Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization

Abstract

A nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization (NDWPSO) was presented to solve the problem that it easily stuck at a local minimum point and its convergence speed is slow, when the linear decreasing inertia weight PSO (LDWPSO) adapt to the complex nonlinear optimization process. The rate of particle evolution changing was introduced in this new algorithm and the inertia weight was formulated as a function of this factor according to its impact on the search performance of the swarm. In each iteration process, the weight was changed dynamically based on the current rate of evolutionary changing value, which provides the algorithm with effective dynamic adaptability. The algorithm of LDWPSO and NDWPSO were tested with three benchmark functions. The experiments show that the convergence speed of NDWPSO is significantly superior to LDWPSO, and the convergence accuracy is improved.