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Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks

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Abstract

In last two decades, multiobjective evolutionary algorithms (MOEAs) have shown their merit for solving different optimization problems within the context of water resources and environmental engineering. MOEAs mainly use the concept of Pareto dominance for obtaining the trade-off solutions considering different criteria. A new alternative method for solving multiobjective problems is multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses scalarizing the objective functions. In this paper, decomposition strategies are developed for the large-scale water distribution network (WDN) design problems by integrating the concepts of harmony search (HS) and genetic algorithm (GA) within the MOEA/D framework. The proposed algorithms are then compared with two well-known non-dominance based MOEAs: NSGA2 and SPEA2 across four different WDN design problems. Experimental results show that MOEA/D outperform the Pareto dominance methods in terms of both non-domination and diversity criteria. MOEA/D-HS in particular could provide very high quality solutions with a uniform distribution along the Pareto front preserving the diversity and dominating the solutions of the other algorithms. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated large-scale WDN design problems.

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References

  1. Cisty M (2010) Hybrid genetic algorithm and linear programming method for least-cost design of water distribution systems. Water Resour Manag 24:1–24

  2. Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA2. KanGAL Report 200001

  3. di Pierro F, Khu S-T, Savic DA, Berardi L (2009) Efficient multi-objective optimal design of water distribution networks on a budget of simulations using hybrid algorithms. Environ Model Softw 24(2009):202–213

  4. Farmani R, Savic DA, Walters GA (2005a) Evolutionary multi-objective optimization in water distribution network design. Eng Optim 37(2):167–183

  5. Farmani R, Walters GA, Savic DA (2005b) Trade-off between total cost and reliability for Anytown water distribution network. J Water Resour Plan Manag 131(3):161–171

  6. Geem ZW (2009) Particle-swarm harmony search for water network design. Eng Optim 41(4):297–311

  7. Geem ZW, Cho Y (2011) Optimal design of water distribution networks using parameter-setting-free harmony search for two major parameters. J Water Resour Plan Manag 137(4):377–380

  8. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

  9. Haghighi A, Samani HMV, Samani ZMV (2011) GA-ILP method for optimization of water distribution networks. Water Resour Manage 25:1791–1808

  10. Jinesh Babu KS, Vijayalakshmi DP (2013) Self-adaptive PSO-GA hybrid model for combinatorial water distribution network design. J Pipeline Syst Eng Pract 4(1):57–67

  11. Kanakoudis V, Tsitsifli S, Samaras P, Zouboulis A, Demetriou G (2011) Developing appropriate performance indicators for urban water distribution systems evaluation at Mediterranean countries. Water Utility J 1:31–40

  12. Keedwell E, Khu ST (2004) Hybrid genetic algorithms for multiobjective optimisation of water distribution networks, Genetic and Evolutionary Computation Gecco 2004, Part 2, Proceedings, in Lecture notes in Computer Science 3103: 1043–1053. Springer-Verlag

  13. Keedwell E, Khu ST (2006) A novel evolutionary metaheuristic for the multiobjective optimisation of real-world water distribution networks. Eng Optim 38(3):1–18

  14. Montalvoa I, Izquierdo J, Schwarzeb S, Pérez-García R (2010) Multi-objective particle swarm optimization applied to water distribution systems design: an approach with human interaction. Math Comput Model 52(7–8):1219–1227

  15. Nicklow J, Reed P, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, Karamouz M et al (2010) State of the art for genetic algorithms and beyond in water resources planning and management. J Water Resour Plan Manag 136(4):412–432

  16. Perelman L, Ostfeld A, Salomons E (2008) Cross entropy multiobjective optimization for water distribution systems design. Water Resour Res 44(9), W09413

  17. Prasad TD, Park N-S (2004) Multiobjective genetic algorithms for design of water distribution networks. J Water Resour Plan Manag 130(1):73–82. doi:10.1061/(ASCE)0733-9496(2004)130:1(73)

  18. Raad D, Sinske A, van Vuuren J (2009) Robust multi-objective optimization for water distribution system design using a metametaheuristic. Int Trans Oper Res 16(5):595–626

  19. Reca J, Martínez J, Gil C, Baños R (2008) Application of several meta-heuristic techniques to the optimization of real looped water distribution networks. Water Resour Manage 22:1367–1379

  20. Savic DA, Walters GA, Randall-Smith M, Atkinson RM (2000) Large water distribution systems design through genetic algorithm optimization. In: Proc. of Joint Conf. on Water Resources Engineering and Water Resources Planning and Management. ASCE

  21. Sedki A, Ouazar D (2012) Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems. Adv Eng Inform 26:582–591

  22. Shirzad A, Tabesh M, Heidarzadeh M (2015) A new method for quasi-optimal design of water distribution networks. Water Resour Manage 29:5295–5308

  23. Spiliotis M (2014) A particle swarm optimization (PSO) heuristic for water distribution system analysis. Water Utility J 8:47–56

  24. Todini E (2000) Looped water distribution networks design using a resilience index based heuristic approach. Urban Water 2:115–122

  25. Tolson B, Asadzadeh AM, Maier HR, Zecchin A (2009) Hybrid discrete dynamically dimensioned search (HD-DDS) algorithm for water distribution system design optimization. Water Resour Res 45, W12416

  26. Van Veldhuizen DA (1999) Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. Ph.D. thesis, AFIT/DS/ENG/99-01, Air Force Institute of Technology, Wright-Patterson AFB, Ohio

  27. Vrugt JA, Robinson BA (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci 104(3):708–711

  28. Wang Q, Creaco E, Franchini M, Savić D, Kapelan Z (2014a) Comparing low and high-level hybrid algorithms on the two-objective optimal design of water distribution systems. Water Resour Manage. doi:10.1007/s11269-014-0823-8

  29. Wang Q, Guidolin M, Savic D, Kapelan Z (2014b) Two-objective design of benchmark problems of a water distribution system via MOEAs: towards the best-known approximation of the true Pareto front. J Water Resour Plan Manag. doi:10.1061/(ASCE)WR.1943-5452.0000460

  30. Yazdi J, Sadollah A, Lee EH, Kim JH (2014) Application of multi-objective evolutionary algorithms for rehabilitation of storm sewer pipe networks, J Flood Risk Manag, forthcoming

  31. Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolutionary Comput 11(6):712–731

  32. Zheng F, Simpson AF, Zecchin AC (2014a) Improving the efficiency of multi-objective evolutionary algorithms through decomposition: an application to water distribution network design. Environ Modelling Software 2014:1–13

  33. Zheng F, Simpson AF, Zecchin AC (2014b) An efficient hybrid approach for multiobjective optimization of water distribution systems. J Water Resour Res 50(5):3650–3671

  34. Zitzler E, Deb K, Thiele L (2000) Comparison of multi-objective evolutionary algorithms: empirical results, Evolutionary Computation: 173–195

  35. Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm, evolutionary methods for design, optimisation and control, In Proceedings of the EUROGEN2001 Conference, Athens, Greece, September 19–21, 2001, 95–100. Barcelona: International Center for Numerical Methods in Engineering (CIMNE)

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Correspondence to J. Yazdi.

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Yazdi, J. Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks. Water Resour Manage 30, 2749–2766 (2016). https://doi.org/10.1007/s11269-016-1320-z

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Keywords

  • MOEA/D
  • Water distribution network
  • NSGA2
  • Optimization
  • MOEA