MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems - Fan, Zhun; Fang, Yi; Li, Wenji; Cai, Xinye; Wei, Caimin; Goodman, Erik (Applied soft computing, 2019)
This paper proposes a novel constraint-handling mechanism, namely the angle-based constrained dominance principle (ACDP), to solve constrained multi-objective optimization problems (CMOPs). In this work, the mechanism of ACDP is embedded in a decomposition-based multi-objective evolutionary algorithm (MOEA/D). ACDP uses the angle information among solutions of a population and the proportion of feasible solutions to adjust the dominance relationship, so that it can maintain good convergence, diversity and feasibility of a population, simultaneously. To evaluate the performance of the proposed MOEA/D-ACDP, fourteen benchmark instances and an engineering optimization problem are studied. Six state-of-the-art CMOEAs, including C-MOEA/D, MOEA/D-CDP, MOEA/D-Epsilon, MOEA/D-SR, NSGA-II-CDP and SP, are compared. The experimental results illustrate that MOEA/D-ACDP is significantly better than the other six CMOEAs on these benchmark problems and the real-world case, which demonstrates the effectiveness of ACDP. •The proposed MOEA/D-ACDP utilizes the angle information to maintain the diversity.•MOEA/D-ACDP enhances convergence to the PF by exploring infeasible regions.•MOEA/D-ACDP is significantly better than the other six CMOEAs on the benchmark problems.