Constraint Handling in Particle Swarm Optimization g07, g10, and g13. For the rest of the test func- tions, SMES and RCVPSO share the same best results. Regarding the results shown in Figure 6, RCVPSO obtains same best results as RY and CW for functions g01, g03, g04, g06, g11, and g12; while attains inferior performance for the rest of the test functions. Overall, the RCVPSO is competitive in terms of performance for most of the test functions for the following selected algorithms: PSO+GlobalWorst, DOM+RVPSO, MSPSO, TY, and SMES. How- ever, PESO, RY and CW are better than RCVOPSO in solving the selected test functions. CONCLUSION AND DISCUSSION This article proposes a constrained PSO, called RCVPSO, to solve for constrained optimization problems. RCVPSO converts the COP into an Simulation study shows that RCVPSO is able to obtain quality feasible solutions for most of the test problems while faces difficulty in solv- ing COPs with combination of linear inequality, nonlinear inequality, linear equality and nonlinear equality constraints functions, and the large por- tion of these constraint functions are also active constraints. This deficiency is contributed by the quick convergence characteristic of PSO and the lacking to support possible search along boundary where the constraints are active constraints. De- spite of this deficiency, RCVPSO's performance is observed to be competitive with most of the selected state-of-the-art approaches, particularly the constrained PSOs. Otherwise, it still falls short in performance when compared to two of the constrained optimization evolutionary algorithms (COEA) and a constrained hybrid PSO (PESO). Future works include improving convergence and diversity issues of PSO by developing criteria