A Dynamical Simplified Swarm Optimization Algorithm for the Multiobjective Annual Crop Planning Problem Conserving Groundwater for Sustainability

Chun-Cheng Lin*, Der Jiunn Deng, Jia Rong Kang, Wan Yu Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In large-scale agriculture, insufficient irrigation water may lead to overpumping of groundwater, increasing the risk of land subsidence. Growing dryland crops can effectively decrease the demand for irrigation water. However, the previous works on annual crop planning (ACP) focused on maximizing the profit through growing wetland crops and consuming much water. For sustainability, in this article, we propose a mathematical programming model for an ACP that allocates a land area for growing dryland and wetland crops to maximize the total profit and minimize the total irrigation water used for multiple cropping, under practical constraints. The simplified swarm optimization (SSO) improves the particle swarm optimization with four probabilities to determine the operations of updating solutions. We further propose dynamic SSO (DSSO) to solve the concerned ACP in which the four probabilities are adjusted dynamically according to the performance of the operations executed. Through simulation on a case study, the proposed DSSO demonstrates high performance over some classical approaches.

Original languageEnglish
Article number9216011
Pages (from-to)4401-4410
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

  • Crop planning
  • irrigation water
  • land subsidence
  • simplified swarm optimization (SSO)
  • sustainability
  • swarm intelligence (SI)

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