A hybrid simplex search and particle swarm optimization for unconstrained optimization

Fan, Shu-Kai S.; Zahara, Erwie

doi:10.1016/j.ejor.2006.06.034

Cited by 298 publications

(126 citation statements)

References 19 publications

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“…Normalization of input data is necessary for obtaining correct trigonometric expansion. ( 1 (38) The symbol N is the total number of data patterns. T i and T i ' represent the actual value and prediction at time i.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…In order to reduce the opportunity of trapping in a local optimum, expand the search scope of the algorithm and enhance the algorithm's climbing ability, it is certainly critical to always maintain the diversity of particles. The existing algorithms such as chaos mechanism optimization [37], hybrid simplex search PSO [38], comprehensive learning PSO, dynamic random search technique [39] are difficult to solve the two problems (global optimization and premature convergence) simultaneously. Therefore, we design an improved particle swarm optimization (IPSO) with adaptive nonlinear inertia weight and dynamic arccosine function acceleration parameters.…”

Section: The Improved Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

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Section: Data Preprocessingmentioning

confidence: 99%

Section: The Improved Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

View full text Add to dashboard Cite

show abstract

“…In fact, the results showed that the hybrid algorithm had faster convergence, higher accuracy and was more effective for solving constrained engineering optimization problems (Luo and Zhang, 2011). Hybrid PSO optimization has also been widely applied (Shieh et al, 2011;Valdez et al, 2011;Fan and Zahara, 2007;Ahandania et al, 2012;Li et al, 2009;Wang et al, 2013) demonstrating faster convergence rates. Likewise, ACO was improved by incorporating a hybridization strategy (Chen et al, 2012;Koide et al, 2013).…”

Section: Introductionmentioning

confidence: 86%

Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

García-Segura

Piqueras

Martí

et al. 2014

Lat. Am. j. solids struct.

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“…Third, the domain of permissible solutions is not limited to variables satisfying some equality or inequality constraints, therefore it is an unconstrained problem. In the literature, there are many methods and algorithms developed to solve the multidimensional unconstrained non-linear problem, such as Newton's method, Broyden's method (Broyden 1965), line-search methods, trust-region methods (Celis, Dennis, and Tapia 1985), the Nelder-Mead simplex method (Nelder and Mead 1965), the conjugate-gradient method (Hestenes and Stiefel 1952) and their variants (Fan and Zahara 2007;Birgin and Martinez 2001). Most of these algorithms involve an iterative process that converges on a sufficiently accurate solution.…”

Section: Multidimensional Unconstrained Non-linear Minimizationmentioning

confidence: 99%

An optimization technique for addressing DEM misregistration in hilly terrain

Goodchild

2015

Annals of GIS

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Digital elevation models (DEMs) represent the Earth's topography and support a variety of applications, ranging from extracting watershed drainage structure to measuring glacier volume. Applications that require conflation of multiple DEMs are problematic, however, because of misregistration. We explore a spatial optimization technique to quantify the misregistration on the pixel level between NASA's Shuttle Radar Topography Mission (SRTM) elevation data set and the USGS's National Elevation Dataset (NED). The misregistration, estimated within blocks by horizontal offset and direction, is modelled spatially in terms of typical topographical parameters: slope, aspect and elevation. A Nelder-Mead algorithm was implemented to compute the local shift at two study sites in the San Gabriel Mountains (SGM) and Santa Monica Mountains (SMM) in Los Angeles County, California. The magnitude of misregistration is generally less than the distance between DEM postings, and varies by terrain type, being larger in steeper terrains; nevertheless, misregistration is systematic within these continuous terrains that have similar topographical features. ARTICLE HISTORY

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A hybrid simplex search and particle swarm optimization for unconstrained optimization

Cited by 298 publications

References 19 publications

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

An optimization technique for addressing DEM misregistration in hilly terrain

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