A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms

A new rank-based CHT is presented for the solution of engineering optimization problems.Builds multiple separate queues based on objective function and violation of each constraint.Overcomes difficulties for constraints with different orders of magnitude and different units.Easy to implement; without user-defined parameters; may be associated with any evolutionary algorithm.Benchmark studies compare its performance with other well-known CHTs.Results demonstrate its increased accuracy and robustness. This work presents a constraint handling technique (CHT) for the solution of real-world engineering optimization problems by evolutionary algorithms. Referred to as the Multiple Constraint Ranking (MCR), it extends the rank-based approach from many CHTs, by building multiple separate queues based on the values of the objective function and the violation of each constraint. This way, it overcomes difficulties found by other techniques when faced with complex problems characterized by several constraints with different orders of magnitude and/or different units.The MCR follows an uncoupled approach where the CHT is not embedded into the optimization algorithm. Extensive studies are performed to assess its accuracy and robustness, compared to six other up-to-date CHTs, all implemented into the same canonical Genetic Algorithm to allow a neutral and unbiased evaluation. The numerical experiments comprise benchmark functions from the IEEE-CEC competitions on constrained optimization, and also classical structural engineering problems. The performance of the CHTs is compared using efficiency measures in terms of nonparametric statistical tests. The results indicate that the MCR is remarkably more accurate and robust for the subset of problems presenting different-magnitude constraints, while remaining very competitive and one of the top-performers for all other benchmark problems comprising the case studies.