Developing New Algorithm and Its Application on Optimal Control of Pumps in Water Distribution Network
In recent years, new techniques for solving complex
problems in engineering are proposed. One of these techniques is
JPSO algorithm. With innovative changes in the nature of the jump
algorithm JPSO, it is possible to construct a graph-based solution
with a new algorithm called G-JPSO. In this paper, a new algorithm
to solve the optimal control problem Fletcher-Powell and optimal
control of pumps in water distribution network was evaluated.
Optimal control of pumps comprise of optimum timetable operation
(status on and off) for each of the pumps at the desired time interval.
Maximum number of status on and off for each pumps imposed to the
objective function as another constraint. To determine the optimal
operation of pumps, a model-based optimization-simulation
algorithm was developed based on G-JPSO and JPSO algorithms.
The proposed algorithm results were compared well with the ant
colony algorithm, genetic and JPSO results. This shows the
robustness of proposed algorithm in finding near optimum solutions
with reasonable computational cost.
[1] O. Yeniay, “A comparative study on optimization methods for the
constrained nonlinear programming problems,” Mathematical Problems
in Engineering. 2, pp.165–173, 2005.
[2] K. S. Lee, Z.W. Geem, S. H. Lee and K.W. Bae, “The harmony search
heuristic algorithm for discrete structural optimization,” Eng. Optim. 37,
pp. 663–684, 2005.
[3] H. Afshar and R. Rajabpourr, “Application of Local and Global Particle
Swarm Optimization Algorithms to Optimal Design and Operation of
Irrigation Pumping Systems,” Irrig. and Drain. 58(3), pp. 321-331, 2009.
[4] G. Mackle, D. A. Savic, and G. A. Walters, “Application of genetic
algorithms to pump scheduling for water supply,” GALESIA, 95.
London: Institute of Electrical Engineers Conference Publication 4(4):
pp. 400-405, 1995.
[5] S. I. Rodin and M. Moradi-Jalal, “Use of genetic algorithm in
optimization of irrigation pumping stations. WAPIRRA program, 2002.
[6] M. Moradi-Jalal, M. A. Marino and A. Afshar, “Optimal design and
operation of irrigation pumping station,” J. Irrig. Drain. Eng. 129(3): pp.
149-154, 2003.
[7] R. Rajabpourr and M.H. Afshar, “Optimized Operation of Serial Pump
Stations Using the PSO Algorithm,” Journal of Water & Wastewater.,
66, pp.56-66, 2008 (In Persian).
[8] J. E. Van Zyl, D. A. Savic, and G. A. Walters, “Operational optimization
of water distribution systems using a hybrid genetic algorithm,” J. Water
Resour. Plann. Manage., 130(2), pp. 160–170, 2004.
[9] M. López-Ibáñez, T. D. Prasad and B. Paechter, “Ant Colony
Optimization for Optimal Control of Pumps in Water Distribution
Networks,” J. Water Resour. Plann. Manage. 134(4), pp. 337–346, 2008.
[10] O. Bozorg Haddad and, M.A. Marino, “Dynamic penalty function as a
strategy in solving water resources combinatorial optimization problems
with honey-bee mating optimization (HBMO) algorithm,” Journal of
Hydro informatics, 9 (3), pp.233-250, 2007.
[11] Sanda-Carmen, P. Radu, M. Andrei , “Pumping Stations Scheduling for
a Water Supply System With Multiple Tanks,” U.P.B. Sci. Bull., Series
D, 72 (3), pp.129-140,2010.
[12] S. Rasoulzadeh Gharibdosti, O. Bozorghadad, “Development and
Application of NLP-GA hybrid algorithm to optimize the design and
operation of pumping stations,” Iranian Journal of Soil and
Water Research., 43(2), pp.129-137, 2012 (In Persian).
[13] S.S. Hashemi, M. Tabesh and B. Ataee Kia, “Ant-colony optimization of
energy cost in water distribution systems using variable speed pumps,”
in: Proceedings of 4th ASCE-EWRI International Perspective on Water
Resources and The Environment, 4–6 January, National University of
Singapore, Singapore, 2011.
[14] S.S. Hashemi, M. Tabesh and B. Ataee Kia, “Scheduling and operating
costs in water distribution networks,” Water Management., 166(8), pp.
432-442, 2012.
[15] J. Kennedy and R. Eberhart, “A discrete binary version of the particle
swarm algorithm,” In: IEEE Conference on Systems, Man, and
Cybernetics, 5: pp. 4104-4108, 1997.
[16] S. Yang, M. Wang and L. Jiao, “A Quantum Particle Swarm
Optimization,” In: Proceedings of CEC2004, the Congress on
Evolutionary Computing, 1, pp. 320-324, 2004.
[17] B. Al-kazemi and C.K. Mohan, “Multi-phase Discrete Particle Swarm
Optimization,” Fourth International Workshop on Frontiers in
Evolutionary Algorithms, Kinsale, Ireland, 2002.
[18] J.A. Moreno-Perez, J.P. Castro-Gutierrez, F.J. Martinez-Garcia, B.
Melian, J.M. Moreno-Vega, and J. Ramos, “Discrete Particle Swarm
Optimization for the p-median problem”. In: Proceedings of the 7th
Meta heuristics International Conference, Montreal, Canada, 2007.
[19] R. Atkinson, J.E. Van Zyl, G.A. Walters and D.A. Savic, “Genetic
algorithm optimization of level-controlled pumping station operation.
Proc,” Water Network Modeling for Optimal Design and Management,
Centre for Water Systems, Exeter, U.K., pp. 79–90, 2000.
[1] O. Yeniay, “A comparative study on optimization methods for the
constrained nonlinear programming problems,” Mathematical Problems
in Engineering. 2, pp.165–173, 2005.
[2] K. S. Lee, Z.W. Geem, S. H. Lee and K.W. Bae, “The harmony search
heuristic algorithm for discrete structural optimization,” Eng. Optim. 37,
pp. 663–684, 2005.
[3] H. Afshar and R. Rajabpourr, “Application of Local and Global Particle
Swarm Optimization Algorithms to Optimal Design and Operation of
Irrigation Pumping Systems,” Irrig. and Drain. 58(3), pp. 321-331, 2009.
[4] G. Mackle, D. A. Savic, and G. A. Walters, “Application of genetic
algorithms to pump scheduling for water supply,” GALESIA, 95.
London: Institute of Electrical Engineers Conference Publication 4(4):
pp. 400-405, 1995.
[5] S. I. Rodin and M. Moradi-Jalal, “Use of genetic algorithm in
optimization of irrigation pumping stations. WAPIRRA program, 2002.
[6] M. Moradi-Jalal, M. A. Marino and A. Afshar, “Optimal design and
operation of irrigation pumping station,” J. Irrig. Drain. Eng. 129(3): pp.
149-154, 2003.
[7] R. Rajabpourr and M.H. Afshar, “Optimized Operation of Serial Pump
Stations Using the PSO Algorithm,” Journal of Water & Wastewater.,
66, pp.56-66, 2008 (In Persian).
[8] J. E. Van Zyl, D. A. Savic, and G. A. Walters, “Operational optimization
of water distribution systems using a hybrid genetic algorithm,” J. Water
Resour. Plann. Manage., 130(2), pp. 160–170, 2004.
[9] M. López-Ibáñez, T. D. Prasad and B. Paechter, “Ant Colony
Optimization for Optimal Control of Pumps in Water Distribution
Networks,” J. Water Resour. Plann. Manage. 134(4), pp. 337–346, 2008.
[10] O. Bozorg Haddad and, M.A. Marino, “Dynamic penalty function as a
strategy in solving water resources combinatorial optimization problems
with honey-bee mating optimization (HBMO) algorithm,” Journal of
Hydro informatics, 9 (3), pp.233-250, 2007.
[11] Sanda-Carmen, P. Radu, M. Andrei , “Pumping Stations Scheduling for
a Water Supply System With Multiple Tanks,” U.P.B. Sci. Bull., Series
D, 72 (3), pp.129-140,2010.
[12] S. Rasoulzadeh Gharibdosti, O. Bozorghadad, “Development and
Application of NLP-GA hybrid algorithm to optimize the design and
operation of pumping stations,” Iranian Journal of Soil and
Water Research., 43(2), pp.129-137, 2012 (In Persian).
[13] S.S. Hashemi, M. Tabesh and B. Ataee Kia, “Ant-colony optimization of
energy cost in water distribution systems using variable speed pumps,”
in: Proceedings of 4th ASCE-EWRI International Perspective on Water
Resources and The Environment, 4–6 January, National University of
Singapore, Singapore, 2011.
[14] S.S. Hashemi, M. Tabesh and B. Ataee Kia, “Scheduling and operating
costs in water distribution networks,” Water Management., 166(8), pp.
432-442, 2012.
[15] J. Kennedy and R. Eberhart, “A discrete binary version of the particle
swarm algorithm,” In: IEEE Conference on Systems, Man, and
Cybernetics, 5: pp. 4104-4108, 1997.
[16] S. Yang, M. Wang and L. Jiao, “A Quantum Particle Swarm
Optimization,” In: Proceedings of CEC2004, the Congress on
Evolutionary Computing, 1, pp. 320-324, 2004.
[17] B. Al-kazemi and C.K. Mohan, “Multi-phase Discrete Particle Swarm
Optimization,” Fourth International Workshop on Frontiers in
Evolutionary Algorithms, Kinsale, Ireland, 2002.
[18] J.A. Moreno-Perez, J.P. Castro-Gutierrez, F.J. Martinez-Garcia, B.
Melian, J.M. Moreno-Vega, and J. Ramos, “Discrete Particle Swarm
Optimization for the p-median problem”. In: Proceedings of the 7th
Meta heuristics International Conference, Montreal, Canada, 2007.
[19] R. Atkinson, J.E. Van Zyl, G.A. Walters and D.A. Savic, “Genetic
algorithm optimization of level-controlled pumping station operation.
Proc,” Water Network Modeling for Optimal Design and Management,
Centre for Water Systems, Exeter, U.K., pp. 79–90, 2000.
@article{"International Journal of Architectural, Civil and Construction Sciences:71066", author = "R. Rajabpour and N. Talebbeydokhti and M. H. Ahmadi", title = "Developing New Algorithm and Its Application on Optimal Control of Pumps in Water Distribution Network", abstract = "In recent years, new techniques for solving complex
problems in engineering are proposed. One of these techniques is
JPSO algorithm. With innovative changes in the nature of the jump
algorithm JPSO, it is possible to construct a graph-based solution
with a new algorithm called G-JPSO. In this paper, a new algorithm
to solve the optimal control problem Fletcher-Powell and optimal
control of pumps in water distribution network was evaluated.
Optimal control of pumps comprise of optimum timetable operation
(status on and off) for each of the pumps at the desired time interval.
Maximum number of status on and off for each pumps imposed to the
objective function as another constraint. To determine the optimal
operation of pumps, a model-based optimization-simulation
algorithm was developed based on G-JPSO and JPSO algorithms.
The proposed algorithm results were compared well with the ant
colony algorithm, genetic and JPSO results. This shows the
robustness of proposed algorithm in finding near optimum solutions
with reasonable computational cost.", keywords = "G-JPSO, operation, optimization, pumping station,
water distribution networks.", volume = "9", number = "9", pages = "1238-4", }
