State-of-the art constrained multiobjective optimisation methods are often explored and demonstrated with the help of function optimisation problems from these accounts. It is sometimes hard for practitioners to extract good approaches for practical problems. In this paper we apply an evolutionary algorithm to a factual problem with realistic constraints and compare the effects of different operators and constraint handling methods. We observe that in spite of an apparently very insular search space, we consistently obtain the best results when using a repair mechanism, effectively eliminating infeasible solutions. This runs contrary to some recommendations in the optimisation literature which propose penalty functions for search spaces where feasible solutions are sparse.
Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010), held as part of the 2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010), Barcelona, Spain, 18-23 July 2010,