Multi-objective pump scheduling optimisation using evolutionary strategies

Advances in Engineering Software

Volumn 36, Issue 1, 2005, Pages 39-47

  Barán, Benjamín ^a   Von Lücken, Christian ^a   Sotelo, Aldo ^a  

^a UNIVERSIDAD NACIONAL DE ASUNCIÓN   (Paraguay)

Author keywords

Evolutionary computation; Genetic algorithms; Multi objective optimisation; Pareto dominance; Pump scheduling; Water supply

Indexed keywords

ALGORITHMS; COSTS; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; MAINTENANCE; OPTIMAL SYSTEMS; PARAMETER ESTIMATION; RESERVOIRS (WATER); WATER SUPPLY;

MULTI-OBJECTIVE OPTIMIZATION; PARETO DOMINANCE; PUMP SCHEDULING; SPEA'S;

PUMPS;

EID: 9644278107     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2004.03.012     Document Type: Conference Paper

References (21)

1. Mays LW. Water distribution systems handbook.New York: McGraw-Hill; 2000.
2. Ormsbee LE, Lansey KE. Optimal control of water supply pumping systems. J Water Resour Plan Mgmt 1994;120(2).
3. Lansey KE, Awumah K. Optimal pump operations considering pump switches. J Water Resour Plan Mgmt 1994; 120(1).
4. Mackle G, Savic DA, Walters GA. Application of genetic algorithms to pump scheduling for water supply, GALESIA '95. Conference Publication 4/4, 400-405, London: Institute of Electrical Engineers; 1995.
5. Savic DA, Walters GA, Schawb M. Multi-objective Genetic Algorithms for Pump Scheduling in Water Supply. ERASMUS, Exeter, UK/Stuttgart, Germany: School of Engineering of Exeter/University of Stuttgart; 1997.
6. Schaetzen W. A genetic algorithm approach for pump scheduling in water supply systems. Exeter, UK: Water Systems group, School of Engineering, University of Exeter; 1998.
7. Zitzler E, Deb K, Thiele L, Coello C, Corne D. Evolutionary multicriterion optimization, Lecture Notes in Computer Science 1993. First International Conference, EMO 2001, Zurich, Switzerland, ISBN 3-540-41745, Berlin: Springer; 2001.
8. Sotelo A, Barán B. Pumping cost optimization in water supply systems using a multi-objective evolutionary combined algorithm. Fifteenth Chilean Conference on Hydraulic Engineering, Conceptión, Chile: University of Concepción; 2001.
9. Zitzler E, Thiele L. Multi-objective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 1999;3(4):257-71.
10. Srinivas N, Deb K. Multi-objective optimisation using non-dominated sorting genetic algorithm. Evol Comput 1994;2(3):221-48.
11. Deb K, Agrawal A, Pratab A, Meyarivan T. A Fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. Parallel problem solving from Nature VI Conference, Berlin: Springer; 2000. p. 849-58.
12. Deb K, Goel T. Controlled elitist non-dominated sorting genetic algorithms for better convergence. First International Conference on evolutionary multi-criterion optimisation, Zurich; 2001. p. 67-81.
13. Horn J, Nafpliotis N, Goldberg D. A Niched pareto genetic algorithm for multi-objective optimisation. First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, New Jersey, vol. 1.; 1994. p. 82-7.
14. Fonseca C, Fleming P. Genetic algorithms for multi-objective optimisation: formulation, discussion and generalisation. Fifth International Conference on Genetic Algorithms, California; 1993. p. 416-23.
15. Van Veldhuizen D. Multi-objective evolutionary algorithms: classification, analysis and new innovation, PhD thesis. Air Force Institute of Technology, Ohio; 1999.
16. Dolqachev FM, Pashkov NN, Hydraulics and Hydraulic Machines. Moscow, Russia: Mir; 1985.
17. Zitzler E, Thiele L. Multi-objective evolutionary algorithms: empirical results. Evol Comput 2000;8(2):173-95.
18. Coello CA. A comprehensive survey of evolutionary-based multi-objective optimization techniques. Knowledge Inform Syst 1999; 1 (3): 269-308.
19. Fonseca CM, Fleming PJ. An overview of evolutionary algorithms in multi-objective optimization. Evol Comput 1995;3(1):1-16.
20. Van Veldhuizen DA, Lamont GB. Multi-objective evolutionary algorithms: analyzing the state-of-the-art. Evol Comput 2000;8(2): 125-47.
21. Goldberg DE. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison Wesley; 1989.