TY  - JOUR
AB  - Most real-world problems involve some type of optimization problems that are often constrained. Numerous researchers have investigated several techniques to deal with constrained single-objective and multi-objective evolutionary optimization in many fields, including theory and application. 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 and articles. As a contribution to this study, the paper reviews the main ideas of the most state-of-the-art constraint handling techniques in population-based optimization, and then the study addresses the bibliometric analysis, with a focus on multi-objective, in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2021 for 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. Additionally, ?Engineering,? ?Computer Science,? and ? Mathematics? were identified as the top three research fields in which future research work is anticipated to increase.
AU  - Rahimi, I
AU  - Gandomi, AH
AU  - Chen, F
AU  - Mezura-Montes, E
DA  - 2022/01/01
DO  - 10.1007/s11831-022-09859-9
JO  - Archives of Computational Methods in Engineering
PB  - SPRINGER
PY  - 2022/01/01
TI  - A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
Y1  - 2022/01/01
Y2  - 2026/06/06
ER  -