Distance Measurement Based Adaptive Particle Swarm Optimization
一种基于距离度量的自适应粒子群优化算法

The inertia weight plays an important role in Particle Swarm Optimization(PSO). The classical PSO used a fixed inertia weight for all particles in an iteration and ignored the difference among the particles. To cope with this issue,a Distance Measurement based Adaptive Particle Swarm Optimization(D MAPSO ) was proposed. The Euclidean distance was used to calculate the difference between a particle and the known best global particle, and the particle tuned adaptively the value of the inertia weight according to the difference. Several classical benchmark functions were used to evaluate the strategy. The experimental results show that for continuous optimization problems, the DMAPSO outper-forms the classical PSO. The iteration times for finding the best solutions in the DMAPSO decrease about 60%averagely compared with that in the classical PSO.