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Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems
In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations.
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.
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