Convergence and diversity analysis of indicator-based multi-objective evolutionary algorithms
Jesús Guillermo Falcón-Cardona, Carlos A. Coello Coello · Proceedings of the Genetic and Evolutionary Computation Conference · 2019
In recent years, quality indicators (QIs) have been employed to design selection mechanisms for multi-objective evolutionary algorithms (MOEAs). These indicator-based MOEAs (IB-MOEAs) generate Pareto front approximations that present convergence and diversity characteristics strongly related to the QI that guides the selection mechanism. However, on complex multi-objective optimization problems, the performance of IB-MOEAs is far from being completely understood. In this paper, we empirically analyze the convergence and diversity properties of five steady-state IB-MOEAs based on the hypervolume, R2, IGD+, ∈+, and Δp. Regarding convergence, we analyze their speed of convergence and the final closeness to the true Pareto front. The IB-MOEAs adopted in our study were tested on problems having different Pareto front shapes, and were taken from six test suites. Our experimental results show general and particular strengths and weaknesses of the adopted IB-MOEAs. We believe that these results are the first step towards a deeper understanding of the behavior of IB-MOEAs.