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Overall constraint violation functions are commonly used in multiobjective evolutionary algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck in two stagnation states: 1) since the feasible region of a multiobjective optimization problem can consist of several disconnected feasible subregions, the search can be easily trapped in a feasible subregion which does not contain all the global Pareto optimal solutions and 2) an overall constraint violation function may have many nonzero minimal points, it can make the search stuck in an unfeasible area. To address these two issues, this article proposes a strategy to detect whether or not the search is stuck in these two stagnation states and then escape from them. Our proposed detect-and-escape strategy uses the feasible ratio and the change rate of overall constraint violation to detect stagnation, and adjusts the weight of the …