An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes
Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many objective problems is often a difficult task even for modern multiobjective algorithms. In some cas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2018
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| Online Access: | https://eprints.nottingham.ac.uk/52700/ |
| _version_ | 1848798789304844288 |
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| author | Pinheiro, Rodrigo Lankaites Landa-Silva, Dario Laesanklang, Wasakorn Constantino, Ademir Aparecido |
| author_facet | Pinheiro, Rodrigo Lankaites Landa-Silva, Dario Laesanklang, Wasakorn Constantino, Ademir Aparecido |
| author_sort | Pinheiro, Rodrigo Lankaites |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario present similarities in their fitness landscapes. That is, there are recurring features in the fitness landscapes when searching for solutions to different problem instances. We propose a methodology to exploit this characteristic by solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We use three goal-based objective functions and show that on benchmark instances of the multiobjective vehicle routing problem with time windows, the methodology is able to produce good results in short computation time. The methodology allows to combine the effectiveness of state-of-the-art multiobjective algorithms with the efficiency of goal programming to find good compromise solutions in problem scenarios where instances have similar fitness landscapes. |
| first_indexed | 2025-11-14T20:25:21Z |
| format | Article |
| id | nottingham-52700 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:25:21Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-527002018-09-26T04:30:20Z https://eprints.nottingham.ac.uk/52700/ An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes Pinheiro, Rodrigo Lankaites Landa-Silva, Dario Laesanklang, Wasakorn Constantino, Ademir Aparecido Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario present similarities in their fitness landscapes. That is, there are recurring features in the fitness landscapes when searching for solutions to different problem instances. We propose a methodology to exploit this characteristic by solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We use three goal-based objective functions and show that on benchmark instances of the multiobjective vehicle routing problem with time windows, the methodology is able to produce good results in short computation time. The methodology allows to combine the effectiveness of state-of-the-art multiobjective algorithms with the efficiency of goal programming to find good compromise solutions in problem scenarios where instances have similar fitness landscapes. Springer 2018-06-15 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/52700/1/dls_icores2018_book.pdf Pinheiro, Rodrigo Lankaites, Landa-Silva, Dario, Laesanklang, Wasakorn and Constantino, Ademir Aparecido (2018) An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes. Communications in Computer and Information Science . ISSN 1865-0929 (In Press) Multi-criteria decision making; Goal programming; Pareto optimisation; Multiobjective vehicle routing |
| spellingShingle | Multi-criteria decision making; Goal programming; Pareto optimisation; Multiobjective vehicle routing Pinheiro, Rodrigo Lankaites Landa-Silva, Dario Laesanklang, Wasakorn Constantino, Ademir Aparecido An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes |
| title | An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes |
| title_full | An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes |
| title_fullStr | An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes |
| title_full_unstemmed | An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes |
| title_short | An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes |
| title_sort | efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes |
| topic | Multi-criteria decision making; Goal programming; Pareto optimisation; Multiobjective vehicle routing |
| url | https://eprints.nottingham.ac.uk/52700/ |