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A platform for research: civil engineering, architecture and urbanism
A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
https://orcid.org/0000-0003-3780-4726
https://orcid.org/0000-0002-2798-0104
https://orcid.org/0000-0003-4971-8729
https://orcid.org/0000-0002-1565-5267
AbstractMost 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.
A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
https://orcid.org/0000-0003-3780-4726
https://orcid.org/0000-0002-2798-0104
https://orcid.org/0000-0003-4971-8729
https://orcid.org/0000-0002-1565-5267
AbstractMost 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.
A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
https://orcid.org/0000-0003-3780-4726
https://orcid.org/0000-0002-2798-0104
https://orcid.org/0000-0003-4971-8729
https://orcid.org/0000-0002-1565-5267
AbstractMost 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.
A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
Arch Computat Methods Eng
Rahimi, Iman (author) / Gandomi, Amir H. (author) / Chen, Fang (author) / Mezura-Montes, Efrén (author)
Archives of Computational Methods in Engineering ; 30 ; 2181-2209
2023-04-01
Article (Journal)
Electronic Resource
English
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