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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Sasan Barak
  • Marziye Yousefi
  • Hamidreza Maghsoudlou
  • Sanaz Jahangiri
<mark>Journal publication date</mark>04/2016
<mark>Journal</mark>Stochastic Environmental Research and Risk Assessment
Issue number4
Number of pages21
Pages (from-to)1167–1187
Publication StatusPublished
Early online date12/06/15
<mark>Original language</mark>English


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.