A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems

We present a comparison of two constraint-handling methods used in the application of particle swarm optimization (PSO) to constrained nonlinear optimization problems (CNOPs). A brief review of constraint-handling techniques for evolutionary algorithms (EAs) is given, followed by a direct comparison of two existing methods of enforcing constraints using PSO. The two methods considered are the application of nonstationary multistage penalty functions and the preservation of feasible solutions. Five benchmark functions are used for the comparison, and the results are examined to assess the performance of each method in terms of accuracy and rate of convergence. Conclusions are drawn and suggestions for the applicability of each method to real-world CNOPs are given.