The performance of traditional penalty boundary intersection (PBI) decomposition-based evolutionary algorithm is totally determined by the penalty factor. The fixed penalty factor causes the imbalance between the convergence and the diversity when solving many-objective problems. So, an adaptive decomposition evolutionary algorithm based on environmental information (MaOEA/ADEI) is proposed to solve the imbalance. The penalty factor of PBI decomposition is determined by the environmental information (include distribution information of weight vectors and population). Furthermore, the parent individual selection strategy is introduced to select promising individuals for variation and the weight vectors adaption strategy is used to handle problems with scaled objectives. Comparisons with 4 algorithms on 24 benchmark instances are used to test the property of MaOEA/ADEI. The experimental results show MaOEA/ADEI performs best on 14 test instances.

传统惩罚边界交点（PBI）分解的进化算法的性能完全取决于惩罚因子。解决多目标问题时，固定的惩罚因子会导致收敛性和多样性之间的不平衡。因此，提出了一种基于环境信息的自适应分解进化算法（MaOEA / ADEI）来解决不平衡问题。PBI分解的惩罚因子取决于环境信息（包括权重向量和种群的分布信息）。此外，引入了父母个体选择策略来选择有前途的个体进行变异，权重向量适应策略用于处理具有规模目标的问题。在24个基准实例上与4种算法进行比较，以测试MaOEA / ADEI的属性。实验结果表明，MaOEA / ADEI在14个测试实例上表现最佳。