An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Mathematical and Computational Applications

Srivarapongse, Tassin¹  Sethanan, Kanchana²  Pitakaso, Rapeepan³  Ketsripongsa, Udompong⁴ 

[1] Author to whom correspondence should be addressed.;Department of Economics, Faculty of Business Administration, Rajamangala University of Technology Thanyaburi, Patumthani 10900, Thailand;Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40000, Thailand;Metaheuristics for Logistics Optimization Laboratory, Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand

关键词: differential evolution algorithm;    crop planning;    economic crops;    improvement differential evolution algorithm;   

DOI  :  10.3390/mca23030040

学科分类：计算数学

来源: mdpi

【 摘 要 】

This research aimed to solve the economic crop planning problem, considering transportation logistics to maximize the profit from cultivated activities. Income is derived from the selling price and production rate of the plants; costs are due to operating and transportation expenses. Two solving methods are presented: (1) developing a mathematical model and solving it using Lingo v.11, and (2) using three improved Differential Evolution (DE) Algorithms—I-DE-SW, I-DE-CY, and I-DE-KV—which are DE with swap, cyclic moves (CY), and K-variables moves (KV) respectively. The algorithms were tested by 16 test instances, including this case study. The computational results showed that Lingo v.11 and all DE algorithms can find the optimal solution eight out of 16 times. Regarding the remaining test instances, Lingo v.11 was unable to find the optimal solution within 400 h. The results for the DE algorithms were compared with the best solution generated within that time. The DE solutions were 1.196–1.488% better than the best solution generated by Lingo v.11 and used 200 times less computational time. Comparing the three DE algorithms, MDE-KV was the DE that was the most flexible, with the biggest neighborhood structure, and outperformed the other DE algorithms.

【 授权许可】

CC BY