Particle Swarm Optimization with Contracted Ranges of Both Search Space and Velocity

To improve further the performance of PSO(Particle Swarm Optimization), a modified PSO algorithm is proposed and called CSV-PSO algorithm. Based on the best fitness of the particles, the ranges of both search space and velocity of the particles are contracted dynamically with the evolution of particle swarm in CSV-PSO algorithm. To avoid the possible occurence of stagnation phenomenon in the PSO algorithm, the re-initialization mechanism based on different search spaces is introduced in the CSV-PSO. Numerical examples show that it is of advantage to accelerating the algorithm's convergence and improving its calculation accuracy so as to contract appropriately the ranges of both search space and velocity of particles in evolutionary progress and the algorithm is easier for convergence, more accurate for calculation and more stable for running.