.png){kind=icon}
.png){kind=icon}
Return
Decomposition-based multi-objective evolutionary algorithm for bi-optimal selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2025.102228.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Decomposition-based multi-objective evolutionary algorithms often suffer from premature convergence when dealing with complex Pareto fronts. To address this issue, this paper proposes a decomposition-based bi-optional optimization algorithm (MOEA/D-BOS). The proposed method integrates the SPEA-II selection mechanism and an individual exploration strategy into the MOEA/D framework to enhance population diversity and information retention. In addition, a new weight vector generation method and a scalarization function are designed to improve the uniformity of solution distribution. Benchmark experiments demonstrate that, compared with several advanced algorithms, MOEA/D-BOS achieves superior performance in terms of both convergence speed and population diversity.
Keywords:
Multi-objective optimization
SPEA-II selection
Individual exploration
Particle update method
Weight vector
Journal
IF:
8.5
Papers:
2.1K
Citations:
1.0W
View more organizations