Mass-spring-damper motion dynamics-based particle swarm optimization

Mass-spring-damper motion dynamics-based particle swarm optimization (MMD-PSO) is a novel optimization paradigm based on motion dynamic model which consists of mass, spring and damper. In MMD-PSO some particles, which are located fitter places than other particles, drop their anchor and connect springs and dampers between the anchors and all the particles. These connections influence the movements of the particles so as to proceed to fitter places attracted by the anchors. To demonstrate the effectiveness of MMD-PSO, several experiments are carried out on numerical optimization problems with complex test functions. The results show that proposed MMD-PSO is more powerful than original PSO and PSO mass-spring analogy in terms of robustness and convergence speed with no tuning parameters.