The effect of varying inertia weight on Particle Swarm Optimization (PSO) algorithm in optimizing PID controller of temperature control system

The implementation of PSO as an optimization algorithm in finding the optimal values of PID's parameters has attracted a significant interest among researchers. One of the important parameters in PSO algorithm is inertia weight (ω) which directly affects the percentage of previous velocity used in determining the next position of particles. A high value of ω will force the particles to fly with a significant influence of previous velocity while with a low value of ω, current velocity will contribute more to the particle's trajectory. This paper studies various types of inertia weight to be implemented with PSO in optimizing PID controller's parameters for a temperature control system. Among popular types of inertia weight are fixed, linearly decreasing, exponentially decreasing and stochastic. The experimental results shows that the Linearly Decreasing Inertia Weight (LDIW) approach managed to improve the performance of PID controller as compared to several other types.