Voting enabled dynamic constraint handling portfolios for evolutionary multi/many-objective optimization.

Constraint handling technique (CHT) plays a key role in handling constrained multi-objective optimization problems (CMOPs). In general, CHTs are associated with their own pros and cons under different scenarios, giving rise to an inherent risk in the selection of appropriate CHTs. To take advantages of diverse CHTs, we design a novel competitive co-evolution scheme for CHT portfolios in which multiple complementary CHTs are synergized via a single-population competitive ensemble framework. A self-organizing voting strategy is developed to allocate restrictive resources to constituent CHTs adaptively according to the feedbacks of CHTs as the search proceeds. In addition, an archive update strategy is designed to facilitate effective usage of potential solutions and achieve better compromise between convergence and diversity. Then the concrete algorithm, namely, VP-CMOEA, is presented. We have comprehensively evaluated our proposal on diverse benchmark functions and real-world applications. Empirical results demonstrate the better generality of VP-CMOEA compared with state-of-the-art peers. [ABSTRACT FROM AUTHOR]