A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization Problems
The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different direct...
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| Vydáno v: | IEEE access Ročník 10; s. 72825 - 72838 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different directions. This paper gives a systematic comparison of six different components for decomposition-based algorithms, including framework analysis, weight vector generation scheme, aggregation evaluation function construction, reproduction operator, individual selection and update strategy, and the characteristics and application scope of various algorithms are also analyzed in detail in the survey. Different from previous survey on decomposition-based multi-objective evolutionary algorithms, a more detailed classification and experimental comparison are elaborated in the proposed paper. |
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| AbstractList | The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different directions. This paper gives a systematic comparison of six different components for decomposition-based algorithms, including framework analysis, weight vector generation scheme, aggregation evaluation function construction, reproduction operator, individual selection and update strategy, and the characteristics and application scope of various algorithms are also analyzed in detail in the survey. Different from previous survey on decomposition-based multi-objective evolutionary algorithms, a more detailed classification and experimental comparison are elaborated in the proposed paper. |
| Author | Guo, Xiaofang |
| Author_xml | – sequence: 1 givenname: Xiaofang orcidid: 0000-0002-8944-3400 surname: Guo fullname: Guo, Xiaofang email: gxfang1981@126.com organization: School of Science, Xi'an Technological University, Xi'an, China |
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| SubjectTerms | Convergence Decomposition evolutionary algorithm Evolutionary algorithms Evolutionary computation Many-objective Multiple objective analysis Optimization Social factors Statistics |
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| Title | A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization Problems |
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