A Resilient Particle Swarm Optimization Algorithm Based on Chaos and Applying It to Optimize the Fermentation Process

When an individual is closed to the optimal particle, its velocity will approximate to zero. This is the main reason why the particle swarm optimization algorithm is prone to trap into local minima, therefore using a strategy in which the velocity is not dependent on the size of distance between the individual and the optimal particle but only dependent on its direction, an adaptive scheme is adopted to adjust the magnitude of the velocity resiliently. At the sametime, by making the best of the ergodicity, stochastic property and regularity of chaos. A resilient particle swarm global optimization algorithm based on chaos is proposed, succeed in applying it to optimize the fermentation process, demonstrate that the new algorithm has the ability to avoid being trapped in local minima, and improves computational precision and convergence ratio.