Particle Swarm Optimization And Its Application In Chemical Engineering

Particle swarm optimization(pso)algorithm is a combination optimization algorithm of l ocal particle properties and global particle properties.In the standard particle swarm optimizat ion(pso)algorithm,all particles will be in the next step to achieve the optimal particle locatio n,which makes the algorithm is very easy to fall into local optimal solution.Because the stan dard pso algorithm can not include the history of particles and the rapid convergence of pso al gorithm.For this weakness mentioned up,this paper achieves the pso algorithm by adding the history about pa rticles' s serch path.This algorithm has more chance to jump out of local convergence by adding a local pat h information to local particles,which can be a more effective use of information in the process of particles in the algorithm.The history of the particle path information is also added to the algorithm,the algorithm c an be more smoothly to achieve the global optimal.Based on Benchmarks test function to test the algorithm.By comparing with the classic particle swarm optimization(pso)algorithm,it can be seen that the improved algorithm can c onverge to global optimal solution of more stable,especially for gradient is very big,and the optimal solution and local solutions are similar.This improved algorithm performs perfect in t he heat exchange network optimization,the benzene-toluene flash experiment and the ferme ntation kinetics model parameters optimization applications.The experimental results of the i mproved algorithm can solve practical chemical problems.