Particle swarm optimization algorithm for constrained problems

A novel particle swarm optimization (PSO) algorithm with the evaluation of infeasibility degree (IFD) of constraints is presented for nonlinear programming (NLP) problems with equality and inequality constraints. The IFD of constraints is defined as the sum of the squared values of the constraint violations. The proposed novel PSO updates the local best position and global best position according to the objective value and the value of IFD simultaneously. The results of several numerical tests and one real engineering optimization problem show that the proposed approach is efficient. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd.