Particle swarm optimization easily falls into a local optimum when solving water supply optimization prob-lems. In order to solve this weakness, by analyzing particle trajectories and the similarity of particles, this paper proposes a parameter adaptive particle swarm optimization ( PAPSO) . By estimating the degree of similarity between particles and expected particles, the algorithm dynamically adjusts parameters and balances the global and local search ability. The algorithm uses the variation strategy of staging to increase the population diversity and ensure that it converges to the global optimum. The tower of Hanoi network and New York network have been optimized by the improved algorithm, and the result shows that the PAPSO algorithm can be effectively applied to the combinato-rial optimization of water supply pipeline networks. The proposed algorithm has been applied to optimize an actual pipe network reconstruction case and the result shows that the algorithm has better optimization and convergence performance.%针对粒子群算法在解决给水管网优化问题时存在容易陷入局部最优的缺点,通过分析粒子的运动轨迹和相似程度,提出一种参数自适应粒子群算法. 该算法利用种群粒子与期望粒子之间相似度的大小,动态调整算法参数,平衡算法全局和局部搜索能力,利用分期变异策略增加种群多样性,保证算法收敛于全局最优值. 将改进算法用于优化汉诺塔管网和纽约管网2个经典的管网案例,证明算法可以有效应用于给水管网这类组合优化问题. 将该算法优化实际的管网改扩建案例,结果表明,所提出的算法具有更好的寻优性能和收敛性能.
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