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

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.

Automated summary

This study analyzes scholarly literature on constraint-handling techniques for single-objective and multi-objective population-based algorithms. The results show that the constraint-handling techniques for multi-objective optimization have received less attention compared to single-objective optimization. Genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence are identified as the most promising algorithms for such optimization. Future research work is anticipated to increase in Engineering, Computer Science, and Mathematics.