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

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: ɛ-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.