A New Particle Swarm Optimization Algorithm with Balancing Local and Global Search Ability

Because of two disadvantages which are the premature convergence and slow searching of particles in the standard Particle Swarm Optimization(PSO), two aspects of improving algorithm are proposed. On the one hand, it changes the parameter's value of learning factor and Inertia weight by particle's fitness to balance particle's local and global search ability. On the other hand, it adds learned objects including all of particles finer than itself to improve social learning ability. Contrasted to standard PSO, the experimental result of some typical testing functions proves that the new algorithm has a higher convergence efficiency and faster search speed.