Dynamic transfer reference point oriented MOEA/D involving local objective-space knowledge
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Xie, Yingbo | |
| dc.contributor.author | Yang, Shengxiang | |
| dc.contributor.author | Wang, Ding | |
| dc.contributor.author | Qiao, Junfei | |
| dc.contributor.author | Yin, Baocai | |
| dc.date.acceptance | 2022-01 | |
| dc.date.accessioned | 2022-02-01T15:57:56Z | |
| dc.date.available | 2022-02-01T15:57:56Z | |
| dc.date.issued | 2022-01 | |
| dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. | en |
| dc.description.abstract | The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has attained excellent performance in solving optimization problems involving multiple conflicting objectives. However, the Pareto optimal front (POF) of many multi-objective optimization problems (MOPs) has irregular properties, which weakens the performance of MOEA/D. To address this issue, we devise a dynamic transfer reference point oriented MOEA/D with local objective-space knowledge (DTR-MOEA/D). The design principle is based on three original and rigorous mechanisms. First, the individuals are projected onto a line segment (two-objective case) or a three-dimensional plane (three-objective case) after being normalized in the objective space. The line segment or the plane is divided into three different regions: the central region, the middle region, and the edge region. Second, a dynamic transfer criterion of reference point is developed based on population density relationships in different regions. Third, a strategy of population diversity enhancement guided by local objective-space knowledge is adopted to improve the diversity of the population. Finally, the experimental results conducted on sixteen benchmark MOPs and eight modified MOPs with irregular POF shapes verify that the proposed DTR-MOEA/D has attained a strong competitiveness compared with other representative algorithms. | en |
| dc.funder | Other external funder (please detail below) | en |
| dc.funder.other | National Natural Science Foundation of China | en |
| dc.identifier.citation | Y. Xie, S. Yang, D. Wang, J. Qiao, and B. Yin. (2022) Dynamic transfer reference point oriented MOEA/D involving local objective-space knowledge. IEEE Transactions on Evolutionary Computation, | en |
| dc.identifier.doi | https://doi.org/10.1109/TEVC.2022.3140265 | |
| dc.identifier.uri | https://hdl.handle.net/2086/21656 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | 61773373, 61890930-5, 61533002, 62021003 | en |
| dc.publisher | IEEE Press | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Multi-objective optimization | en |
| dc.subject | decomposition | en |
| dc.subject | dynamic transfer reference point | en |
| dc.subject | local objective space | en |
| dc.title | Dynamic transfer reference point oriented MOEA/D involving local objective-space knowledge | en |
| dc.type | Article | en |