A Hyper-feasible Solutions Based Update Weight Vectors Evolutionary Algorithm for Constrained Multiobjective Optimization Problem

Existing decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs) use fixed decomposition methods to partition the search space, which may limit their ability in solving some certain constrained multi-objective optimization problems (CMOPs). To address this issue, this paper introduces the concept of hyper-feasible solutions, which are extracted from feasible solutions and promising infeasible solutions. Based on these solutions, we propose a novel algorithm called HSWU, which adaptively partitions the search space and guides the search direction to enhance the efficiency of solution searching. Experimental results on three benchmark test suites demonstrate that HSWU outperforms five state-of-the-art CMOEAs in terms of performance.