Abstract: System evaluation is one of the focuses in system engineering theory and practice research.As one main method of system evaluation in correcting judgment matrix，CAHP has a high-dimensional，multi-constrained nonlinear optimization problem.On the basis of particle swarm optimization algorithm，the thesis designs the WS small-world network with non-zero degree of node as a neighbor particle structure，adaptively adjusts population density，improves population diversity and introduces the boundary correction strategy to prevent the particles into the local optimal solution，which composes PSO in the consistency of the judgment matrix of correcting AHP.This research not only expands the application area of PSO，but also provides a new way to solve nonlinear optimization problems in system evaluation.Through analyzing an example of the sustainable use system of water resource in the lake，the results are stable and highly precise.

Key words: systematic evaluation, analytic hierarchy process, particle swarm optimization, adaptive strategy

摘要： 系统评价是系统工程理论和实践研究中的热点和难点，修正矩阵的层次分析法（CAHP）是系统评价的主要方法之一，是高维多约束的非线性优化问题。在粒子群算法的基础上，设计了节点度不为0的WS型小世界网络作为粒子的邻居结构，自适应调整种群密度，提高种群多样性，引入了边界修正策略防止粒子陷入边界局部最优解，构成了修正AHP中判断矩阵一致性的粒子群优化算法。拓展了粒子群优化算法的应用领域，同时也为系统评价中非线性优化问题的求解提供了一种新途径。通过对某区域湖泊水资源可持续利用系统实例分析表明，计算结果稳定、精度高。

关键词: 系统评价, 层次分析法, 粒子群算法, 自适应策略