Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization
Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal front for multi-objective optimization problems u...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/10/3/37 |
| _version_ | 1797472134114574336 |
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| author | Lilian Astrid Bejarano Helbert Eduardo Espitia Carlos Enrique Montenegro |
| author_facet | Lilian Astrid Bejarano Helbert Eduardo Espitia Carlos Enrique Montenegro |
| author_sort | Lilian Astrid Bejarano |
| collection | DOAJ |
| description | Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal front for multi-objective optimization problems using clustering techniques. With this approach, an alternative is sought for further use and improvement of multi-objective optimization algorithms considering solutions and clusters found. To carry out the clustering, the methods k-means and fuzzy c-means are employed, in such a way that there are two alternatives to generate the possible clusters. Regarding the results, it is observed that both clustering algorithms perform an adequate separation of the optimal Pareto continuous fronts; for discontinuous fronts, k-means and fuzzy c-means obtain results that complement each other (there is no superior algorithm). In terms of processing time, k-means presents less execution time than fuzzy c-means. |
| first_indexed | 2024-03-09T19:58:49Z |
| format | Article |
| id | doaj.art-3ea4e724487543da9097eccb076bf603 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-09T19:58:49Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-3ea4e724487543da9097eccb076bf6032023-11-24T00:49:59ZengMDPI AGComputation2079-31972022-03-011033710.3390/computation10030037Clustering Analysis for the Pareto Optimal Front in Multi-Objective OptimizationLilian Astrid Bejarano0Helbert Eduardo Espitia1Carlos Enrique Montenegro2Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, ColombiaFacultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, ColombiaFacultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, ColombiaBio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal front for multi-objective optimization problems using clustering techniques. With this approach, an alternative is sought for further use and improvement of multi-objective optimization algorithms considering solutions and clusters found. To carry out the clustering, the methods k-means and fuzzy c-means are employed, in such a way that there are two alternatives to generate the possible clusters. Regarding the results, it is observed that both clustering algorithms perform an adequate separation of the optimal Pareto continuous fronts; for discontinuous fronts, k-means and fuzzy c-means obtain results that complement each other (there is no superior algorithm). In terms of processing time, k-means presents less execution time than fuzzy c-means.https://www.mdpi.com/2079-3197/10/3/37clusteringc-meansfuzzyPareto frontmulti-objectiveoptimization |
| spellingShingle | Lilian Astrid Bejarano Helbert Eduardo Espitia Carlos Enrique Montenegro Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization Computation clustering c-means fuzzy Pareto front multi-objective optimization |
| title | Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization |
| title_full | Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization |
| title_fullStr | Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization |
| title_full_unstemmed | Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization |
| title_short | Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization |
| title_sort | clustering analysis for the pareto optimal front in multi objective optimization |
| topic | clustering c-means fuzzy Pareto front multi-objective optimization |
| url | https://www.mdpi.com/2079-3197/10/3/37 |
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