Multi-objective constrained algorithm (MCA) and non-dominated sorting genetic algorithm (NSGA-ii) for solving multi-objective crop planning problem
Crop planning problem is a multi-objective optimization problem. It is related to many factors such as land type, capital, demand etc. From very earlier years, people have been trying to find out a best solution for crop planning to get more profit in exchange of less investment and cost. In this p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Asian Research Publishing Network
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/56519/ http://irep.iium.edu.my/56519/ http://irep.iium.edu.my/56519/1/56519_Multi-objective%20constrained%20algorithm%20%28MCA%29_Scopus.pdf http://irep.iium.edu.my/56519/2/56519_Multi-objective%20constrained%20algorithm%20%28MCA%29.pdf |
| Summary: | Crop planning problem is a multi-objective optimization problem. It is related to many factors such as land type,
capital, demand etc. From very earlier years, people have been trying to find out a best solution for crop planning to get more profit in exchange of less investment and cost. In this paper, we formulate a crop planning problem as a multiobjective optimization model and try to solve two different versions of the problem using two different optimization algorithms MCA and NSGA. In this two algorithms, they provide superior solutions to maximize total net benefit and minimize total cost. We investigate these algorithms here as a linear crop planning model and use them to acquire the maximum total gross margin according with minimum total working capital in order to satisfy some constraints. We also compare the performance of these two algorithms and analyse the solution from the decision-making point of view. |
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