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Found 5 papers, 0 papers with code
MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems
The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
Compared with other CMOEAs, the proposed PPS method can more efficiently get across infeasible regions and converge to the feasible and non-dominated regions by applying push and pull search strategies at different stages.
An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions
In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs.
A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus images
In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images.
Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit
Multi-objective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems.
