Ant Colony Optimization Applied to Water Distribution System Design: Comparative Study of Five Algorithms

Water distribution systems (WDSs) are costly infrastructure, and much attention has been given to the application of optimization methods to minimize design costs. In previous studies, ant colony optimization (ACO) has been found to perform extremely competitively for WDS optimization. In this paper, five ACO algorithms are tested: one basic algorithm (ant system) and four more advanced algorithms [ant colony system, elitist ant system, elitist-rank ant system ( ASrank ) , and max-min ant system (MMAS)]. Experiments are carried out to determine their performance on four WDS case studies, three of which have been considered widely in the literature. The findings of the study show that some ACO algorithms are very successful for WDS design, as two of the ACO algorithms (MMAS and ASrank ) outperform all other algorithms applied to these case studies in the literature.

[1]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[2]  Angus R. Simpson,et al.  Genetic algorithms compared to other techniques for pipe optimization , 1994 .

[3]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[4]  Dragan Savic,et al.  Genetic Algorithms for Least-Cost Design of Water Distribution Networks , 1997 .

[5]  Angus R. Simpson,et al.  Application of two ant colony optimisation algorithms to water distribution system optimisation , 2006, Math. Comput. Model..

[6]  Maria da Conceição Cunha,et al.  Water Distribution Network Design Optimization: Simulated Annealing Approach , 1999 .

[7]  Angus R. Simpson,et al.  Parametric study for an ant algorithm applied to water distribution system optimization , 2005, IEEE Transactions on Evolutionary Computation.

[8]  Angus R. Simpson,et al.  Ant Colony Optimization for Design of Water Distribution Systems , 2003 .

[9]  Paul F. Boulos,et al.  Using Genetic Algorithms to Rehabilitate Distribution Systems , 2001 .

[10]  A. Simpson,et al.  An Improved Genetic Algorithm for Pipe Network Optimization , 1996 .

[11]  Luca Maria Gambardella,et al.  A COOPERATIVE LEARNING APPROACH TO TSP , 1997 .

[12]  James P. Heaney,et al.  Robust Water System Design with Commercial Intelligent Search Optimizers , 1999 .

[13]  B. Bullnheimer,et al.  A NEW RANK BASED VERSION OF THE ANT SYSTEM: A COMPUTATIONAL STUDY , 1997 .

[14]  Giovanni Righini,et al.  Heuristics from Nature for Hard Combinatorial Optimization Problems , 1996 .

[15]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[16]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[17]  Kevin E Lansey,et al.  Water distribution network design using the shuffled frog leaping algorithm , 2001 .

[18]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..