A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
Abstract
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.
Keywords
Cite
@article{arxiv.2206.13802,
title = {A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization},
author = {Iman Rahimi and Amir H. Gandomi and Fang Chen and Efren Mezura-Montes},
journal= {arXiv preprint arXiv:2206.13802},
year = {2022}
}
Comments
38 pages, 16643 words