Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization

Many optimization problems that involve practicalapplications have functional constraints, and some of these constraintsare active, meaning that they prevent any solution from improving theobjective function value to the one that is better than any solution lyingbeyond the constraint limits. Therefore, the optimal solution usuallylies on the boundary of the feasible region. In order to convergefaster when solving such problems, a new ranking and selection schemeis introduced which exploits this feature of constrained problems. Inconjunction with selection, a new crossover method is also presentedbased on three parents. When comparing the results of this newalgorithm with six other evolutionary based methods, using 12 benchmarkproblems from the literature, it shows very encouraging performance.T-tests have been applied in this research to show if there is anystatistically significance differences between the algorithms. A studyhas also been carried out in order to show the effect of each componentof the proposed algorithm.