针对低维度多目标优化问题，目前的众多多目标优化算法已能保证足够接近问题的最优前沿，并兼顾解集的收敛性和多样性。然而，大多数算法往往忽略了解的分布性。在具有不规则帕累托前沿的多目标优化问题上，解的分布越均匀，越能充分覆盖问题的最优前沿超平面，提供给决策者的选择区间也越趋于合理。文中针对解的分布性的改善问题，以SPEA2算法为主框架，在改进个体适应度计算的基础上，提出一种新的多目标优化算法CM-SPEA2。该算法首先通过不等权重层次聚类法将种群划分为若干簇；然后改进原有杂乱度计算方法，衡量个体在所属簇内的杂乱度，寻找杂乱度最低的个体作为参考点；最后，基于其他个体与参考点的Manhattan距离计算表征分布性的算子并改进适应度函数，设置适应度阈值筛选参考点附近的非支配个体，从而间接调整环境选择策略，使保留的个体分布更加均匀，进而提升收敛性和多样性。对比实验结果表明，与一些类似的多目标优化算法相比，CM-SPEA2算法在IMOP、ZDT和VNT类型的测试问题上具有一定的优势。

For low-dimensional multi-objective optimization problems, the existing multi-objective optimization algorithms have been able to ensure the proximity to the optimal front of the problem, and balance the convergence and the diversity of solution sets. However, the uniformity of the solution is ignored in most algorithms. In the multi-objective optimization problem with irregular Pareto front, the more uniform the distribution of solution is, the more the solution can reflect the true distribution of the optimal front of the problem, and the more reasonable the choices provided to decision makers. To improve the uniform distribution of solutions, a new multi-objective optimization algorithm CM-SPEA2 is proposed based on SPEA2 algorithm and the improved individual fitness calculation. In this algorithm, firstly, the initial population is divided into different clusters by means of hierarchical clustering. Next, the original calculation method of messy degree is improved to measure the messy degree of individuals in their clusters, and the individuals with the lowest messy degree are selected as reference points. Then, based on the Manhattan distance between other individuals and the reference point, the operator representing distribution is calculated and the fitness function is improved. Finally, the fitness threshold is set to screen non-dominated individuals near the reference point, so as to indirectly adjust the environmental selection strategy, make the distribution of retained individuals more uniform, thus improving the convergence and diversity. As compared with some similar multi-objective optimization algorithms, the proposed CM-SPEA2 algorithm has certain advantages in solving IMOP, ZDT and VNT test problems.