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

Abstract

This work focuses on the optimization of some high-pressure and temperature food treatments. When dealing with real--life multi-objective optimization problems, 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 work, we analyze and compare different mechanisms to improve the convergence of a preference-based multi-objective optimization algorithm called Weighting Achievement Scalarizing Function Genetic Algorithm. The combination of these techniques has been applied for optimizing a particular food treatment process. In particular, the proposed method based on the introduction of an advanced population achieves important improvements in the considered quality indicator measures.