Two-stage Probing Method for Constrained Optimization Problems through Particle Swarm Optimization

Particle Swarm Optimizer (PSO) suffers a problem which gets used to trap into a sub-optimal solution, especially in Constrained Optimization (CO). On the other hand, it´s difficult to converge to a feasible domain for some constrained optimization problems. The paper proposes a two-stage probing method to improve PSO method. The first stage guarantees the particle to get away from feasible region as little probability as possible, and the second stage probes further to overcome local minima by Rosenbrock method. The proposed method is implemented and tested for several functions. The results show that the combining method demonstrates a quite good performance in finding global minima reliably and predictably with no need of many parameters to be modified.