[1]孟勤超,杨翠丽,乔俊飞.基于改进SPEA2算法的给水管网多目标优化设计[J].智能系统学报,2018,13(01):118-124.[doi:10.11992/tis.201701012]
　MENG Qinchao,YANG Cuili,QIAO Junfei.Multi-objective optimization design of water distribution systems based on improved SPEA2 algorithm[J].CAAI Transactions on Intelligent Systems,2018,13(01):118-124.[doi:10.11992/tis.201701012]
点击复制

基于改进SPEA2算法的给水管网多目标优化设计(/HTML)
分享到：

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年01期
页码:
118-124
栏目:
学术论文—智能系统
出版日期:
2018-01-24

文章信息/Info

Title:
Multi-objective optimization design of water distribution systems based on improved SPEA2 algorithm
作者:
孟勤超1; 2;  杨翠丽1; 2;  乔俊飞1; 2
1. 北京工业大学 信息学部, 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):
MENG Qinchao1; 2;  YANG Cuili1; 2;  QIAO Junfei1; 2
1. Faculty of Information Technology, 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; 多目标优化; 强度帕累托进化算法; 参考向量; 经济性; 可靠性; 双环管网; 纽约管网
Keywords:
water distribution system; multi-objective optimization; strength Pareto evolutionary algorithm 2; reference vectors; economy; reliability; two-loop network; New York tunnels network
分类号:
TP18
DOI:
10.11992/tis.201701012
摘要:
针对给水管网多目标优化设计问题，将管网造价、节点富余水头总和以及节点富余水头方差设为目标函数，从经济性和可靠性两方面对给水管网进行优化。为了获取多样性和收敛性好的解，本文结合选择机制中支配和分解的思想，引入参考向量到强度帕累托进化算法（strength Pareto evolutionary algorithm 2, SPEA2）中，配合支配强度进行解的选择。通过双环管网和纽约管网两个管网案例，仿真结果表明了所提算法在解决管网多目标优化设计问题上的有效性，并最终应用于实际的管网工程建设中。
Abstract:
To solve the multi-objective optimization problem in water distribution system design, we consider three objective functions-the cost of the pipe network and the sum and variance of the node surplus head. Then, we optimize the water distribution system in terms of economy and reliability. To obtain well-diversified and well-convergent solutions, we combine the concepts of domination and decomposition and introduce reference vectors into the Strength Pareto Evolutionary Algorithm 2 (SPEA2). The diversity and convergence of the algorithm are increased by the use of the domination strength-based solutions selection method. We use the proposed algorithm to optimize the two-loop network and the New York Tunnels network, and the simulation results demonstrate its effectiveness in realizing the multi-objective optimization of water distribution systems design. Finally, we apply the algorithm to actual pipe network construction.

参考文献/References:

[1] ALPEROVITS E, SHAMIR U. Design of optimal water distribution systems[J]. Water resources research, 1977, 13(6): 885-900.
[2] MURPHY L J, SIMPSON A R, DANDY G C. Design of a pipe network using genetic algorithms[J]. Water-melbourne then artarmon, 1993, 20(4): 40-42.
[3] MORGAN D R, GOULTER I C. Optimal urban water distribution design[J]. Water resources research, 1985, 21(5): 642-652.
[4] HALHAL D, WALTERS G A, OUAZAR D, et al. Water network rehabilitation with structured messy genetic algorithm[J]. Journal of water resources planning and management, 1997, 123(3): 137-146.
[5] MONTALVO I, IZQUIERDO J, SCHWARZE S, et al. Multi-objective particle swarm optimization applied to water distribution systems design: an approach with human interaction[J]. Mathematical and computer modelling, 2010, 52(7/8): 1219-1227.
[6] LIU Haixing, YUAN Yixing, ZHAO Ming, et al. Hybrid multi-objective genetic algorithm for optimal design of water supply network[C]//Proceedings of the 12th Annual Conference on Water Distribution Systems Analysis. Tucson, United States, 2010: 899-908.
[7] 刘书明, 李明明, 王欢欢, 等. 基于NSGA-Ⅱ算法的给水管网多目标优化设计[J]. 中国给水排水, 2015, 31(5): 50-53.
LIU Shuming, LI Mingming, WANG Huanhuan, et al. Multi-objective optimization design of water distribution system based on non-dominated sorting genetic algorithm-Ⅱ[J]. China water & wastewater, 2015, 31(5): 50-53.
[8] 蒋怀德. 给水管网多目标优化设计[D]. 上海：同济大学, 2007, 5–7.
JIANG Huaide. Multi-objective optimization design of urban water distribution networks[D]. Shanghai: Tongji University, 2007, 5–7.
[9] LI Mingming, LIU Shuming, ZHANG Ling, et al. Non-dominated sorting genetic algorithms-iibased on multi-objective optimization model in the water distribution system[J]. Procedia engineering, 2012, 37: 309-313.
[10] TOLSON B A, MAIER H R, SIMPSON A R, et al. Genetic algorithms for reliability-based optimization of water distribution systems[J]. Journal of water resources planning and management, 2004, 130(1): 63-72.
[11] ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength Pareto evolutionary algorithm: TIK-Report 103[R]. Swiss: Swiss Federal Institute of Technology, 2001.
[12] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE transactions on evolutionary computation, 2002, 6(2): 182-197.
[13] ZHANG Qingfu, LI Hui. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE transactions on evolutionary computation, 2007, 11(6): 712-731.
[14] DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints[J]. IEEE transactions on evolutionary computation, 2014, 18(4): 577-601.
[15] PRASAD T D, PARK N S. Multiobjective genetic algorithms for design of water distribution networks[J]. Journal of water resources planning and management, 2004, 130(1): 73-82.
[16] JIANG Siwei, CAI Zhihua, ZHANG Jie, et al. Multiobjective optimization by decomposition with Pareto-adaptive weight vectors[C]//International Conference on Natural Computation, Icnc 2011, Shanghai, China, 26-28 July. DBLP, 2011: 1260-1264.
[17] SEDKI A, OUAZAR D. Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems[J]. Advanced engineering informatics, 2012, 26(3): 582-591.
[18] MA Xiaoliang, LIU Fang, QI Yutao, et al. A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables[J]. IEEE transactions on evolutionary computation, 2016, 20(2): 275-298.
[19] TANG Lixin, WANG Xianpeng. A hybrid multiobjective evolutionary algorithm for multiobjective optimization problems[J]. IEEE transactions on evolutionary computation, 2013, 17(1): 20-45.
[20] JIANG Shouyong, YANG Shengxing. Evolutionary dynamic multiobjective optimization: benchmarks and algorithm comparisons[J]. IEEE transactions on cybernetics, 2017, 47(1): 198-211.

相似文献/References:

[1]王 艳,曾建潮.多目标微粒群优化算法综述[J].智能系统学报,2010,5(05):377.[doi:10.3969/j.issn.1673-4785.2010.05.001]
　WANG Yan,ZENG Jian-chao.A survey of a multiobjective particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2010,5(01):377.[doi:10.3969/j.issn.1673-4785.2010.05.001]
[2]陶新民,徐鹏,刘福荣,等.组合分布估计和差分进化的多目标优化算法[J].智能系统学报,2013,8(01):39.[doi:10.3969/j.issn.1673-4785.201208035]
　TAO Xinmin,XU Peng,LIU Furong,et al.Multi objective optimization algorithm composed of estimation of distribution and differential evolution[J].CAAI Transactions on Intelligent Systems,2013,8(01):39.[doi:10.3969/j.issn.1673-4785.201208035]
[3]王超,乔俊飞.参数自适应粒子群算法的给水管网优化研究[J].智能系统学报,2015,10(5):722.[doi:10.11992/tis.201410036]
　WANG Chao,QIAO Junfei.An parameter adaptive particle swarm optimization foroptimal design of water supply systems[J].CAAI Transactions on Intelligent Systems,2015,10(01):722.[doi:10.11992/tis.201410036]
[4]秦全德,程适,李丽,等.人工蜂群算法研究综述[J].智能系统学报,2014,9(02):127.[doi:10.3969/j.issn.1673-4785.201309064]
　QIN Quande,CHENG Shi,LI Li,et al.Artificial bee colony algorithm: a survey[J].CAAI Transactions on Intelligent Systems,2014,9(01):127.[doi:10.3969/j.issn.1673-4785.201309064]
[5]张伟,乔俊飞.神经网络的污水处理过程多目标优化控制方法[J].智能系统学报,2016,11(5):594.[doi:10.11992/tis.201512022]
　ZHANG Wei,QIAO Junfei.Multi-objective optimization control for wastewatertreatment processing based on neural network[J].CAAI Transactions on Intelligent Systems,2016,11(01):594.[doi:10.11992/tis.201512022]
[6]屠传运,陈韬伟,余益民,等.膜系统下的一种多目标优化算法[J].智能系统学报,2017,12(05):678.[doi:10.11992/tis.201706013]
　TU Chuanyun,CHEN Taowei,YU Yimin,et al.Multi-objective optimization algorithm based on membrane system[J].CAAI Transactions on Intelligent Systems,2017,12(01):678.[doi:10.11992/tis.201706013]
[7]林燕清,傅仰耿.基于NSGA-II的扩展置信规则库激活规则多目标优化方法[J].智能系统学报,2018,13(03):422.[doi:10.11992/tis.201710012]
　LIN Yanqing,FU Yanggeng.NSGA-II-based EBRB rules activation multi-objective optimization[J].CAAI Transactions on Intelligent Systems,2018,13(01):422.[doi:10.11992/tis.201710012]
[8]柳强,焦国帅.基于Kriging模型和NSGA-Ⅱ的航空发动机管路卡箍布局优化[J].智能系统学报,2019,14(2):281.[doi:10.11992/tis.201709044]
　LIU Qiang,JIAO Guoshuai.Layout optimization of aero-engine pipe clamps based on Kriging model and NSGA-Ⅱ[J].CAAI Transactions on Intelligent Systems,2019,14(01):281.[doi:10.11992/tis.201709044]
[9]钱小宇,葛洪伟,蔡明.基于目标空间分解和连续变异的多目标粒子群算法[J].智能系统学报,2019,14(3):464.[doi:10.11992/tis.201711015]
　QIAN Xiaoyu,GE Hongwei,CAI Ming.Decomposition and continuous mutation-based multi-objective particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2019,14(01):464.[doi:10.11992/tis.201711015]
[10]林锦,胡家琛,刘莞玲,等.利用MISA多目标优化的置信规则库分类算法[J].智能系统学报,2019,14(5):982.[doi:10.11992/tis.201809022]
　LIN Jin,HU Jiachen,LIU Wanling,et al.Belief rule base classification algorithm using MISA multi-objective optimization[J].CAAI Transactions on Intelligent Systems,2019,14(01):982.[doi:10.11992/tis.201809022]

备注/Memo

备注/Memo:
收稿日期:2017-01-15。
基金项目:国家自然科学基金项目（61533002, 61603012）.
作者简介:孟勤超,男,1993年生,硕士研究生,主要研究方向为智能优化算法及其应用;杨翠丽,女,1986年生,讲师,博士研究生,主要研究方向为进化算法和智能信息处理。发表学术论文10余篇,其中SCI检索7篇,EI检索12篇;乔俊飞,男,1968年生,教授,博士生导师,国家杰出青年基金获得者,教育部新世纪优秀人才,北京市精品课程负责人,主要研究方向为智能信息处理、智能优化控制。近5年在Automatica、IEEE Transactionson Control Systems Technology、Journal of Process Control、Control Engineering Practice、自动化学报及电子学报等刊物上发表学术论文近70篇,被SCI收录15篇。获教育部科技进步奖一等奖和北京市科学技术奖三等奖各1项,获得授权国家发明专利12项。
通讯作者:孟勤超.E-mail:qinchaomeng@foxmail.com.
更新日期/Last Update: 2018-02-01