Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization
It remains a challenge to identify a satisfactory set of tradeoff solutions for many-objective optimization problems that have more than three objectives. Coevolving the solutions with preference is becoming increasingly popular due to the enhanced local search capability, which makes it suitable fo...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8493477/ |
| _version_ | 1818329620118765568 |
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| author | Zhe Shu Weiping Wang |
| author_facet | Zhe Shu Weiping Wang |
| author_sort | Zhe Shu |
| collection | DOAJ |
| description | It remains a challenge to identify a satisfactory set of tradeoff solutions for many-objective optimization problems that have more than three objectives. Coevolving the solutions with preference is becoming increasingly popular due to the enhanced local search capability, which makes it suitable for solving many-objective optimization problems. The framework of preference-inspired co-evolutionary algorithms (PICEAs) is suitable for obtaining promising performance for such problems, and the PICEA with goal vectors (PICEA-g) has achieved good performance in many applications. In this paper, an improved PICEA-g is proposed to further resolve this long-standing problem. The local principal component analysis operator is used as a controller to further expand the ability of the PICEA-g algorithm and enhance the convergence of PICEA-g. The proposed algorithm was evaluated using several widely used benchmark test suites that had 3-15 objectives and made a systematic comparison with five state-of-the-art multi-objective evolutionary algorithms. The resulting substantial amount of experimental results revealed that the algorithm we proposed could have good performance on most of the test suites assessed in our research, and it performs very well compared with other many-objective optimization algorithms. In addition, a sensitivity test was carried out to explore the impact of a key parameter in the algorithm we proposed in this study. |
| first_indexed | 2024-12-13T12:50:57Z |
| format | Article |
| id | doaj.art-da3dd31713284b83af55c0c3262db073 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T12:50:57Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-da3dd31713284b83af55c0c3262db0732022-12-21T23:45:20ZengIEEEIEEE Access2169-35362018-01-016687016871510.1109/ACCESS.2018.28762738493477Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective OptimizationZhe Shu0https://orcid.org/0000-0001-6201-4457Weiping Wang1College of Systems Engineering, National University of Defense Technology, Changsha, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha, ChinaIt remains a challenge to identify a satisfactory set of tradeoff solutions for many-objective optimization problems that have more than three objectives. Coevolving the solutions with preference is becoming increasingly popular due to the enhanced local search capability, which makes it suitable for solving many-objective optimization problems. The framework of preference-inspired co-evolutionary algorithms (PICEAs) is suitable for obtaining promising performance for such problems, and the PICEA with goal vectors (PICEA-g) has achieved good performance in many applications. In this paper, an improved PICEA-g is proposed to further resolve this long-standing problem. The local principal component analysis operator is used as a controller to further expand the ability of the PICEA-g algorithm and enhance the convergence of PICEA-g. The proposed algorithm was evaluated using several widely used benchmark test suites that had 3-15 objectives and made a systematic comparison with five state-of-the-art multi-objective evolutionary algorithms. The resulting substantial amount of experimental results revealed that the algorithm we proposed could have good performance on most of the test suites assessed in our research, and it performs very well compared with other many-objective optimization algorithms. In addition, a sensitivity test was carried out to explore the impact of a key parameter in the algorithm we proposed in this study.https://ieeexplore.ieee.org/document/8493477/Evolutionary algorithmsmany-objective optimizationoriented goal vectorsco-evolutionary computation |
| spellingShingle | Zhe Shu Weiping Wang Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization IEEE Access Evolutionary algorithms many-objective optimization oriented goal vectors co-evolutionary computation |
| title | Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization |
| title_full | Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization |
| title_fullStr | Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization |
| title_full_unstemmed | Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization |
| title_short | Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization |
| title_sort | preference inspired co evolutionary algorithms with local pca oriented goal vectors for many objective optimization |
| topic | Evolutionary algorithms many-objective optimization oriented goal vectors co-evolutionary computation |
| url | https://ieeexplore.ieee.org/document/8493477/ |
| work_keys_str_mv | AT zheshu preferenceinspiredcoevolutionaryalgorithmswithlocalpcaorientedgoalvectorsformanyobjectiveoptimization AT weipingwang preferenceinspiredcoevolutionaryalgorithmswithlocalpcaorientedgoalvectorsformanyobjectiveoptimization |