2014
DOI: 10.1061/(asce)ps.1949-1204.0000154
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Integer Discrete Particle Swarm Optimization of Water Distribution Networks

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Cited by 51 publications
(25 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%
“…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%
“…The classical MOPSO is a robust algorithm to get globally optimal results for continuous definition domains. However, The MOPSO cannot be applied to discrete problems directly, which is a significant limitation because many optimization problems are set in a space featuring discrete variables [23]. Some attempts have been made to design multi-objective discrete particle swarm optimization (MODPSO) algorithms, and several methodologies have been proposed [24][25][26][27].…”
Section: Mopso Algorithmmentioning
confidence: 99%
“…Particles move in the search space relevant to their best positions ( p best ). The global best position or value reached so far by particles in the group is named g best (Ezzeldin, Djebedjian, & Saafan, ). Considering the j th particle in the n‐dimensional space as p j = ( p j 1 , p j2 ,…, p jn ) and its velocity as v j = ( v j 1 , v j 2 ,…, v jn ), the best previous position of this particle and the best particle between all particles can be written as p best j = ( p best j 1 , p best j 2 ,…, p best jn ) and g best = ( g best 1 g best 2 ,…, g best n ), respectively.…”
Section: Dimensional Analysis and Model Developmentmentioning
confidence: 99%