Benchmark testing of simulated annealing, adaptive random search and genetic algorithms for the global optimization of bioprocesses

摘要

This paper studies the global optimisation of bioprocesses employing model-based dynamic programming schemes. Three stochastic optimisation algorithms were tested: simulated annealing, adaptive random search and genetic algorithms. The methods were employed for optimising two challenging optimal control problems of fed-batch bioreactors. The main results show that adaptive random search and genetic algorithms are superior at solving these problems than the simulated annealing based method, both in accuracy and in the number of function evaluations.