Energy-preserving chaotic particle swarm optimization algorithm with application in parameter identification in bioprocess

Fermentation process is one of the most complicated processes, which is a large scale nonlinear modeling problem with multi-constraints and multi-variables. The successful applications of model-based advanced measurement and control techniques mostly depend on the accuracy of bioprocess model. As a swarm intelligence approach, particle swarm optimization (PSO) algorithm can explore the global optimal solution of complex problems. In recent years, there has increasingly research activities regarding the chaotic PSO (CPSO) algorithms, which performs better by instead pseudo random number with chaotic series. In this paper, a novel CPSO algorithm exploiting the properties of swarm energy is proposed for the bioprocess model parameters identification. The algorithm remains the good global searching ability by releasing the swarm energy slowly and accelerating the particles with worse fitness. Simulation experiment results show that the improved CPSO not only performs better convergence capability but also presents better solution quality as compared to the conventional CPSO.