MOSA/D-O and MOSAD/D-O-II: Performance analysis of decomposition-based algorithms in many objective problems

In recent years, many-objective optimization problems (MaOPs) have been challenging. Classically, algorithms obtain first the Pareto front (PF). Next, a decision maker (DM) can choose the best solutions according to their preferences in the region of interest (ROI). However, the DM effort increases with objectives in MaOPs. For this reason, this paper proposes a new C++ software based on simulated annealing, decomposition, differential evolution, and outranking relations. MOSA/D-O and MOSA/D-O-II algorithms from the software can pressure toward the ROI at run-time in MaOPs. Both algorithms were tested with DTLZ and WFG benchmarks, showing promised performance in an ad hoc indicator for 5 and 10 objectives with 10 DMs.