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

Iman Rahimi, Amir H. Gandomi, Fang Chen, Efren Mezura-Montes
2022 arXiv   pre-print
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
more » ... ticles, 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.
arXiv:2206.13802v1 fatcat:zhr2ril2bjgqbaefd2ygh3l2tu