A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization

The global search ability and local search ability are two highly important components of particle swarm optimizer, which are inconsistent each other in many cases, we proposed a novel inertia weight strategy that can adaptively select a preferable inertia weight decline curve for a particle swarm form curves of the constructed function according to the fitness value of swarm, and to automatically harmonize global and local search ability, quicken convergence speed, avoid premature problem, and obtain global optimum. Experimental results on several benchmark functions show that the algorithm can rapidly converge at very high quality solutions.