Particle Swarm Optimization with Adaptive Mutation and Its Application Research in Tuning of PID Parameters

Abstract

Particle swarm optimization (PSO) is a powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. However, it has been observed that the standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. To resolve this problem an improved particle swarm optimization (IPSO) is proposed in this paper. This new algorithm introduces mutation operator with adaptive mutation probability into the PSO algorithm; meanwhile it replaces those particles that fly out the solution space with new generated random particles during the searching process. Through testing two benchmark functions with large dimensionality, the experimental results show the new method enhances the global optimization ability greatly, and avoids the premature convergence problem effectively. Based on it, this improved algorithm is applied to tune the PID controller's parameters of the marine system. The results show that this approach is effective and the designed controller has more excellent performance than the controllers designed by the PSO algorithm and the standard genetic algorithm (SGA).