Comparative Study and Implementation of Multi-objective Pso Algorithm Using Different Inertia Weight Techniques for Optimal Control of a Cstr Process

In this paper, different Inertia weight techniques in Particle Swarm Optimization algorithm (PSO) have been compared to search for the optimal PID controller gains for a Continuous Stirred Tank Reactor (CSTR) process. The optimization problem considered is highly nonlinear, complex with multiple objectives, wide operating range and constraints. The efficiency of PSO algorithm lies in the suitable selection of Inertia weight (w) to provide a balance between global exploration and local exploitation which in turn ensures the convergence behaviour of particles. The standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. The proposed approach is efficient in achieving stable convergence characteristics, good computational efficiency and capability to avoid from local optima. In the present study an attempt has been made to review some of the inertia weight techniques. Simulation results demonstrate that Adaptive Inertia weight Particle Swarm Optimization (AWPSO) technique is superior to all PSOs considered with various Inertia weight methods for both single objective and multi-objective functions.