Abstract:
For multi-objective optimization algorithms, one of the key features is the ability to find a good approximation to the optimal trade-off among the multiple objectives. The most traditional algorithms focus only on the distribution of solutions in the objective space. Nevertheless, a good representation of solutions in the decision space is also important from the point of view of the decision-making process. This work presents a dominance-weighted uniformity-based multi-objective algorithm. The selection phase of the algorithm performs a greedy search of the uniformity measure weighted by a certain measure of dominance. Preliminary experiments suggest that this method promotes the uniformity of the population in the decision variable space while keeping the convergence performance in the objective space.
