Search space reduction technique for constrained optimization with tiny feasible space
Proceedings of the 10th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
In this paper, we compare two multi-objective evolutionary algorithms by solving bi-objective linear and nonlinear constrained optimization problems. The problems considered are three instances of a realistic crop planning problem. The multiobjective algorithms compared are a well-known multi-objective evolutionary algorithm NSGAII and our own algorithm MCA. We discuss the solutions obtained and analyse the sensitivity of variables for multiobjective solutions. From our analysis, it can be concluded that there is still room for improvement in the performance of the evolutionary optimization algorithms for some of these optimization problems.