A Weight Vector Bi-Objective Evolutionary Algorithm with Bi-criterion Evolution for Many-Objective Optimization
In the multi-objective optimization process, its main purpose is to obtain Pareto non-dominated solutions with well convergence and diversity, but in most cases the convergence and diversity of solutions are conflicting. In order to solve this problem, this paper proposes a new convergence and diver...
Gespeichert in:
| Veröffentlicht in: | 2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS) S. 273 - 279 |
|---|---|
| Hauptverfasser: | , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
29.07.2022
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | In the multi-objective optimization process, its main purpose is to obtain Pareto non-dominated solutions with well convergence and diversity, but in most cases the convergence and diversity of solutions are conflicting. In order to solve this problem, this paper proposes a new convergence and diversity evaluation method to convert the multi-objective optimization problem into a bi-goal, more effectively evaluate the dominant relationship between individuals through the weight vectors, and then increase the selection pressure. We introduce the bi-criterion evolution can better balance the convergence and diversity of Pareto optimal solutions. Based on the proposed method, a new multi-objective optimization algorithm BiGE-BEW is proposed. Experimental results show that the proposed algorithm shows strong competitiveness in solving multi-objective optimization problems and greatly improves the performance of algorithms for solving multi-objective optimization problems. |
|---|---|
| AbstractList | In the multi-objective optimization process, its main purpose is to obtain Pareto non-dominated solutions with well convergence and diversity, but in most cases the convergence and diversity of solutions are conflicting. In order to solve this problem, this paper proposes a new convergence and diversity evaluation method to convert the multi-objective optimization problem into a bi-goal, more effectively evaluate the dominant relationship between individuals through the weight vectors, and then increase the selection pressure. We introduce the bi-criterion evolution can better balance the convergence and diversity of Pareto optimal solutions. Based on the proposed method, a new multi-objective optimization algorithm BiGE-BEW is proposed. Experimental results show that the proposed algorithm shows strong competitiveness in solving multi-objective optimization problems and greatly improves the performance of algorithms for solving multi-objective optimization problems. |
| Author | Chen, Hanning Wang, Jiangtao |
| Author_xml | – sequence: 1 givenname: Jiangtao surname: Wang fullname: Wang, Jiangtao email: w_jiangtao0602@163.com organization: Tiangong University,School of Artificial Intelligence,Tianjin,China – sequence: 2 givenname: Hanning surname: Chen fullname: Chen, Hanning email: perfect_chn@hotmail.com organization: Tiangong University,School of Computer Science and Technology,Tianjin,China |
| BookMark | eNpNkF9LwzAUxSPog5t-Al_yBTqb_8ljLVMLkwoOfRxpdrNF2mbUOpmf3gwH-nLvPZzDD86doPM-9oAQJvmMkNzcVuVzVb4IQSWf0ZzSmdGK6VydoQmRUnCpkrxEscBvEDbbEb-CG-OA70JWN-_pDnvA831sP8cQezsccNFu4hDGbYe_0jwGXZIwJPsviH1iPNn-8I9S78bQhW979K_QhbftB1yf9hQt7-fL8jFb1A9VWSyywDXJ1koZBsQ1RBgvNFeEa2e9FE56YSRXwlNOndFa2DU4o6xvOGPSSgeMgGRTdPOLDQCw2g2hSw1WpxewH7VUWNY |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICPICS55264.2022.9873807 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 1665467738 9781665467735 |
| EndPage | 279 |
| ExternalDocumentID | 9873807 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i481-d7793e1cb159f5847148caf65c6f596475f242c9885adec97afb4336a6ce31e63 |
| IEDL.DBID | RIE |
| IngestDate | Wed Aug 27 02:51:44 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i481-d7793e1cb159f5847148caf65c6f596475f242c9885adec97afb4336a6ce31e63 |
| PageCount | 7 |
| ParticipantIDs | ieee_primary_9873807 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-July-29 |
| PublicationDateYYYYMMDD | 2022-07-29 |
| PublicationDate_xml | – month: 07 year: 2022 text: 2022-July-29 day: 29 |
| PublicationDecade | 2020 |
| PublicationTitle | 2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS) |
| PublicationTitleAbbrev | ICPICS |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.8020458 |
| Snippet | In the multi-objective optimization process, its main purpose is to obtain Pareto non-dominated solutions with well convergence and diversity, but in most... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 273 |
| SubjectTerms | bi-criterion evolution Bi-goal evolution Convergence convergence and diversity estimation Diversity methods Estimation Evolutionary computation many-objective optimization multi-objective optimization Optimization Pareto optimization |
| Title | A Weight Vector Bi-Objective Evolutionary Algorithm with Bi-criterion Evolution for Many-Objective Optimization |
| URI | https://ieeexplore.ieee.org/document/9873807 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JTwIxFG6AePCkBox7evBoYbZuRyQQOQgkEuVG2k6rGJkxCCT-e1-HETTx4q1pXtqky1u-9nsPoWsWOQVHQRHDuSYJjRURJpVEgbeguUgsTV1RbIIPBmIykaMKutlyYay1xecz2_TN4i0_zc3KQ2UtiI99fvQqqnLONlyt7885gWz1O6N-54FSMPEQ90VRsxT_VTelMBu9g_9NeIgaO_4dHm0tyxGq2KyO8jZ-KnBM_Fgg7fh2Rob6daOwcHddniG1-MTtt-ccgv6XOfYwqxcE3eCTMufZThCDu4rvQRf8GGUICmReMjMbaNzrjjt3pCyXQGaJCEnK4arZ0GhwUJx__IRAxyjHqGGOer4pdWCOjRSCqtQayZXTSRwzxYyNQ8viY1TL8syeIJxqKwKjqKNhnGgpVWyEAUfDBloyE9BTVPdrNX3fJMSYlst09nf3Odr32-EB0UheoNpysbKXaM-sl7OPxVWxi19PnqIA |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JTwIxFH5BNNGTGjDu9uDRwmydaY9IIBDZEolyI22nRYzMGAQS_73tMIImXrw1TZeky1u-9nsP4Db0NDdHgWMZRQIHxOeYyphhbqwFEdFAkVhnySaiXo-ORmxQgLsNF0YplX0-UxVbzN7y41QuLVRWNf6xjY--A7s2c1bO1vr-nuOwars-aNcfCTFK3nh-nlfJO_zKnJIpjubh_6Y8gvKWgYcGG91yDAWVlCCtoecMyURPGdaO7qe4L17XIgs1Vvkp4vNPVHubpMbtf5khC7TahkY62LDMabJtiIzBirpGGvwYpW9EyCznZpZh2GwM6y2cJ0zA04C6OI7MZVOuFMZE0fb507g6kuuQyFATyzgl2ihkySglPFaSRVyLwPdDHkrluyr0T6CYpIk6BRQLRR3JiSauHwjGuC-pNKaGcgQLpUPOoGTXavy-Dokxzpfp_O_qG9hvDbudcafde7iAA7s1Fh712CUUF_OluoI9uVpMP-bX2Y5-Aer7pUk |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2022+IEEE+4th+International+Conference+on+Power%2C+Intelligent+Computing+and+Systems+%28ICPICS%29&rft.atitle=A+Weight+Vector+Bi-Objective+Evolutionary+Algorithm+with+Bi-criterion+Evolution+for+Many-Objective+Optimization&rft.au=Wang%2C+Jiangtao&rft.au=Chen%2C+Hanning&rft.date=2022-07-29&rft.pub=IEEE&rft.spage=273&rft.epage=279&rft_id=info:doi/10.1109%2FICPICS55264.2022.9873807&rft.externalDocID=9873807 |