An improved evolutionary algorithm for solving multi-objective crop planning models

Authors:
Ruhul Sarker;Tapabrata Ray
Affiliations:
School of Information Technology and Electrical Engineering, University of New South Wales at the Australian Defence Force Academy, Northcott Drive, Canberra 2600, Australia;School of Aerospace, Civil and Mechanical Engineering, University of New South Wales at the Australian Defence Force Academy, Northcott Drive, Canberra 2600, Australia
Venue:
Computers and Electronics in Agriculture
Year:
2009

Citing 4
Cited 0

Modelling a nationwide crop planning problem using a multiple criteria decision making tool

Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
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Interfaces
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

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Abstract

In this paper, we analyse multi-objective optimization problems and provide useful insights about solutions that are generated using population-based approaches. We formulate a crop-planning problem as a multi-objective optimization model and solve two different versions of the problem using three different optimization approaches. The approaches considered are: @?-constrained method, a well-known multi-objective evolutionary algorithm NSGAII and our proposed multi-objective constrained algorithm (MCA). We compare the performance of our algorithm with other two algorithms and analyse the solutions from decision-making point of view. Our algorithm delivers superior solutions to non-linear version of the crop-planning model.