Improving the performance of a preference-based multi-objective algorithm to optimize food treatment processes

This work focuses on the optimization of some high-pressure and temperature food treatments. In some cases, when dealing with real-life multi-objective optimization problems, such as the one considered here, the computational cost of evaluating the considered objective functions is usually quite high. Therefore, only a reduced number of iterations is affordable for the optimization algorithm. However, using fewer iterations can lead to inaccurate solutions far from the real Pareto optimal front. In this article, different mechanisms are analysed and compared to improve the convergence of a preference-based multi-objective optimization algorithm called the Weighting Achievement Scalarizing Function Genetic Algorithm (WASF-GA). The combination of these techniques has been applied to optimize a particular food treatment process. In particular, one of the proposed methods, based on the introduction of an advanced population, achieves important improvements in the quality indicator measures considered