An improved indicator-based two-archive algorithm for many-objective optimization problems

The large number of objectives in many-objective optimization problems (MaOPs) has posed significant challenges to the performance of multi-objective evolutionary algorithms (MOEAs) in terms of convergence and diversity. To design a more balanced MOEA, a multiple indicator-based two-archive algorithm named IBTA is proposed to deal with problems with complicated Pareto fronts. Specifically, a two-archive framework is introduced to focus on convergence and diversity separately. In IBTA, we assign different selection principles to the two archives. In the convergence archive, the inverted generational distance with noncontributing solution detection (IGD-NS) indicator is applied to choose the solutions with favorable convergence in each generation. In the diversity archive, we use crowdedness and fitness to select solutions with favorable diversity. To evaluate the performance of IBTA on MaOPs, we compare it with several state-of-the-art MOEAs on various benchmark problems with different Pareto fronts. The experimental results demonstrate that IBTA can deal with multi-objective optimization problems (MOPs)/MaOPs with satisfactory convergence and diversity.