Decomposition-based Multiobjective Balance Optimizer

This work proposes the Multiobjective Equilibrium Optimizer based on Decomposition (MEO/D), which is founded on the Tchebycheff decomposition method. The proposed algorithm was applied to optimize the 15 problems of the IEEE CEC 2018 (IEEE Congress on Evolutionary Computation) benchmark with 10 objectives, thus characterizing them as many-objective problems. The Hypervolume (HV) metric was used to evaluate the convergence and diversity of the MEO/D. The results were compared against state-of-the-art multiobjective metaheuristics using the Friedman test with the Nemenyi post-hoc (critical distance diagram) at a 5% significance level. The results demonstrate that MEO/D was competitive, in several cases, with the compared algorithms, proving its efficiency in solving many-objective optimization problems.