Estimation Using Differential Evolution for Optimal Crop Plan

Pant, Millie; Thangaraj, Radha; Rani, Deepti; Abraham, Ajith; Srivastava, D. K.

doi:10.1007/978-3-540-87656-4_36

Cited by 6 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, we examined an application of DE presented by Pant et al [50] to determine an optimal crop plan for the Pamba-Achankovil-Vaippar (PAV) link project area. Finally, the authors reviewed a study by Yi et al [51], who used three improved hybrid metaheuristic algorithms for engineering design optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Ketsripongsa

Pitakaso

Sethanan

et al. 2018

MCA

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

Ketsripongsa

Pitakaso

Sethanan

et al. 2018

MCA

View full text Add to dashboard Cite

show abstract

“…Development of multi-objective algorithms for optimal crop planning [15] and using DE for crop planning single and multi-objective optimization model [16]. For Pant, M. et al [17] presents an application of Differential Evolution (DE) to determine optimal crop plan for command area of Pamba-Achankovil-Vaippar (PAV) link project. And Yi H. et al [18] used three improved hybrid metaheuristic algorithms for engineering design optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Process; It was mixed species process which produced new species of better or worse result for selection of decision variables. The result was Trial Vector (Ui,G+1) Selection Process; It was the selection process for the best answer between Target Vector and Trial Vector using Formula(17) by compare Function Value or Cost Value of Trial Vector with Target Vector. In case of Function Value of Trial Vector was better than Target Vector would be replaced by Trial Vector in the next generation.…”

mentioning

confidence: 99%

An Improvement Differential Evolution Algorithms for the Crop Planning in the Northeastern Region of Thailand

Ketsripongsa¹,

Pitakaso²,

Sethanan³

et al. 2018

Preprint

View full text Add to dashboard Cite

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).

show abstract

“…Pant et al [3] employed the Differential Evolution (DE) algorithm to provide solutions to a crop planning problem under adequate, normal and limited irrigated water supply. The objective was to maximize the net benefits gained under these conditions.…”

Section: Journal Of Applied Mathematicsmentioning

confidence: 99%

Three New Stochastic Local Search Metaheuristics for the Annual Crop Planning Problem Based on a New Irrigation Scheme

Chetty

Adediran

2013

Journal of Applied Mathematics

View full text Add to dashboard Cite

Annual Crop Planning (ACP) is an NP-hard-type optimization problem in agricultural planning. It involves finding optimal solutions concerning the seasonal allocations of a limited amount of agricultural land amongst the various competing crops that are required to be grown on it. This study investigates the effectiveness of employing three new local search (LS) metaheuristic techniques in determining solutions to an ACP problem at a new Irrigation Scheme. These three new LS metaheuristic techniques are the Best Performance Algorithm (BPA), Iterative Best Performance Algorithm (IBPA), and the Largest Absolute Difference Algorithm (LADA). The solutions determined by these LS metaheuristic techniques are compared against the solutions of two other well-known LS metaheuristic techniques in the literature. These techniques are Tabu Search (TS) and Simulated Annealing (SA). The comparison with TS and SA was to determine the relative merits of the solutions found by BPA, IBPA, and LADA. The results show that TS performed as the overall best. However, LADA determined the best solution that was the most economically feasible.

show abstract

Estimation Using Differential Evolution for Optimal Crop Plan

Cited by 6 publications

References 4 publications

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

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

An Improvement Differential Evolution Algorithms for the Crop Planning in the Northeastern Region of Thailand

Three New Stochastic Local Search Metaheuristics for the Annual Crop Planning Problem Based on a New Irrigation Scheme

Contact Info

Product

Resources

About