An improved particle swarm optimization algorithm

Jiang, Yan; Hu, Tiesong; Huang, Chun Ming; Wu, Xianing

doi:10.1016/j.amc.2007.03.047

Cited by 257 publications

(109 citation statements)

References 6 publications

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“…Similar to PSSE-PSO [27], MSSE-PSO combines the strengths of the particle swarm optimization, competitive evolution and sub-swarm shuffling, which greatly enhances survivability by sharing the information gained independently by each swarm. Besides, MSSE-PSO adopts the hierarchical idea, by which the master swarm guides the whole group to the optimal direction to control the balance between exploration and exploitation.…”

Section: Master-slave Swarms Shuffling Evolution Based On Pso (Msse-pso)mentioning

confidence: 99%

“…And then propose some modifications in the position update rule of particle swarm optimization algorithm in order to make the convergence faster. Jiang et al [27] propose an improved PSO algorithm named PSSE-PSO (parallel swarms shuffling evolution algorithm based on particle swarm optimization) for hydrological parameters calibration, by introducing the ideas of population division and biological evolution into the standard PSO to avoid premature convergence. In this paper, we present MSSE-PSO (master-slave swarms shuffling evolution algorithm based on particle swarm optimization) to improve the performance of PSSE-PSO in accordance with the idea of hierarchical evolution.…”

Section: Introductionmentioning

confidence: 99%

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Improved particle swarm algorithm for hydrological parameter optimization

Jiang

Liu

Huang

et al. 2010

Applied Mathematics and Computation

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a b s t r a c tIn this paper, a new method named MSSE-PSO (master-slave swarms shuffling evolution algorithm based on particle swarm optimization) is proposed. Firstly, a population of points is sampled randomly from the feasible space, and then partitioned into several sub-swarms (one master swarm and other slave swarms). Each slave swarm independently executes PSO or its variants, including the update of particles' position and velocity. For the master swarm, the particles enhance themselves based on the social knowledge of master swarm and that of slave swarms. At periodic stage in the evolution, the master swarm and the whole slave swarms are forced to mix, and points are then reassigned to several subswarms to ensure the share of information. The process is repeated until a user-defined stopping criterion is reached. The tests of numerical simulation and the case study on hydrological model show that MSSE-PSO remarkably improves the accuracy of calibration, reduces the time of computation and enhances the performance of stability. Therefore, it is an effective and efficient global optimization method.

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Section: Master-slave Swarms Shuffling Evolution Based On Pso (Msse-pso)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved particle swarm algorithm for hydrological parameter optimization

Jiang

Liu

Huang

et al. 2010

Applied Mathematics and Computation

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“…PSO was proposed by Kennedy and Eberhart (1995) based on the analogy of swarming animals and is a simple and powerful heuristic method for solving nonlinear, nondifferential and multimodal optimization problems. Since Gill et al (2006) used PSO for parameter estimation in hydrology, many researchers have presented various types of PSO for hydrologic model calibration (Jiang et al, 2007(Jiang et al, , 2010Zhang et al, 2009;Kuok et al, 2010;Kraue et al, 2011). PSO approach has many computational advantages over traditional evolutionary computing, such as rapid convergence (Jiang et al, , 2007Chau, 2007).…”

Section: Introductionmentioning

confidence: 99%

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Jiang

Liu

et al. 2015

Environmental Modelling & Software

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a b s t r a c tWe present a study on the Hydro-Informatic Modelling System (HIMS) rainfall-runoff model for a semiarid region. The model includes nine parameters in need of calibration. A master-slave swarms, shuffling evolution algorithm based on self-adaptive dynamic particle swarm optimization (MSSE-SDPSO) is proposed to derive model parameters. In comparison with SCE-UA, PSO, MSSE-PSO and MSSE-SPSO algorithms, MSSE-SDPSO has faster convergence and more stable performance. The model is used to simulate discharge in the Luanhe River basin, a semiarid region. Compared with the SimHyd and SMAR models, HIMS model has the highest Nash-Sutcliffe efficiencies (NSE) and smallest relative errors (RE) of volumetric fitness for the periods of calibration and verification. In addition, the studies indicate that the HIMS model with all-gauge data improves runoff prediction compared with single-gauge data. A distributed HIMS model performs better than a lumped one. Finally, the Morris method is used to analyze model parameters sensitivity for the objective functions NSE and RE.

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“…PSO shows promising performance on nonlinear function optimization and has thus received much attention [4] . However, the local search ability of PSO is rather poor [5] and often results in premature convergence, especially under circumstances where PSO is applied to complex multi-peak search problems [6] . For this reason, a strategic parameter, the well-known inertia weight factor, was introduced by Shi and Eberhart [7] in an effort to strike a better balance between global exploration and local exploitation.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive catfish particle swarm optimization

Chuang¹,

Tsai²,

Yang

2012

AIR

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The catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization technique. This algorithm was inspired by the interactive behavior of sardines and catfish. The observed catfish effect is applied to improve the performance of particle swarm optimization (PSO). In this paper, we propose fuzzy CatfishPSO (F-CatfishPSO), which uses fuzzy to dynamically change the inertia weight of CatfishPSO. Ten benchmark functions with 10, 20, and 30 different dimensions were selected as the test functions. Statistical analysis of the experimental results indicates that F-CatfishPSO outperformed PSO, F-PSO and CatfishPSO.

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An improved particle swarm optimization algorithm

Cited by 257 publications

References 6 publications

Improved particle swarm algorithm for hydrological parameter optimization

Improved particle swarm algorithm for hydrological parameter optimization

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Fuzzy adaptive catfish particle swarm optimization

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