A Coevolutionary Framework for Constrained Multiobjective Optimization Problems

Constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity CMOPs with small feasible regions. To remedy this issue, article proposes a coevolutionary framework for constrained optimization, which solves complex CMOP assisted by simple helper problem. The proposed evolves one population to solve original another problem derived from one. two populations evolved same optimizer separately, assistance solving is achieved sharing useful information between populations. In experiments, compared several state-of-the-art tailored CMOPs. High competitiveness applying it 47 benchmark vehicle routing time windows.