Constrained　multimodal　multi-objective　optimization　problems　(CMMOPs)　are　characterized　by　multiple　constrained　Pareto　sets　(CPSs)　sharing　the　same　constrained　Pareto　front　(CPF).　The　challenge　lies　in　efficiently　identifying　equivalent　CPSs　while　maintaining　a　balance　among　convergence,　diversity,　and　constraints.　Addressing　this　challenge,　we　propose　a　dynamic-ranking-based　constraint　handling　technique　implemented　in　a　co-evolutionary　algorithm,　named　DRCEA,　specifically　designed　for　solving　CMMOPs.　To　search　for　equivalent　CPSs,　we　introduce　a　co-evolutionary　framework　involving　two　populations:　a　convergence-first　population　and　a　constraint-first　population.　The　co-evolutionary　framework　facilitates　knowledge　transfer　and　sustains　diverse　solutions.　Subsequently,　a　dynamic　ranking　strategy　is　employed　with　dynamic　weight　parameters　that　consider　both　dominance　and　constraint　relationships　among　individuals.　Within　the　convergence-first　population,　the　weight　parameter　for　convergence　gradually　decreases,　while　the　constraint　parameter　increases.　Conversely,　in　the　constraint-first　population,　the　weight　parameter　for　constraints　gradually　decreases,　while　the　convergence　parameter　increases.　This　approach　ensures　a　well-balanced　consideration　of　convergence　and　constraints　within　the　two　distinct　populations.　Experimental　results　on　the　CMMOP　test　suite　and　the　real-world　CMMOP　test　scenario　validate　the　effectiveness　of　the　proposed　dynamic-ranking-based　constraint　handling　technique,　demonstrating　the　superiority　of　DRCEA　over　seven　state-of-the-art　algorithms.　©　2024