一种用于动态多目标优化的特殊点骨架重构算法
A Special Point Skeleton Reconstruction Algorithm for Dynamic Multiobjective Optimization
中文总结 AI 辅助
针对现有动态多目标优化算法未充分挖掘代表性解结构关系的问题，提出SPSR-DMOEA算法，通过构建预测骨架并按比例分配个体等方式提升动态跟踪能力，在DF基准套件上验证了有效性。
中文摘要 AI 辅助
针对现有动态多目标优化算法在环境变化后主要依赖个体迁移或独立特殊点采样，未能充分挖掘代表性解之间结构关系的问题，本文提出一种基于特殊点骨架重构的动态多目标进化算法（SPSR-DMOEA）。首先，从当前环境的帕累托最优解集中提取质心、膝点和极值点，并根据这些点在连续环境间的移动速度自适应预测它们在新环境中的位置。随后，在决策空间中，将质心与其他锚点连接，并在非质心锚点间构建最小生成树，从而建立可描述整体种群结构的预测骨架。根据骨架边的长度按比例分配分配到每条边的个体数量，沿每条边均匀生成候选解，并引入随机正交扰动以扩大骨架周围的搜索区域。在DF动态多目标基准测试套件上的实验结果表明，所提方法在动态跟踪能力方面具有有效性。
英文摘要
To address the issue that existing dynamic multi-objective optimization algorithms mainly rely on individual migration or independent special point sampling after environmental changes, while failing to fully exploit the structural relationships among representative solutions, a Special Point Skeleton Reconstruction based Dynamic Multi-Objective Evolutionary Algorithm (SPSR-DMOEA) is proposed. First, the centroid, knee points, and extreme points are extracted from the Pareto optimal solution set of the current environment, and their positions in the new environment are adaptively predicted according to their movement velocities across consecutive environments. Subsequently, in the decision space, the centroid is connected with other anchor points, and a minimum spanning tree is constructed among the non-centroid anchor points, thereby establishing a prediction skeleton capable of describing the overall population structure. According to the lengths of the skeleton edges, the number of individuals allocated to each edge is determined proportionally. Candidate solutions are uniformly generated along each edge, and random orthogonal perturbations are introduced to expand the search region around the skeleton. Experimental results on the DF dynamic multi-objective benchmark suite demonstrate the effectiveness of the proposed method in dynamic tracking capability.
