An Improved Particle Swarm Optimization Algorithm

Because the variable inertia weight particle swarm optimization algorithm is easy to fall into the local optimum, this paper introduces the improved simulated annealing operator, chaotic disturbance operator and Cauchy mutation operator to the former and proposes an improved particle swarm optimization algorithm; Then, two typical Benchmark functions are used to test the performance of basic the proposed algorithm; Finally, the relations of population size and particle dimension to performance of the proposed algorithm is analyzed. Simulation results show that while maintains the superiorities of simple structure, few parameters and the ease of implement, the proposed algorithm improves the convergence precision largely.