Multiobjective optimization of a free radical polymerization - Genetic algorithms

The present paper continues a previous article [11 where multiobjective optimization of a polymerization process was approached with a classical method - sequential quadratic programming. In this article, an optimization method based on genetic algorithms is used for the free radical polymerization of methyl methacrylate. The influence on the optimization results of the main parameters (the size of the initial population, the number of generations, recombination rate, mutation rate), as well as that of different variants of genetic algorithms (different ways of recombination, mutation and selection) is studied. The main conclusion of the article is that these values and methods depend on the studied process, but also inter-condition each other, such that the optimization results are rather more correlated with the overall set of values considered. Thus, we try to establish some directions to guide the search for optimal values.