# Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection

arXiv: https://arxiv.org/abs/2402.12381 · HTML: https://mlsanity.com/paper/2402.12381/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 8 of 8)

- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems](https://mlsanity.com/paper/2105.02741/) (2021)
- [Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit](https://mlsanity.com/paper/1612.07603/) (2016)
- [Push and Pull Search for Solving Constrained Multi-objective Optimization Problems](https://mlsanity.com/paper/1709.05915/) (2017)
- [An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions](https://mlsanity.com/paper/1707.08767/) (2017)
- [Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics](https://mlsanity.com/paper/2105.10657/) (2021)
- [Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization](https://mlsanity.com/paper/2206.10464/) (2022)

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

- [Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images](https://mlsanity.com/paper/2512.05410/) (2025)
- [Deep Reinforcement Learning-Assisted Automated Operator Portfolio for Constrained Multi-objective Optimization](https://mlsanity.com/paper/2603.16401/) (2026)
- [Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model](https://mlsanity.com/paper/2509.11016/) (2025)
- [A general optimization solver based on OP-to-MaxSAT reduction](https://mlsanity.com/paper/2604.21961/) (2026)

## Abstract (arXiv)

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually difficult to select suitable operators for the problem at hand. Hence, improving operator selection is promising and necessary for CMOEAs. This work proposes an online operator selection framework assisted by Deep Reinforcement Learning. The dynamics of the population, including convergence, diversity, and feasibility, are regarded as the state; the candidate operators are considered as actions; and the improvement of the population state is treated as the reward. By using a Q-Network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance. The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems. The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.