A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation

A new particle swarm optimal algorithm is proposed in this paper. In the algorithm, logarithm decreasing inertia weight is introduced to improve the convergence speed. Further, in order to overcome the disadvantage of easily getting into premature, the diversity of the population is increased by using a chaos mutation. The experiments demonstrate that the new algorithm is better than the particle swarm optimization with linearly decreasing inertia weight not only in convergence speed but also in accuracy.