Optimization of Land Leveling Operations through Least Square Method and Its Comparison with the Genetic Algorithm and Particle Swarm Optimization Algorithm

Document Type : Research Paper

Authors

1 Ph. D. Student, Deptartment of Mechanizasion, College of agriculture and natural resources, Tehran University

2 Professor, Deptartment of Mechanizasion, College of agriculture and natural resources, Tehran University

3 Associate Professor, Deptartment of Mechanizasion, College of agriculture and natural resources, Tehran University

4 Assistant Professor, Deptartment of Mechanizasion, College of agriculture and natural resources, Tehran University

Abstract

For a uniform distribution of water, decrease in water waste and decrease in erosion of soil, it is important that a land be prepared with proper slopes along its length as well as width. The aim of leveling is to create appropriate slopes for irrigation and drainage on the lands that were not already properly levelled and of the same time creating the level surface with a minimum transport of soil. Throughout the present study, characteristics of a level plane of an agricultural land are modeled by programming algorithm with the results being compared with Minimum Least Square method. Statistical and descriptive results show that Genetic Algorithm and Particle Swarm Optimization algorithm benefit from more accuracies than Minimum Least Square. Also, practice of such restrictions as maximum depth of excavation is easy to be applied in this method. In addition, using Genetic Algorithm method decreased the volume of excavation by 20% and 17.5%. Another method, called Particle Swarm Optimization, was also applied with the results indicating that the volume of the soil cut and fill for Particle Swarm Optimization method was recorded as less than that in Genetic Algorithm method.

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References

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Iranian Journal of Biosystems Engineering
Volume 45, Issue 2 - Serial Number 2
Autumn & Winter
October 2014
Pages 105-112

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History

  • Receive Date: 04 November 2013
  • Revise Date: 10 October 2014
  • Accept Date: 19 October 2014

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