On the optimality of particle swarm parameters in dynamic environments

This paper investigates whether the optimal parameter configurations for particle swarm optimizers (PSO) change when changes in the search landscape occur. To test this, specific environmental changes that may occur during dynamic function optimization are deliberately constructed, using the moving peaks function generator. The parameters of the chargedand quantum PSO algorithms are then optimized for the initial environment, as well as for each of the constructed problems. It is shown that the optimal parameter configurations for the various environments differ not only with respect to the initial optimal configurations, but also with respect to each other. The results lead to the conclusion that PSO parameters need to be re-optimized or selfadapted whenever environmental changes are detected.