Differential evolution algorithm for crop planning: Single and multi-objective optimization model
TL;DR: It is concluded that this methodology can be used to generate better results than using a multi-objective model only and it is suggested that each objective can be solved separately to get better solutions than the ones generated by multi- objective models using the same procedure with suitable modifications.
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Abstract: Single objective optimization for maximizing total net benefit from farming is presented in this study. Differential evolution algorithm which is a family of evolutionary algorithm for fast optimization is employed for the model. The single objective optimization is used to find a better solution using the results of multi-objective optimization of crop planning where three objectives are considered. The objectives are to maximize both total net benefit and agricultural output while minimizing the total irrigation water used. The methodology adopted in this study is used to assist in choosing a solution when many non dominated solutions are presented by a multi-objective optimization. The other two objectives are used as constraints of the problem while maximizing the total net benefit only. The ten strategies of differential evolution are tested with this model. DE/rand/1/bin generated a maximum total net benefit of ZAR 1,330,000 after 1,207 iterations from a planting area of 771,000 m 2 using 704694 m 3 of irrigation water while multi-objective differential evolution algorithm (MDEA1) generated the total net benefit of ZAR 1,304,600. It is concluded that this methodology can be used to generate better results than using a multi-objective model only. It is also suggested that each objective can be solved separately to get better solutions than the ones generated by multi-objective models using the same procedure with suitable modifications.
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Citations
Comparison Study of Swarm Intelligence Techniques for the Annual Crop Planning Problem
TL;DR: This study investigates the effectiveness of employing three relatively new swarm intelligence (SI) metaheuristic techniques in determining the solutions to the ACP problem with case study from an existing irrigation scheme, showing that each of the three SI algorithms provides superior solutions for the case studied.
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Nature Inspired Metaheuristics and Their Applications in Agriculture: A Short Review
Jorge Mendes,Paulo Moura Oliveira,Filipe Neves dos Santos,Raul Morais dos Santos +3 more
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TL;DR: Among the most well-established nature inspired metaheuristics the ones selected to be addressed in this work are the following: genetic algorithms, differential evolution, simulated annealing, harmony search, particle swarm optimization, ant colony optimization, firefly algorithm and bat algorithm.
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Crop planning optimization research — A detailed investigation
S. Saranya,T. Amudha +1 more
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TL;DR: This paper presents a general idea of Crop Planning and various algorithms which are used to solve this problem and also address the problems that need effective solutions.
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Optimum irrigation water allocation and crop distribution using combined Pareto multi-objective differential evolution
TL;DR: CPMDE is a good and robust alternative algorithm suitable for resolving crop distribution underlimited water availability and shows that maize produced the highest crop yield under limited water allocation in the study area.
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•Journal Article
Multiobjective optimization of crop-mix planning using generalized differential evolution algorithm
TL;DR: The empirical findings of this study indicated that generalized differential evolution 3 algorithm is a feasible optimization tool for solving optimal mixed-cropping planning problems.
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Rainer Storn,Kenneth Price +1 more
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DEMO: differential evolution for multiobjective optimization
Tea Robič,Bogdan Filipič +1 more
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Multiobjective optimization using a Pareto differential evolution approach
N.K. Madavan
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TL;DR: The differential evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach and performs well when applied to several test optimization problems from the literature.