Solving Crop Planning Problem by Differential Evolution Algorithm: A Case Study of Agriculture Economy in the Southern Region of Thailand
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Published:
Dec 31, 2018
Keywords:
Crop planning Differential Evolution Improved Differential Evolution Swap algorithm
Main Article Content
Abstract
This research presented solving multi-level crop planning problem of agriculture economy in the southern region of Thailand using algorithms based on Differential Evolution (DE) and Improved Differential Evolution (IDE) in order to find the maximum profit. The algorithm sets were comprised of four types: 1) Differential Evolution, 2) Differential Evolution with local search by adding the step of local search after the selection process which uses Swap algorithm (DE-S), 3) Random best of Differential Evolution by improved methods from the process of mutation (DE-R), 4) Random best of Differential Evolution with Swap algorithm (DE-SR). The results showed that in the small test instances, all algorithms were not different as all of them can find the optimal solution. In medium, large test instances and the real case, DE-SR presented the best solutions compared with the other proposed algorithms. The maximum profits of the real case are 13,823,443.2 Baht per day.
Article Details
How to Cite
Mayachearw, P., & Pijitbanjong, P. (2018). Solving Crop Planning Problem by Differential Evolution Algorithm: A Case Study of Agriculture Economy in the Southern Region of Thailand. Thai Industrial Engineering Network Journal, 4(2), 1–13. Retrieved from https://ph02.tci-thaijo.org/index.php/ienj/article/view/176674
Section
Research and Review Article
บทความ ข้อมูล เนื้อหา รูปภาพ ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารฯ ถือเป็นลิขสิทธิ์ของวารสารฯ หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใดๆ จะได้รับอนุญาต แต่ห้ามนำไปใช้เพื่่อประโยชน์ทางธุรกิจ และห้ามดัดแปลง
References
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[28] Chakraborty, U. K. and et al. “Differential evolution with local neighborhood”, New Jersey: IEEE Press, 2006.
[29] Yang, Z., He, J. and Yao, X. “Making a difference to differential evolution”, In Advances in Metaheuristic for Hard Optimization. Michalewicz, Z. and Siarry, P. Editors. p. 397-414. Berlin: Springer, 2008.
[30] Sethanan, K. and Pitakaso, R. “Differential evolution algorithms for scheduling raw milk transportation”, Computers and Electronics in Agriculture. 121(3): 245-259; February, 2016a.
[31] Sethanan, K. and Pitakaso, R. “Improved differential evolution algorithms for solving generalized assignment problem”, Expert Systems with Applications. 45(3): 450-459; March, 2016b.
[32] Qin, A. K. and et al. “Differential evolution algorithm with strategy adaptation for global numerical optimization”, IEEE Transactions on Evolutionary Computation. 13(2): 398–417; April, 2009.
[33] Diaz, J. A. and Fernandez, E. “A Tabu search heuristic for the generalized assignment problem”, European Journal of Operational Research. 132(1): 22–38; July, 2001.
[2] Storn, R. and Price, K. “Differential evolution a simple and efficient adaptive scheme for global optimization over continuous spaces”, Journal of Global Optimization. 11(4): 341-359; January, 1995.
[3] Qin, A. K. and Suganthan, P. N. “Self-adaptive differential evolution algorithm for numerical optimization”, In Proceedings of the 2005 IEEE Congress on Evolutionary Computation. p.1785–1791. Edinburgh, Scotland: IEEE Xplore, 2005.
[4] Zou, D. and et al. “An improved differential evolution algorithm for the task assignment problem”, Engineering Applications of Artificial Intelligence. 24(4): 616-624; June, 2011.
[5] Chakraborty, U. K. and et al. “Differential evolution with local neighborhood”, New Jersey: IEEE Press, 2006.
[6] Liu, J. and Lampinen, J. “A fuzzy adaptive differential evolution algorithm”, Soft Computing- A Fusion of Foundations, Methodologies, and Applications. 9(6): 448–462; June, 2005.
[7] Omran, M. and et al. “Self-adaptive differential evolution”, Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer-Verlag, 2005.
[8] Qin, A. K. and Suganthan, P. N. “Self-adaptive differential evolution algorithm for numerical optimization”, In Proceedings of the 2005 IEEE Congress on Evolutionary Computation. p.1785–1791. Edinburgh, Scotland: IEEE Xplore, 2005.
[9] Zou, D. and et al. “An improved differential evolution algorithm for the task assignment problem”, Engineering Applications of tificial Intelligence. 24(4): 616-624; June, 2011.
[10] ปรุฬห์ มะยะเฉี่ยว. การแก้ปัญหาการเลือกสถาน ที่ตั้งแบบหลายวัตถุประสงค์และหลายลาดับขั้นในโซ่อุปทาน: กรณีศึกษาโรงงานผลิตน้ามันปาล์มในเขตพัฒนาเฉพาะกิจจังหวัดชายแดนภาคใต้. วิทยานิพนธ์ปริญญาปรัชญาดุษฎี: มหาวิทยาลัยอุบลราชธานี, 2555.
[11] ทองพูน ทองดี และระพีพันธ์ ปิตาคะโส. “การแก้ปัญหาการเลือกสถานที่ตั้งและการมอบหมาย เส้นทางแบบหลายลาดับขั้น หลายต้นกาเนิดและหลายวัตถุประสงค์ด้วยกระบวนการโมดิฟายดิฟเฟอเรนเชียลอีโวลูชั่น”, วารสารวิจัยมหาวิทยาลัยขอนแก่น. 18(4): 662-672; ตุลาคม-ธันวาคม, 2556.
[12] Fan, Q. and Yan, X. “Self-adaptive differential evolution algorithm with discrete mutation control parameters”, Expert Systems with Applications. 42(3): 1551-1572; February, 2015.
[13] Jarmo, I. and et al. “Differential evolution training algorithm for feed-forward neural networks”, Neural Proceeding Letters. 17(1): 93-105; January, 2003.
[14] Lopez C. I. L. and et al. “Efficient Differential Evolution algorithms for multimodal optimal control problems”, Applied Soft Computing. 3(2): 97–122; September, 2003.
[15] Dervis K. and Selcuk, O. “A simple and global optimization algorithm for engineering problems: Differential evolution algorithm”, Turkish Journal of Electrical Engineering and Computer Sciences. 12(1): 53-60; January, 2004.
[16] Kukkonen, S. and Lampinen, J. “An extension of generalized differential evolution for multi-objective optimization with constraints”, In Proceeding of PPSN 2004, he 8th International conference on parallel problem solving from nature. p. 752-761. Birmingham: Springer, 2004.
[17] Babu, B. V. and Anbarasu, B. “Multi-Objective Differential Evolution (MODE): An Evolutionary Algorithm for Multi-Objective Optimization Problems (MOOPs)”, In Proceedings of the Third International Conference on Computational Intelligence, Robotics and Autonomous Systems. p. 210-217. Singapore: Orchard Hotel, 2005.
[18] Bin, Q. and et al. “Scheduling multi-objective job shop using a memetic algorithm based on differential evolution”, International Journal of Advanced Manufacturing and Technology. 35(9–10): 1014–1027; January, 2008.
[19] Erbao, C. and Mingyong, L. “A Hybrid differential evolution algorithm to vehicle routing problem with fuzzy demands”, Journal of Computational and Applied Mathematics. 231(1): 302-310; September, 2009.
[20] Liao, T. W. “Two hybrid differential evolution algorithms for engineering design optimization”, Applied Soft Computing. 10(4): 1188-1199; September, 2010.
[21] Josiah, A. and Fred, O. “Differential evolution algorithm for solving multi-objective crop planning model”, Journal of Agricultural Water Management. 97(6): 848-856; June, 2010.
[22] Das, S. and Suganthan, P. N. “Differential evolution a survey of the state-of-the-art”, IEEE Transactions on Evolutionary Computation. 15(1): 4-31; February, 2011.
[23] Sahin, C. and Kuvvetli, Y. “Differential evolution based meta-heuristic algorithm for dynamic continuous berth allocation problem”, Applied Mathematical Modelling. 40(23–24): 10679-10688; December, 2016.
[24] Su, Z. and et al. “Multiple emergency resource allocation for concurrent incidents in natural disasters”, International Journal of Disaster Risk Reduction. 17(1): 199-212; January, 2016.
[25] Jazebi, S. and et al. “DSTATCOM Allocation in Distribution Networks Considering Reconfiguration Using Differential Evolution Algorithm”, Energy Conversion and Management. 52(7): 2777-2783; July, 2011.
[26] Pitakaso, R. and Thongdee, T. “Solving a multi-objective, Source & Stage location- allocation Problem using Differential Evolution” In Proceeding of the Asoa Pacific Industrial Engineering & Management Systems Conference 2012: p.1053-1060. Ubon Ratchathani: Ubon Ratchathani University, 2012.
[27] Dechampai, D. and et al. “A differential evolution algorithm for the capacitated VRP with Flexibility for mixing pickup and delivery services and the maximum duration route in poultry industry”, Journal of Intelligent Manufacturing. 28(6): 1357-1376; August, 2017.
[28] Chakraborty, U. K. and et al. “Differential evolution with local neighborhood”, New Jersey: IEEE Press, 2006.
[29] Yang, Z., He, J. and Yao, X. “Making a difference to differential evolution”, In Advances in Metaheuristic for Hard Optimization. Michalewicz, Z. and Siarry, P. Editors. p. 397-414. Berlin: Springer, 2008.
[30] Sethanan, K. and Pitakaso, R. “Differential evolution algorithms for scheduling raw milk transportation”, Computers and Electronics in Agriculture. 121(3): 245-259; February, 2016a.
[31] Sethanan, K. and Pitakaso, R. “Improved differential evolution algorithms for solving generalized assignment problem”, Expert Systems with Applications. 45(3): 450-459; March, 2016b.
[32] Qin, A. K. and et al. “Differential evolution algorithm with strategy adaptation for global numerical optimization”, IEEE Transactions on Evolutionary Computation. 13(2): 398–417; April, 2009.
[33] Diaz, J. A. and Fernandez, E. “A Tabu search heuristic for the generalized assignment problem”, European Journal of Operational Research. 132(1): 22–38; July, 2001.