The Generalized PSO: A New Door to PSOEvolution

Laviada, Jaime; García–Gonzalo, Esperanza

doi:10.1155/2008/861275

Cited by 65 publications

(14 citation statements)

References 12 publications

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“…Examples of methods used to optimise CEC-2013 problems include Particle Swarm Optimization [15], Adaptive Differential Evolution [19,48,49], Mean Variance Mapping [50] and GA [17]. The methods for optimisation of the traditional test functions, covered in this work, include Evolutionary Programming [12], Particle Swarm Optimization [51], GA [52], and Hybrid Bee Colony/QEA [53]. This section presents the bQIEA and rQIEA results that we produced.…”

Section: Resultsmentioning

confidence: 99%

Convergence properties of quantum evolutionary algorithms on high dimension problems

Wright

Jordanov

2019

Neurocomputing

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We propose and investigate new rotation gates for two modified Quantum Inspired Evolutionary methods for solving high dimension optimisation problems. The Quantum Inspired Evolutionary Algorithms (QIEA) were originally used for solving binary encoded problems and their signature features follow superposition of multiple states on a quantum bit and a rotation gate. In order to apply this paradigm to high dimension problems, we propose two quantum methods Half Significant Bit (HSB) and Stepwise Real QEA (SRQEA), developed using binary and real encoding respectively, while keeping close to the original quantum computing metaphor. We introduce five performance metrics and use them to evaluate the proposed approaches against sets of multimodal mathematical test functions and real world problems of high dimensionality. We report issues found while implementing some of the published real QIEA techniques which were the motivation for developing our real algorithm modifications. Our methods focus on introducing and implementing new rotation gate operators used for evolution, including a novel mechanism for preventing premature convergence in the binary algorithm. The applied performance metrics show superior results for our quantum methods on most of the test problems (particularly with high dimension problems), demonstrating faster convergence and accuracy.

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Section: Resultsmentioning

confidence: 99%

Convergence properties of quantum evolutionary algorithms on high dimension problems

Wright

Jordanov

2019

Neurocomputing

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“…From the physical point of view, PSO can be interpreted as a double stochastic gradient algorithm in the model space and is the particular case of the generalized PSO (GPSO) algorithm (Fernández Martínez and García Gonzalo [ 41 ]) for t = k and a unit time-step (∆ t = 1):

…”

Section: Rr-pso Samplingmentioning

confidence: 99%

Predictive Mathematical Models of the Short-Term and Long-Term Growth of the COVID-19 Pandemic

Fernández-Martínez

Fernández-Muñiz

Cernea

et al. 2021

Computational and Mathematical Methods in Medicine

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The prediction of the dynamics of the COVID-19 outbreak and the corresponding needs of the health care system (COVID-19 patients’ admissions, the number of critically ill patients, need for intensive care units, etc.) is based on the combination of a limited growth model (Verhulst model) and a short-term predictive model that allows predictions to be made for the following day. In both cases, the uncertainty analysis of the prediction is performed, i.e., the set of equivalent models that adjust the historical data with the same accuracy. This set of models provides the posterior distribution of the parameters of the predictive model that adjusts the historical series. It can be extrapolated to the same analyzed time series (e.g., the number of infected individuals per day) or to another time series of interest to which it is correlated and used, e.g., to predict the number of patients admitted to urgent care units, the number of critically ill patients, or the total number of admissions, which are directly related to health needs. These models can be regionalized, that is, the predictions can be made at the local level if data are disaggregated. We show that the Verhulst and the Gompertz models provide similar results and can be also used to monitor and predict new outbreaks. However, the Verhulst model seems to be easier to interpret and to use.

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“…The PSO algorithm can be physically interpreted as a particular discretization of a stochastic damped mass-spring system (Fernández-Martínez and García-Gonzalo, 2008). Based on analysing this stochastic differential model Fernández-Martínez and García-Gonzalo (2009b) proposed a family of PSO algorithms: the GPSO or centered-regressive PSO (Fernández-Martínez and García-Gonzalo, 2009a), the CC-GPSO or centered PSO, and the CP-PSO or centered-progressive PSO.…”

Section: The Particle Swarm Optimizersmentioning

confidence: 99%

“…Based on analysing this stochastic differential model Fernández-Martínez and García-Gonzalo (2009b) proposed a family of PSO algorithms: the GPSO or centered-regressive PSO (Fernández-Martínez and García-Gonzalo, 2009a), the CC-GPSO or centered PSO, and the CP-PSO or centered-progressive PSO. Although these algorithms are stochastic in nature they are not heuristic, since their convergence can be related to the first and second order stability of the trajectories now represented as stochastic processes (Fernández-Martínez and García-Gonzalo, 2008;García-Gonzalo, 2009 a,b, 2010). Both stability regions can be calculated analytically.…”

Section: The Particle Swarm Optimizersmentioning

confidence: 99%

“…In the algorithm design the time step parameter ( t ∆ ) plays a very important role: increasing 1 t ∆ > provides more exploratory character, while decreasing 1 t ∆ < allows for a more detailed local search around the global best. Switching of t ∆ with iterations has been named the lime and sand algorithm (Fernández-Martínez and García-Gonzalo, 2008) and allows escaping entrapment in local minima. In the case study the CP version with a variable time step has shown to be a very efficient algorithm.…”

Section: The Particle Swarm Optimizersmentioning

confidence: 99%

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Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers

et al. 2012

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History matching provides to the reservoir engineers an improved spatial distribution of physical properties to be used in forecasting the reservoir response for field management. The ill-posed character of the history matching problem yields non-uniqueness and numerical instabilities that increase with the reservoir complexity. These features might cause local optimization methods to provide unpredictable results not being able to discriminate among the multiple models that fit the observed data (production history). In this manuscript the ill-conditioned character of this history matching inverse problem is attenuated by reducing the model complexity using the Spatial Principal Component base and by combining as observables flow production measurements and time lapse seismic cross-well tomographic images. Additionally the inverse problem is solved in a stochastic framework. For this purpose we use a family of Particle Swarm Optimizers that have been deduced from a physical analogy of the swarm system. For the synthetic Stanford VI sand-and-shale reservoir we analyze the performance of the different PSO optimizers, both in terms of exploration and convergence rate for two different reservoir models with different complexity and under the presence of different levels of white Gaussian noise. We show that PSO optimizers have a very good convergence rate and provide in addition, approximate measures of uncertainty around the optimum facies model. Uncertainty estimation makes our algorithms more robust in presence of noise which is always the case for real data. This is an important achievement since in cases where the reservoir exhibits small scale features local methods get trapped and clearly fail to find a good solution. Finally we briefly introduce differential evolution and we show some preliminary results of its performance on the Stanford VI reservoir, showing that we are able to achieve similar results.

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The Generalized PSO: A New Door to PSOEvolution

Cited by 65 publications

References 12 publications

Convergence properties of quantum evolutionary algorithms on high dimension problems

Convergence properties of quantum evolutionary algorithms on high dimension problems

Predictive Mathematical Models of the Short-Term and Long-Term Growth of the COVID-19 Pandemic

Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers

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