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This paper proposes a novel constraint-handling mechanism named angle-basedconstrained dominance principle (ACDP) embedded in a decomposition-basedmulti-objective evolutionary algorithm (MOEA/D) to solve constrainedmulti-objective optimization problems (CMOPs). To maintain the diversity of theworking population, ACDP utilizes the information of the angle of solutions toadjust the dominance relation of solutions during the evolutionary process.This paper uses 14 benchmark instances to evaluate the performance of theMOEA/D with ACDP (MOEA/D-ACDP). Additionally, an engineering optimizationproblem (which is I-beam optimization problem) is optimized. The proposedMOEA/D-ACDP, and four other decomposition-based CMOEAs, including C-MOEA/D,MOEA/D-CDP, MOEA/D-Epsilon and MOEA/D-SR are tested by the above benchmarks andthe engineering application. The experimental results manifest that MOEA/D-ACDPis significantly better than the other four CMOEAs on these test instances andthe real-world case, which indicates that ACDP is more effective for solvingCMOPs.

MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems