# Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

arXiv: https://arxiv.org/abs/1711.07907 · HTML: https://mlsanity.com/paper/1711.07907/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2017.

## Built on by (most cited first, 15 of 15)

- [Pareto Set Learning for Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2501.06773/) (2025)
- [Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making](https://mlsanity.com/paper/2108.00597/) (2021)
- [A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities](https://mlsanity.com/paper/2205.10358/) (2022)
- [Multi-Fidelity Methods for Optimization: A Survey](https://mlsanity.com/paper/2402.09638/) (2024)
- [Multi-objective Feature Selection with Missing Data in Classification](https://mlsanity.com/paper/2104.08747/) (2021)
- [Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2401.02160/) (2024)
- [Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference](https://mlsanity.com/paper/2206.02391/) (2022)
- [Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system](https://mlsanity.com/paper/2204.12585/) (2022)
- [Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization](https://mlsanity.com/paper/2201.06700/) (2022)
- [Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems](https://mlsanity.com/paper/2304.09444/) (2023)
- [Multi-Objective Hierarchical Optimization with Large Language Models](https://mlsanity.com/paper/2601.13892/) (2026)
- [Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding](https://mlsanity.com/paper/2112.08760/) (2021)
- [Towards KAB2S: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem](https://mlsanity.com/paper/2206.12906/) (2022)
- [ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization](https://mlsanity.com/paper/2301.03317/) (2023)
- [Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model](https://mlsanity.com/paper/2509.11016/) (2025)

## Abstract (arXiv)

When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, two-archive evolutionary algorithm, for constrained multi-objective optimization. It maintains two co-evolving populations simultaneously: one, denoted as convergence archive, is the driving force to push the population toward the Pareto front; the other one, denoted as diversity archive, mainly tends to maintain the population diversity. In particular, to complement the behavior of the convergence archive and provide as much diversified information as possible, the diversity archive aims at exploring areas under-exploited by the convergence archive including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, comparing to five state-of-the-art constrained evolutionary multi-objective optimizers.