Design of water distribution networks using accelerated momentum particle swarm optimisation technique

被引:7
作者
Aghdam, Kazem Mohammadi [1 ]
Mirzaee, Iraj [1 ]
Pourmahmood, Nader [2 ]
Aghababa, Mohammad Pourmahmood [3 ]
机构
[1] Urmia Univ Technol, Dept Mech Engn, Orumiyeh, Iran
[2] Urmia Univ, Dept Mech Engn, Orumiyeh, Iran
[3] Urmia Univ Technol, Dept Elect Engn, Orumiyeh, Iran
关键词
accelerated velocity; momentum coefficient; water distribution network; hydraulic condition; particle swarm optimisation; ANT COLONY OPTIMIZATION; ALGORITHM; EVOLUTION;
D O I
10.1080/0952813X.2013.863227
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Optimisation of looped water distribution networks (WDNs) has been recognised as an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical optimisation techniques. This article proposes the use of a new version of heuristic particle swarm optimisation (PSO) for solving this problem. In order to increase the convergence speed of the original PSO algorithm, some accelerated parameters are introduced to the velocity update equation. Furthermore, momentum parts are added to the PSO position updating formula to get away from trapping in local optimums. The new version of the PSO algorithm is called accelerated momentum particle swarm optimisation (AMPSO). The proposed AMPSO is then applied to solve WDN design problems. Some illustrative and comparative illustrative examples are presented to show the efficiency of the introduced AMPSO compared with some other heuristic algorithms.
引用
收藏
页码:459 / 475
页数:17
相关论文
共 28 条
[11]  
Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
[12]   Ant colony optimization distribution for design of water systems [J].
Maier, HR ;
Simpson, AR ;
Zecchin, AC ;
Foong, WK ;
Phang, KY ;
Seah, HY ;
Tan, CL .
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE, 2003, 129 (03) :200-209
[13]   Considering direct interaction of artificial ant colony foraging simulation and animation [J].
Meng, Zhigang ;
Zou, Beiji ;
Zeng, Yu .
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2012, 24 (01) :95-107
[14]   No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms [J].
Moradinasab, Nazanin ;
Shafaei, Rasoul ;
Rabiee, Meysam ;
Ramezani, Pezhman .
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2013, 25 (02) :207-225
[15]   Quantifying the exploration performed by metaheuristics [J].
Pellegrini, Paola ;
Favaretto, Daniela .
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2012, 24 (02) :247-266
[16]   Application of several meta-heuristic techniques to the optimization of real looped water distribution networks [J].
Reca, J. ;
Martinez, J. ;
Gil, C. ;
Banos, R. .
WATER RESOURCES MANAGEMENT, 2008, 22 (10) :1367-1379
[17]  
Riget J., 2002, 20022 ARPSO EVALIFE
[18]   An evolutionary algorithm for abductive reasoning [J].
Romdhane, L. B. ;
Ayeb, B. .
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2011, 23 (04) :529-544
[19]  
Rossman L. A., 2000, REPORTS
[20]  
Schaake J., 1969, 116 MIT