Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing

• Published in: Water resources management Vol. 29; no. 13; pp. 4817 - 4831
• Main Authors: Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., Ballesteros-Pérez, P.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-10-2015; Springer Nature B.V
• Subjects: Algorithms; Anura; Atmospheric Sciences; Calibration; Civil Engineering; Design optimization; Earth and Environmental Science; Earth Sciences; Efficiency; Environment; Evolutionary algorithms; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology/Water Resources; Linear programming; Mathematical models; Mathematical programming; Methods; Networks; Optimization; Optimization algorithms; Optimization techniques; Performance evaluation; Pipelines; Researchers; Simulation; Sizing; Statistical analysis; Studies; Swarm intelligence; Water resources management; Water supply engineering; Chile; Brazil, Paraiba, Joao Pessoa; ANW, USA, New York; Vietnam, Hanoi; Design; Pipe sizing; Evolutionary algorithms; Water networks; Efficiency
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-015-1092-x

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.