010015

AMA Style

Sato M, Fukuyama Y, Iizaka T, Matsui T. Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration. Algorithms. 2019; 12(1):15. https://doi.org/10.3390/a12010015

Chicago/Turabian Style

Sato, Mayuko, Yoshikazu Fukuyama, Tatsuya Iizaka, and Tetsuro Matsui. 2019. "Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration" Algorithms 12, no. 1: 15. https://doi.org/10.3390/a12010015

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