Decomposition-based multi-objective evolutionary algorithm for bi-optimal selection

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.