Abstract: Evolutionary algorithms have demonstrated strong performance in solving constrained multi-objective optimization problems (CMOP). However, when the unconstrained Pareto front (UPF) and constrained Pareto front (CPF) do not intersect and are far apart, the evolutionary process often lacks effective differentiation. This leads to the transfer of negative individuals and a lack of diverse feasible solutions, which can hinder population convergence and overall optimization performance. To address these issues, this paper proposes a Problem-Type Guided Dynamic Knowledge Transfer Cooperative Evolutionary Algorithm (DKTCEA), which includes two phases: independent exploration and cooperative evolution. In the independent exploration phase, the main task leverages prior knowledge from the auxiliary task to navigate infeasible regions, identify the problem type, and design a differentiated evolutionary strategy for guiding population evolution in the next phase. In the cooperative evolution phase, the auxiliary task introduces an improved ε-constraint handling mechanism to enhance solution feasibility. Furthermore, an improved knowledge transfer strategy is employed to select individuals from the source task to transfer to the target task. This minimizes the transfer of negative individuals, improving population quality and enhancing the global convergence of the main task population. Compared to five state-of-the-art constrained multi-objective optimization algorithms, DKTCEA achieved 14 and 11 optimal results in Inverted Generational Distance (IGD) and Hypervolume (HV) across 23 problems in the MW and DOC test sets, respectively. Ablation experiments further validate the effectiveness of the proposed strategies.

摘要： 进化算法在求解约束多目标优化问题(CMOP)时展现出优越能力。但对于不同类型问题尤其是无约束帕累托前沿(UPF)与约束帕累托前沿(CPF)不相交且边界较远时，进化过程通常缺乏有效的差异化引导，且种群间知识转移时引入的消极个体和多样性的可行解缺失都会阻碍任务种群收敛，影响整体优化性能。为此，本文根据帕累托前沿特征重新划分了CMOP问题类型并提出了一种问题类型引导的动态知识转移协同进化算法（DKTCEA），包括独立探索和协同演化两个阶段。在独立探索阶段，主任务利用辅助任务先验知识跨过不可行区域，判断问题类型并设计了差异化进化策略为下一阶段引导种群进化做好准备。在协同演化阶段，辅助任务引入改进的ε约束处理机制提高解的可行性，并通过改进的知识转移策略从源任务确定转移到目标任务的个体，减少消极个体解的转移，提高优化种群质量并增强主任务种群的全局收敛能力。与5种最新的约束多目标优化算法相比，DKTCEA在MW和 DOC 测试集共23个问题中在逆世代距离(IGD)和超体积(HV)上分别取得14与11个最优结果，表明其所采用的进化策略和知识转移策略在解决CMOP问题上的优势，消融实验也进一步验证了各个策略的有效性。