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Optimal Control of Fed-batch Process with Improved Particle Swarm Optimization

机译：改进的粒子群算法优化分批投料过程的最优控制

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To overcome the problem of premature convergence on particle swarm optimization(PSO),an improved particle swarm optimization(IPSO) was proposed, which was called keeping particles active PSO. It was guaranteed to keep the diversity of the particle swarm. When particles lose activity, a special mutation or perturbation was used to activate particles and to make particles explore the search space more efficiently. The IPSO was used to determine optimal substrate feeding rate for fed-batch alcohol fermentation process. The experimental results showed that the IPSO can find better feeding rate quickly than SPSO, and increase alcohol yield more 14 percent than GA.
机译：为了解决粒子群优化算法（PSO）过早收敛的问题，提出了一种改进的粒子群优化算法（IPSO），称为粒子群保持主动PSO。保证了保持粒子群的多样性。当粒子失去活性时，会使用特殊的突变或扰动来激活粒子，并使粒子更有效地探索搜索空间。 IPSO用于确定分批补料酒精发酵过程的最佳底物补料速率。实验结果表明，IPSO可以比SPSO更快地找到更好的进料速度，并且比GA可以提高酒精产量14％。

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