New Dynamic Constrained Optimization PSO Algorithm

A new particle swarm optimization (PSO) algorithm solving dynamic constrained optimization problem (DCOP) is proposed in this paper. First, the time period of DCOP was divided into several small estequal subperiods. In each subperiod, the DCOP is approximated by a static constrained optimization problem, Thus, the original DCOP is approximately transformed into several static constrained optimization problems defined in different subperiods. Second, in order to solve each static constrained optimization problem, a new fitness function based on the original objective and the constraints of DCOP is designed. Accordingly, when the individuals are evaluated or selected, it doesn´t need to care about the feasibility of individuals. At last, the comparative study shows that the proposed algorithm is more effective and can find better solutions in environment-varying than the compared algorithms can.