Proceedings of the 5th International Conference on Genetic Algorithms
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A self-adaptive differential evolution algorithm with constraint sequencing
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
| Hi-index | 0.00 |
Existing optimization approaches adopt a full evaluation policy, i.e. all the constraints corresponding to a solution are evaluated throughout the course of search. Furthermore, a common sequence of constraint evaluation is used for all the solutions. In this paper, we introduce a scheme of constraint handling, wherein every solution is assigned a random sequence of constraints and the evaluation process is aborted whenever a constraint is violated. The solutions are sorted based on two measures i.e. the number of satisfied constraints and the violation measure. The number of satisfied constraints takes a precedence over the amount of violation. We illustrate the performance of the proposed scheme and compare it with other state-of-the-art constraint handling methods within a framework of differential evolution. The results are compared using gseries test functions for inequality constraints. The results clearly highlight the potential savings offered by the proposed method