Hybridizing Cellular Automata Principles and NSGAII for Multi-objective Design of Urban Water Networks

  • Yufeng Guo
  • Edward C. Keedwell
  • Godfrey A. Walters
  • Soon-Thiam Khu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4403)


Genetic algorithms are one of the state-of-the-art metaheuristic techniques for optimal design of capital-intensive infrastructural water networks. They are capable of finding near optimal cost solutions to these problems given certain cost and hydraulic parameters. Recently, multi-objective genetic algorithms have become prevalent due to the conflicting nature of these hydraulic and cost objectives. The Pareto-front of solutions obtained enables water engineers to have more flexibility by providing a set of design alternatives. However, multi-objective genetic algorithms tend to require a large number of objective function evaluations to achieve an acceptable Pareto-front. This paper describes a novel hybrid cellular automaton and genetic algorithm approach, called CAMOGA for multi-objective design of urban water networks. The method is applied to four large real-world networks. The results show that CAMOGA can outperform the standard multi-objective genetic algorithm in terms of optimization efficiency and quality of the obtained Pareto fronts.


Multi-Objective Optimization Pipe Networks Cellular Automata Genetic Algorithms 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Yufeng Guo
    • 1
  • Edward C. Keedwell
    • 1
  • Godfrey A. Walters
    • 1
  • Soon-Thiam Khu
    • 1
  1. 1.School of Engineering, Computer Science and Mathematics, University of Exeter, Exeter, EX4 4QFUnited Kingdom

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