参数自适应粒子群算法的给水管网优化研究

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Title:

An parameter adaptive particle swarm optimization foroptimal design of water supply systems

作者:

王超^1,2, 乔俊飞^1,2

1. 北京工业大学 电子信息与控制工程学院, 北京 100124;
2. 北京工业大学 计算智能与智能系统北京市重点实验室, 北京 100124

Author(s):

WANG Chao^1,2, QIAO Junfei^1,2

1. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing University of Technology, Beijing 100124, China

关键词:

给水管网系统; 粒子轨迹; 相似度; 参数调整; 自适应粒子群

Keywords:

water supply system; particle trajectories; similarity; parameter adjustment; adaptive particle swarm op-timization

分类号:

TP18

DOI:

10.11992/tis.201410036

文献标志码:

A

摘要:

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。

Abstract:

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.