Version 1
: Received: 6 July 2018 / Approved: 6 July 2018 / Online: 6 July 2018 (14:03:42 CEST)
How to cite:
Ketsripongsa, U.; Pitakaso, R.; Sethanan, K.; Srivarapongse, T. An Improvement Differential Evolution Algorithms for the Crop Planning in the Northeastern Region of Thailand. Preprints2018, 2018070117. https://doi.org/10.20944/preprints201807.0117.v1
Ketsripongsa, U.; Pitakaso, R.; Sethanan, K.; Srivarapongse, T. An Improvement Differential Evolution Algorithms for the Crop Planning in the Northeastern Region of Thailand. Preprints 2018, 2018070117. https://doi.org/10.20944/preprints201807.0117.v1
Ketsripongsa, U.; Pitakaso, R.; Sethanan, K.; Srivarapongse, T. An Improvement Differential Evolution Algorithms for the Crop Planning in the Northeastern Region of Thailand. Preprints2018, 2018070117. https://doi.org/10.20944/preprints201807.0117.v1
APA Style
Ketsripongsa, U., Pitakaso, R., Sethanan, K., & Srivarapongse, T. (2018). An Improvement Differential Evolution Algorithms for the Crop Planning in the Northeastern Region of Thailand. Preprints. https://doi.org/10.20944/preprints201807.0117.v1
Chicago/Turabian Style
Ketsripongsa, U., Kanchana Sethanan and Tassin Srivarapongse. 2018 "An Improvement Differential Evolution Algorithms for the Crop Planning in the Northeastern Region of Thailand" Preprints. https://doi.org/10.20944/preprints201807.0117.v1
Abstract
This research presents a solution to the problem of planning the optimum area for economic crops by developed mathematical models and developed an algorithm to solve the problem of planning the optimum area by considered economic value for the maximize profit of farmers. The data were collected from farmers in 8 provinces in the northeastern region of Thailand. The 3 economic crops studied were rice, cassava and sugarcane. The solving problem methods were 1) Created mathematical models and solved the problems with Lingo V.11. 2) Improved Differential Evolution algorithms (I-DE) to solve the problems, which had 3 local search methods included (Swap, Cyclic Move and K-variable moves). The results of this study showed that in the small and medium problems instances, Lingo V.11 and DE provided equal profit outcome but DE was faster but in the large size of test instances DE generated better solution than that of Lingo v.11 when Lingo simulation time is set to 250 hours and DE simulation time has set to maximum 21.82 minutes. 2) Comparing DE and I-DE , I-DE outperforms DE in finding the better solution for all size of test instances (small, medium and large).
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
