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Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study

Citation

Barak, S and Yousefi, M and Maghsoudlou, H and Jahangiri, S, Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study, Stochastic Environmental Research and Risk Assessment, 30, (4) pp. 1167-1187. ISSN 1436-3240 (2016) [Refereed Article]

Copyright Statement

Copyright 2015 Springer-Verlag Berlin Heidelberg

DOI: doi:10.1007/s00477-015-1098-1

Abstract

In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.

Item Details

Item Type:Refereed Article
Keywords:agricultural management, energy, GHG, emissions, MOPSO, NRGA-II, NSGA, optimization
Research Division:Engineering
Research Group:Environmental Engineering
Research Field:Environmental Engineering Modelling
Objective Division:Energy
Objective Group:Environmentally Sustainable Energy Activities
Objective Field:Environmentally Sustainable Energy Activities not elsewhere classified
UTAS Author:Jahangiri, S (Ms Sanaz Jahangiri)
ID Code:128566
Year Published:2016
Web of Science® Times Cited:6
Deposited By:Maritime and Logistics Management
Deposited On:2018-09-30
Last Modified:2018-11-12
Downloads:0

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