UNASSIGNED: In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.
UNASSIGNED: The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.
UNASSIGNED: The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.
■在TS-MOEA中,一个两阶段的框架,包括早期和晚期,是为了防止算法停滞。此外,这两个阶段集中在不同的多目标问题模型上,从而平衡TS-MOEA中的趋同和种群多样性。受通道相关矩阵稀疏性的启发,稀疏初始化运算符,它对决策变量使用基于领域知识的分数分配策略,被引入以生成初始种群。此外,利用基于分数的变异算子来提高TS-MOEA的搜索效率。
■使用基于62通道EEG的脑机接口系统评估了TS-MOEA和其他五种最先进的多目标算法的性能,用于疲劳检测任务,结果证明了TS-MOEA的有效性。
■提出的两阶段框架可以帮助TS-MOEA摆脱停滞,并促进多样性和收敛性之间的平衡。综合信道相关矩阵的稀疏性和问题域知识可以有效降低TS-MOEA的计算复杂度,同时提高其优化效率。