The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Renato A Krohling - One of the best experts on this subject based on the ideXlab platform.
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coevolutionary particle swarm optimization using Gaussian Distribution for solving constrained optimization problems
Systems Man and Cybernetics, 2006Co-Authors: Renato A Krohling, Dos Santos L CoelhoAbstract:In this correspondence, an approach based on coevolutionary particle swarm optimization to solve constrained optimization problems formulated as min-max problems is presented. In standard or canonical particle swarm optimization (PSO), a uniform probability Distribution is used to generate random numbers for the accelerating coefficients of the local and global s. We propose a Gaussian probability Distribution to generate the accelerating coefficients of PSO. Two populations of PSO using Gaussian Distribution are used on the optimization algorithm that is tested on a suite of well-known benchmark constrained optimization problems. Results have been compared with the canonical PSO (constriction factor) and with a coevolutionary genetic algorithm. Simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness
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co evolutionary particle swarm optimization for min max problems using Gaussian Distribution
Congress on Evolutionary Computation, 2004Co-Authors: Renato A Krohling, Frank Hoffmann, Ld S CoelhoAbstract:Previous work presented an approach based on coevolutionary particle swarm optimization (Co-PSO) to solve constrained optimization problems formulated as min-max problems. Preliminary results demonstrated that Co-PSO constitutes a promising approach to solve constrained optimization problems. However the difficulty to obtain fine tuning of the solution using a uniform Distribution became evident. In this paper, a modified PSO using a Gaussian Distribution is applied in the context of Co-PSO. The modified Co-PSO is tested on some benchmark optimization problems and the results show a superior performance compared to the standard Co-PSO.
Ld S Coelho - One of the best experts on this subject based on the ideXlab platform.
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co evolutionary particle swarm optimization for min max problems using Gaussian Distribution
Congress on Evolutionary Computation, 2004Co-Authors: Renato A Krohling, Frank Hoffmann, Ld S CoelhoAbstract:Previous work presented an approach based on coevolutionary particle swarm optimization (Co-PSO) to solve constrained optimization problems formulated as min-max problems. Preliminary results demonstrated that Co-PSO constitutes a promising approach to solve constrained optimization problems. However the difficulty to obtain fine tuning of the solution using a uniform Distribution became evident. In this paper, a modified PSO using a Gaussian Distribution is applied in the context of Co-PSO. The modified Co-PSO is tested on some benchmark optimization problems and the results show a superior performance compared to the standard Co-PSO.
Andrew T. A. Wood - One of the best experts on this subject based on the ideXlab platform.
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An elliptically symmetric angular Gaussian Distribution
Statistics and Computing, 2018Co-Authors: Phillip J. Paine, Michail Tsagris, S. P. Preston, Andrew T. A. WoodAbstract:We define a Distribution on the unit sphere $$\mathbb {S}^{d-1}$$ S d - 1 called the elliptically symmetric angular Gaussian Distribution. This Distribution, which to our knowledge has not been studied before, is a subfamily of the angular Gaussian Distribution closely analogous to the Kent subfamily of the general Fisher–Bingham Distribution. Like the Kent Distribution, it has ellipse-like contours, enabling modelling of rotational asymmetry about the mean direction, but it has the additional advantages of being simple and fast to simulate from, and having a density and hence likelihood that is easy and very quick to compute exactly. These advantages are especially beneficial for computationally intensive statistical methods, one example of which is a parametric bootstrap procedure for inference for the directional mean that we describe.
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An elliptically symmetric angular Gaussian Distribution
Statistics and Computing, 2017Co-Authors: Phillip J. Paine, Simon Preston, Michail Tsagris, Andrew T. A. WoodAbstract:We define a Distribution on the unit sphere Sd−1 called the elliptically symmetric angular Gaussian Distribution. This Distribution, which to our knowledge has not been studied before, is a subfamily of the angular Gaussian Distribution closely analogous to the Kent subfamily of the general Fisher–Bingham Distribution. Like the Kent Distribution, it has elliptical contours, enabling modelling of rotational asymmetry about the mean direction, but it has the additional advantages of being simple and fast to simulate from, and having a density and hence likelihood that is easy and very quick to compute exactly. These advantages are especially beneficial for computationally intensive statistical methods, one example of which is a parametric bootstrap procedure for inference for the directional mean that we describe.
Maria Sabrina Greco - One of the best experts on this subject based on the ideXlab platform.
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Multivariate Generalized Gaussian Distribution: Convexity and Graphical Models
IEEE Transactions on Signal Processing, 2013Co-Authors: Teng Zhang, Ami Wiesel, Maria Sabrina GrecoAbstract:We consider covariance estimation in the multivariate generalized Gaussian Distribution (MGGD) and elliptically symmetric (ES) Distribution. The maximum likelihood optimization associated with this problem is non-convex, yet it has been proved that its global solution can be often computed via simple fixed point iterations. Our first contribution is a new analysis of this likelihood based on geodesic convexity that requires weaker assumptions. Our second contribution is a generalized framework for structured covariance estimation under sparsity constraints. We show that the optimizations can be formulated as convex minimization as long the MGGD shape parameter is larger than half and the sparsity pattern is chordal. These include, for example, maximum likelihood estimation of banded inverse covariances in multivariate Laplace Distributions, which are associated with time varying autoregressive processes.
Phillip J. Paine - One of the best experts on this subject based on the ideXlab platform.
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An elliptically symmetric angular Gaussian Distribution
Statistics and Computing, 2018Co-Authors: Phillip J. Paine, Michail Tsagris, S. P. Preston, Andrew T. A. WoodAbstract:We define a Distribution on the unit sphere $$\mathbb {S}^{d-1}$$ S d - 1 called the elliptically symmetric angular Gaussian Distribution. This Distribution, which to our knowledge has not been studied before, is a subfamily of the angular Gaussian Distribution closely analogous to the Kent subfamily of the general Fisher–Bingham Distribution. Like the Kent Distribution, it has ellipse-like contours, enabling modelling of rotational asymmetry about the mean direction, but it has the additional advantages of being simple and fast to simulate from, and having a density and hence likelihood that is easy and very quick to compute exactly. These advantages are especially beneficial for computationally intensive statistical methods, one example of which is a parametric bootstrap procedure for inference for the directional mean that we describe.
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An elliptically symmetric angular Gaussian Distribution
Statistics and Computing, 2017Co-Authors: Phillip J. Paine, Simon Preston, Michail Tsagris, Andrew T. A. WoodAbstract:We define a Distribution on the unit sphere Sd−1 called the elliptically symmetric angular Gaussian Distribution. This Distribution, which to our knowledge has not been studied before, is a subfamily of the angular Gaussian Distribution closely analogous to the Kent subfamily of the general Fisher–Bingham Distribution. Like the Kent Distribution, it has elliptical contours, enabling modelling of rotational asymmetry about the mean direction, but it has the additional advantages of being simple and fast to simulate from, and having a density and hence likelihood that is easy and very quick to compute exactly. These advantages are especially beneficial for computationally intensive statistical methods, one example of which is a parametric bootstrap procedure for inference for the directional mean that we describe.