Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization
Abstract Multi-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions...
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| Format: | Article |
| Language: | English |
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Springer
2021-11-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-021-00570-z |
| _version_ | 1797840689261707264 |
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| author | Haijuan Zhang Gai-Ge Wang |
| author_facet | Haijuan Zhang Gai-Ge Wang |
| author_sort | Haijuan Zhang |
| collection | DOAJ |
| description | Abstract Multi-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions obey different distributions in decision space and the performance of NSGA-III when dealing with DMOPs should be further improved. In this paper, centroid distance is proposed and combined into NSGA-III with transfer learning together for DMOPs, called TC_NSGAIII. Centroid distance-based strategy is regarded as a prediction method to prevent some inappropriate individuals through measuring the distance of the population centroid and reference points. After the distance strategy, transfer learning is used for generating an initial population using the past experience. To verify the effectiveness of our proposed algorithm, NSGAIII, Tr_NSGAIII (NSGA-III combining with transfer learning only), Ce_NSGAIII (NSGA-III combining with centroid distance only), and TC_NSGAIII are compared. Seven state-of-the-art algorithms have been used for comparison on CEC 2015 benchmarks. Besides, transfer learning and centroid distance are regarded as a dynamic strategy, which is incorporated into three static algorithms, and the performance improvement is measured. What’s more, twelve benchmark functions from CEC 2015 and eight sets of parameters in each function are used in our experiments. The experimental results show that the performance of algorithms can be greatly improved through the proposed approach. |
| first_indexed | 2024-04-09T16:18:54Z |
| format | Article |
| id | doaj.art-02141fcf3f954e768440beeb767cad3b |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-09T16:18:54Z |
| publishDate | 2021-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-02141fcf3f954e768440beeb767cad3b2023-04-23T11:32:48ZengSpringerComplex & Intelligent Systems2199-45362198-60532021-11-01921143116410.1007/s40747-021-00570-zImproved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimizationHaijuan Zhang0Gai-Ge Wang1Department of Computer Science and Technology, Ocean University of ChinaDepartment of Computer Science and Technology, Ocean University of ChinaAbstract Multi-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions obey different distributions in decision space and the performance of NSGA-III when dealing with DMOPs should be further improved. In this paper, centroid distance is proposed and combined into NSGA-III with transfer learning together for DMOPs, called TC_NSGAIII. Centroid distance-based strategy is regarded as a prediction method to prevent some inappropriate individuals through measuring the distance of the population centroid and reference points. After the distance strategy, transfer learning is used for generating an initial population using the past experience. To verify the effectiveness of our proposed algorithm, NSGAIII, Tr_NSGAIII (NSGA-III combining with transfer learning only), Ce_NSGAIII (NSGA-III combining with centroid distance only), and TC_NSGAIII are compared. Seven state-of-the-art algorithms have been used for comparison on CEC 2015 benchmarks. Besides, transfer learning and centroid distance are regarded as a dynamic strategy, which is incorporated into three static algorithms, and the performance improvement is measured. What’s more, twelve benchmark functions from CEC 2015 and eight sets of parameters in each function are used in our experiments. The experimental results show that the performance of algorithms can be greatly improved through the proposed approach.https://doi.org/10.1007/s40747-021-00570-zDynamic optimizationTransfer learningMulti-objectiveNSGA-IIIEvolutionary algorithms |
| spellingShingle | Haijuan Zhang Gai-Ge Wang Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization Complex & Intelligent Systems Dynamic optimization Transfer learning Multi-objective NSGA-III Evolutionary algorithms |
| title | Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization |
| title_full | Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization |
| title_fullStr | Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization |
| title_full_unstemmed | Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization |
| title_short | Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization |
| title_sort | improved nsga iii using transfer learning and centroid distance for dynamic multi objective optimization |
| topic | Dynamic optimization Transfer learning Multi-objective NSGA-III Evolutionary algorithms |
| url | https://doi.org/10.1007/s40747-021-00570-z |
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