# An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions

arXiv: https://arxiv.org/abs/1707.08767 · HTML: https://mlsanity.com/paper/1707.08767/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection](https://mlsanity.com/paper/2402.12381/) (2024)
- [Multi-objective Optimal Roadside Units Deployment in Urban Vehicular Networks](https://mlsanity.com/paper/2402.18581/) (2024)
- [Meta-Learning-Assisted Constraint Relaxation for Constrained Black-Box Optimization](https://mlsanity.com/paper/2602.00532/) (2026)
- [Deep Reinforcement Learning-Assisted Automated Operator Portfolio for Constrained Multi-objective Optimization](https://mlsanity.com/paper/2603.16401/) (2026)

## Abstract (arXiv)

This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) in the current population. 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. Then the fourteen benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem.