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

Abstract

Abstract: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.

A Decision Support Framework for National Crop Production Planning

“…This change affects the global fertilizer input, irrigation water requirements, and greenhouse gas emissions. In [21] a crop optimization model is presented to maximize the profits by optimizing transportation expenses. An improved differential evaluation algorithm is presented which finds the best optimal solution for the developed mathematical model.…”

“…The literature discussed in [36]- [43] provides a guideline for sustainable agriculture and through this research, we explore the quality impact of advanced crop allocation on the overall environment. A benchmark table is presented in Table . 1 in which we present contributions of main studies that we followed [21], [25], [33], [34], the limitation of each study and eventually summarize our research. The compared research provides gaps in the development of the crop allocation model, and in research specific to the study area which helps in the identification of the problem statement.…”

A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-Objective Reference Set Problems

“…Examples for the latter are subdivision [34][35][36] and cell mapping techniques [37][38][39]. Another class of set based methods is given by multi-objective evolutionary algorithms (MOEAs) that have proven to be very effective for the treatment of MOPs [14,16,[40][41][42][43]. Some reasons for this include that are very robust, do not require hard assumptions on the model, and allow to compute a reasonable finite size representation of the solution set already in a single run.…”

Modified Differential Evolution Algorithm for U-Shaped Assembly Line Balancing Type 2

“…The DE algorithm was first described by Storn and Price [23] in 1997. It has been used to solve a wide variety of problems and objectives in, for example, assignment problem to minimize cost [22], transportation problem to maximize profit and minimize cost of transport [24][25][26], location routing problem to minimize the fuel usage [27], simple assembly line balancing problem-type 1 (SALBP-1) to minimize number of workstation [28,29] and Ushaped assembly line balancing problem-type 1 (UALBP-1) to minimize number of workstation [13]. Ramadas, Abraham and Kumar [30] proposed a new revised mutation strategy in DE algorithm to improve the optimal solution.…”