基于重新匹配策略的ε约束多目标分解优化算法

针对MOEA/D算法中权重向量与个体分配不合理，导致种群多样性降低的问题，提出基于重新匹配策略的ε约束多目标分解优化算法.首先，对Tchebycheff分解策略进行理论分析，推导出关于多样性和收敛性的定理，从而为研究MOEA/D算法奠定理论基础.其次，为有效解决由于随机为权重向量分配个体造成种群多样性降低的问题，提出权重向量和个体间的重新匹配策略，合理地为权重向量分配个体，改善种群多样性.最后，提出的个体比较准则较好地兼顾多样性和收敛性，提高了算法的约束多目标优化性能.通过与5种优秀算法的对比实验结果表明，该文算法所求得的近似Pareto最优解集的分布性和收敛性均得到一定提高，相比于对比算法具有一定的优势.

The ε Constrained Multi-objective Decomposition Optimization Algorithm Based on Re-matching Strategy

Aiming at the problem that unreasonable distribution between weight vector and individual in MOEA/D reduce diversity,the ε constrained multi-objective decomposition optimization algorithm based on re-matching strategy is proposed.Firstly,through theoretical analysis of the chebycheff decomposition strategy,two theorems about diversity and convergence are gained,which could provide a theoretical basis for the research of MOEA/D.Secondly,in order to solve the problem of diversity reduction caused by random assignment of individual to weight vector,the re-matching strategy is presented for reasonably assigning individual to weight vector,and then diversity is improved.Finally,the suggested individual comparison criterion has a good balance between diversity and convergence,and it increases optimization performance.Comparative experiment results with five excellent algorithms show that our algorithm achieves better diversity and convergence,and our algorithm has a certain advantage.