This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical constrained optimization problems. The approach uses a single method to handle different types of constraints (linear, nonlinear, equality or inequality) and it incorporates a shake-mechanism and a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions taken from the specialized literature and is compared with respect to algorithms representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a highly competitive alternative to solve constrained optimization problems.
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