2006
DOI: 10.1080/15730620600855928
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Design of water distribution networks using particle swarm optimization

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Cited by 141 publications
(53 citation statements)
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“…In the mid 1990s, after the first popular applications of a GA [20,151], there was a swing towards stochastic methods and they dominate the field since (see Figure 4). A great range of those methods has been applied to optimise design of WDSs to date, inclusive of (but not limited to) a GA [42,45,50,85,86,[152][153][154], fmGA [88], non-crossover dither creeping mutation-based GA (CMBGA) [149], adaptive locally constrained GA (ALCO-GA) [155], SA [60], shuffled frog leaping algorithm (SFLA) [103], ACO [104,156], shuffled complex evolution (SCE) [157], harmony search (HS) [105,158,159], particle swarm HS (PSHS) [160], parameter setting free HS (PSF HS) [161], combined cuckoo-HS algorithm (CSHS) [162], particle swarm optimisation (PSO) [106,153,154], improved PSO (IPSO) [163], accelerated momentum PSO (AMPSO) [164], integer discrete PSO (IDPSO) [165], newly developed swarm-based optimisation (DSO) algorithm [150], scatter search (SS) [166], CE [61,62], immune algorithm (IA) [167], heuristic-based algorithm (HBA) [168], memetic algorithm (MA) [107], genetic heritage evolution by stochastic transmission (GHEST) [169], honey bee mating optimisation (HBMO) …”
Section: Solution Methodologymentioning
confidence: 99%
See 1 more Smart Citation
“…In the mid 1990s, after the first popular applications of a GA [20,151], there was a swing towards stochastic methods and they dominate the field since (see Figure 4). A great range of those methods has been applied to optimise design of WDSs to date, inclusive of (but not limited to) a GA [42,45,50,85,86,[152][153][154], fmGA [88], non-crossover dither creeping mutation-based GA (CMBGA) [149], adaptive locally constrained GA (ALCO-GA) [155], SA [60], shuffled frog leaping algorithm (SFLA) [103], ACO [104,156], shuffled complex evolution (SCE) [157], harmony search (HS) [105,158,159], particle swarm HS (PSHS) [160], parameter setting free HS (PSF HS) [161], combined cuckoo-HS algorithm (CSHS) [162], particle swarm optimisation (PSO) [106,153,154], improved PSO (IPSO) [163], accelerated momentum PSO (AMPSO) [164], integer discrete PSO (IDPSO) [165], newly developed swarm-based optimisation (DSO) algorithm [150], scatter search (SS) [166], CE [61,62], immune algorithm (IA) [167], heuristic-based algorithm (HBA) [168], memetic algorithm (MA) [107], genetic heritage evolution by stochastic transmission (GHEST) [169], honey bee mating optimisation (HBMO) …”
Section: Solution Methodologymentioning
confidence: 99%
“…Pipe sizes/diameters are discrete by nature of the problem, because they are to be selected from a set of commercially available sizes, however both discrete and continuous values are used mainly depending on the optimisation method. Discrete sizes are used mostly for stochastic algorithms (i.e., metaheuristics) [42,70,85,88,[102][103][104][105][106][107][108][109], whereas continuous sizes for deterministic methods [16,110,111]. In regards to continuous sizes, the final solution can be modified by splitting a link into two pipes of closest upper-and lower-sized commercially available discrete diameter [16].…”
Section: Pipesmentioning
confidence: 99%
“…Afterwards, a number of other evolutionary algorithms were developed and applied to WDS design. These include simulated annealing (Cunha and Sousa 2001); harmony search (Geem et al 2002); the shuffled frog leaping algorithm (Eusuff and Lansey 2003); Ant Colony Optimization (Maier et al M a n u s c r i p t N o t C o p y e d i t e d (Suribabu and Neelakantan 2006); cross entropy (Perelman and Ostfeld, 2007); scatter search (Lin et al 2007); HD-DDS (Tolson et al 2009) and differential evolution (Suribabu 2010). These EAs have been applied to a number of WDS case studies and exhibit good performance in terms of finding optimal solutions.…”
Section: N O T C O P Y E D I T E Dmentioning
confidence: 99%
“…En el momento en que se llevaron a cabo los trabajos Montalvo et al, 2008e), no se encontraron publicadas por otros autores, aplicaciones de la PSO en el problema de diseño óptimo de sistemas de distribución de agua. Como se mencionó con anterioridad, posterior a las publicaciones de este autor, se encontró el trabajo (Suribabu y Neelakantan, 2006); no obstante, las modificaciones introducidas como resultado de esta investigación son totalmente diferentes de las ideas presentadas en (Suribabu y Neelakantan, 2006) La PSO está inspirada en el comportamiento social de un grupo de pájaros migratorios tratando de alcanzar un destino desconocido. El algoritmo simula una bandada (swarm) de pájaros que se comunican mientras vuelan a través de en un espacio multidimensional, con tantas dimensiones como variables tenga el problema.…”
Section: Publicaciones Encontradasunclassified
“…Aplicado al diseño óptimo de SDA, no se encontraron referencias del uso del algoritmo PSO en el momento en que fueron publicados los primeros trabajos de este autor; posteriormente se conoció de la publicación (Suribabu y Neelakantan, 2006). Este algoritmo tiene sus bases en la inteligencia colectiva (swarm intelligence), la cual es una categoría relativamente nueva de algoritmo estocástico, basado en la evolución de poblaciones y que está íntimamente relacionado con los mecanismos evolutivos que imitan la evolución natural.…”
unclassified