# A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization

arXiv: https://arxiv.org/abs/2206.13802 · HTML: https://mlsanity.com/paper/2206.13802/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2022.

## Built on by (most cited first, 3 of 3)

- [LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systems](https://mlsanity.com/paper/2601.19121/) (2026)
- [An Improved FOX Optimization Algorithm Using Adaptive Exploration and Exploitation for Global Optimization](https://mlsanity.com/paper/2504.09574/) (2025)
- [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)

This presented study provides a novel analysis of scholarly literature on constraint handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals, keywords, authors, and articles. The paper reviews the main ideas of the most state-of-the-art constraint handling techniques in multi-objective population-based optimization, and then the study addresses the bibliometric analysis in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2020 for the analysis. The results indicate that the constraint handling techniques for multi-objective optimization have received much less attention compared with single-objective optimization. The most promising algorithms for such optimization were determined to be genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence.