A Scalarized Augmented Lagrangian Algorithm (SCAL) for Multi-objective Optimization Constrained Problems

In this paper, a methodology to solve constrained multi-objective problems is presented, using an Augmented Lagrangian technique to deal with the constraints and the Augmented Weighted Tchebycheff method to tackle the multi-objective problem and find the Pareto Frontier. We present the algorithm, as well as some preliminary results that seem very promising when compared to previous state-of-theart work. As far as we know, the idea of incorporating an Augmented Lagrangian in multi-objective optimization is rarely used so, the obtained results are very encouraging to pursuit further in this line of investigation, namely with the tuning of the Augmented Lagrangian parameters as well as testing other algorithms to solve the subproblems or to handle the multi-objective problems. It is also our intention to investigate the resolution of problems with three or more objectives.