Premium
Hybrid discrete dynamically dimensioned search (HD‐DDS) algorithm for water distribution system design optimization
Author(s) -
Tolson Bryan A.,
Asadzadeh Masoud,
Maier Holger R.,
Zecchin Aaron
Publication year - 2009
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2008wr007673
Subject(s) - algorithm , meta optimization , computer science , mathematical optimization , metaheuristic , ant colony optimization algorithms , local search (optimization) , genetic algorithm , population , particle swarm optimization , global optimization , search algorithm , hybrid algorithm (constraint satisfaction) , optimization problem , heuristics , mathematics , demography , sociology , stochastic programming , constraint programming , constraint logic programming
The dynamically dimensioned search (DDS) continuous global optimization algorithm by Tolson and Shoemaker (2007) is modified to solve discrete, single‐objective, constrained water distribution system (WDS) design problems. The new global optimization algorithm for WDS optimization is called hybrid discrete dynamically dimensioned search (HD‐DDS) and combines two local search heuristics with a discrete DDS search strategy adapted from the continuous DDS algorithm. The main advantage of the HD‐DDS algorithm compared with other heuristic global optimization algorithms, such as genetic and ant colony algorithms, is that its searching capability (i.e., the ability to find near globally optimal solutions) is as good, if not better, while being significantly more computationally efficient. The algorithm's computational efficiency is due to a number of factors, including the fact that it is not a population‐based algorithm and only requires computationally expensive hydraulic simulations to be conducted for a fraction of the solutions evaluated. This paper introduces and evaluates the algorithm by comparing its performance with that of three other algorithms (specific versions of the genetic algorithm, ant colony optimization, and particle swarm optimization) on four WDS case studies (21‐ to 454‐dimensional optimization problems) on which these algorithms have been found to perform well. The results obtained indicate that the HD‐DDS algorithm outperforms the state‐of‐the‐art existing algorithms in terms of searching ability and computational efficiency. In addition, the algorithm is easier to use, as it does not require any parameter tuning and automatically adjusts its search to find good solutions given the available computational budget.