  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.
