Balanced ranking method for constrained optimization problems using evolutionary algorithms
This work presents a new technique to handle constraints in the solution of optimization problems by evolutionary algorithms  the Balanced Ranking Method (BRM). In this method the fitness function is based on two rankings, for feasible and infeasible solutions respectively. The rankings are merged according to deterministic criteria that consider the status of the search process and specific properties of the population. The focus of the BRM method is to comprise a constraint-handling technique (CHT) that is not coupled to the optimization algorithm, and thus can be implemented into different algorithms. The method is compared with other well-known CHTs that follow this same uncoupled approach, all implemented into a canonical Genetic Algorithm. Two well-known suites of benchmark functions and five engineering problems are used as case studies. The performance of the different CHTs is assessed by nonparametric statistical tests, including the Sign test and the Wilcoxon Signed-Ranks test. The results indicate that the BRM presents a good performance, being reliable and efficient, while maintaining its uncoupled characteristic leading to an easy implementation and hybridization with any search algorithm.
constrained optimization genetic algorithms constraint handling