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

Abstract Most real-world problems involve some type of optimization that are often constrained. Numerous researchers have investigated several techniques to deal with constrained single-objective and multi-objective evolutionary in many fields, including theory application. This presented study provides a novel analysis scholarly literature on constraint-handling for population-based algorithms according the most relevant journals articles. As contribution this study, paper reviews main ideas state-of-the-art constraint handling optimization, then addresses bibliometric analysis, focus multi-objective, field. The extracted papers include research articles, reviews, book/book chapters, conference published between 2000 2021 analysis. results indicate received much less attention compared optimization. promising such were determined be genetic algorithms, differential particle swarm intelligence. Additionally, “Engineering,” “Computer Science,” “ Mathematics” identified as top three fields which future work is anticipated increase.