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ON THE STABILITY OF AN ADAPTIVE LEARNING DYNAMICS IN TRAFFIC GAMES

JOURNAL OF DYNAMICS AND GAMES 2018年第4期5卷 265-282页

作者： Dumett, Miguel A. Cominetti, Roberto San Diego State Univ Computat Sci Res Ctr San Diego CA 92182 USA Univ Adolfo Ibanez Fac Ingn & Ciencias Santiago Chile

This paper investigates the dynamic stability of an adaptive learning procedure in a traffic game. Using the Routh-Hurwitz criterion we study the stability of the rest points of the corresponding mean field dynamics. In the special case with two routes and two players we provide a full description of the number and nature of these rest points as well as the global asymptotic behavior of the dynamics. Depending on the parameters of the model, we find that there are either one, two or three equilibria and we show that in all cases the mean field trajectories converge towards a rest point for almost all initial conditions.

关键词： Congestion games adaptive learning dynamics stochastic algorithms routing equilibrium dynamical systems stability Routh-Hurwitz criterion

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A new sine cosine algorithm for economic and emission dispatch problems with price penalty factors

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JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2019年第3期40卷 679-697页

作者： Gonidakis, Dimitrios Vlachos, Aristidis Univ Piraeus Dept Informat 80M Karaoli & A Dimitriou St Piraeus 18534 Greece Acad Merchant Marine Aspropyrgos Deptartment Mech Engn Aspropyrgos 19300 Greece

Combined economic and emission dispatch (CEED) is the process of determining the outputs of generating units in a power system in order to minimise the fuel cost and pollutants emissions at the same time. This multi-objective mathematical problem can be transformed into a single-objective with the insertion of a price penalty factor. In this study, a newly developed sine and cosine algorithm (SCA) is employed to solve CEED. The proposed method is described by a trigonometric-based model and applied to three test systems with different characteristics. The numerical results indicate that SCA provides high quality solutions and outperforms other recently reported optimisation methods.

关键词： Combined economic and emission dispatch Economic load dispatch Sine cosine algorithm Multi-objective optimisation Price penalty factor stochastic algorithms

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Multi-Objective Optimization of Additive Manufacturing Process

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IFAC-PapersOnLine 2018年第11期51卷 152-157页

作者： Asadollahi-Yazdi, Elnaz Gardan, Julien Lafon, Pascal ICD/LASMIS UMR CNRS 6281 University of Technology of Troyes Troyes France EPF Engineering school Rosière-Près-Troyes France

This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fused Deposition Modeling (FDM) is investigated as a common AM technology. For this purpose, a multi-optimization problem is formulated according to the analysis of FDM technology. In this problem, layer thickness and part orientation are determined as the decision variables which are the important parameters of manufacturing. As objective functions, production time and material mass are considered and the surface roughness of FDM products and mechanical behavior of material are defined as the constraint functions. Different methodologies are developed to model the AM criteria according to these decision variables. To find the optimal solutions for manufacturing, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used. Finally, a case study highlighted the reliability of the proposed approach. © 2018

关键词： Multiobjective optimization 3D printers Decision making Deposition Genetic algorithms Layered manufacturing stochastic systems Surface roughness Additive manufacturing process Fused deposition modeling Manufacturing parameters Mechanical behavior of materials Multi objective optimization problem Non dominated sorting genetic algorithm ii (NSGA II) Non dominated sorting genetic algorithm ii stochastic algorithms

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A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental algorithms

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JOURNAL OF MACHINE LEARNING RESEARCH 2017年第1期18卷 5164-5198页

作者： Zhang, Huishuai Zhou, Yi Liang, Yingbin Chi, Yuejie Syracuse Univ Dept EECS Syracuse NY 13244 USA Ohio State Univ Dept ECE Columbus OH 43210 USA

We study the problem of solving a quadratic system of equations, i.e., recovering a vector signal x is an element of R-n from its magnitude measurements y(i) = ||, i = 1,..., m. We develop a gradient descent algorithm... 详细信息

We study the problem of solving a quadratic system of equations, i.e., recovering a vector signal x is an element of R-n from its magnitude measurements y(i) = |< a(i), x(i)>|, i = 1,..., m. We develop a gradient descent algorithm (referred to as RWF for reshaped Wirtinger flow) by minimizing the quadratic loss of the magnitude measurements. Comparing with Wirtinger flow (WF) (Candes et al., 2015), the loss function of RWF is nonconvex and nonsmooth, but better resembles the least-squares loss when the phase information is also available. We show that for random Gaussian measurements, RWF enjoys linear convergence to the true signal as long as the number of measurements is O(n). This improves the sample complexity of WF (O(n log n)), and achieves the same sample complexity as truncated Wirtinger flow (TWF) (Chen and Candes, 2015), but without any sophisticated truncation in the gradient loop. Furthermore, RWF costs less computationally than WF, and runs faster numerically than both WF and TWF. We further develop an incremental (stochastic) version of RWF (IRWF) and connect it with the randomized Kaczmarz method for phase retrieval. We demonstrate that IRWF outperforms existing incremental as well as batch algorithms with experiments.

关键词： gradient descent phase retrieval nonconvex optimization regularity condition stochastic algorithms

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stochastic Differential Equations 1

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2017年

作者： Panik Michael J.

ISBN: (数字)9781119377412

ISBN: (纸本)9781119377382

A beginner’s guide to stochastic growth modeling The chief advantage of stochastic growth models over deterministic models is that they combine both deterministic and stochastic elements of dynamic behaviors, such as weather, natural disasters, market fluctuations, and epidemics. This makes stochastic modeling a powerful tool in the hands of practitioners in fields for which population growth is a critical determinant of outcomes. However, the background requirements for studying SDEs can be daunting for those who lack the rigorous course of study received by math majors. Designed to be accessible to readers who have had only a few courses in calculus and statistics, this book offers a comprehensive review of the mathematical essentials needed to understand and apply stochastic growth models. In addition, the book describes deterministic and stochastic applications of population growth models including logistic, generalized logistic, Gompertz, negative exponential, and linear. Ideal for students and professionals in an array of fields including economics, population studies, environmental sciences, epidemiology, engineering, finance, and the biological sciences, stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling: • Provides precise definitions of many important terms and concepts and provides many solved example problems • Highlights the interpretation of results and does not rely on a theorem-proof approach • Features comprehensive chapters addressing any background deficiencies readers may have and offers a comprehensive review for those who need a mathematics refresher • Emphasizes solution techniques for SDEs and their practical application to the development of stochastic population models An indispensable resource for students and practitioners with limited exposure to mathematics and statistics, stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling is an excellent

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A review of velocity-type PSO variants

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JOURNAL OF algorithms & COMPUTATIONAL TECHNOLOGY 2017年第1期11卷 23-30页

作者： Sousa-Ferreira, Ivo Sousa, Duarte Univ Madeira Fac Exact Sci & Engn Dept Math Funchal Portugal

This paper presents a review of the particular variants of particle swarm optimization, based on the velocity-type class. The original particle swarm optimization algorithm was developed as an unconstrained optimization technique, which lacks a model that is able to handle constrained optimization problems. The particle swarm optimization and its inapplicability in constrained optimization problems are solved using the dynamic-objective constraint-handling method. The dynamic-objective constraint-handling method is originally developed for two variants of the basic particle swarm optimization, namely restricted velocity particle swarm optimization and self-adaptive velocity particle swarm optimization. Also on the subject velocity-type class, a review of three other variants is given, specifically: (1) vertical particle swarm optimization;(2) velocity limited particle swarm optimization;and (3) particle swarm optimization with scape velocity. These velocity-type particle swarm optimization variants all have in common a velocity parameter which determines the direction/movements of the particles.

关键词： stochastic algorithms particle swarm optimization velocity-type constrained optimization nonlinear programming

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stochastic algorithms Assessment Using Performance Profiles 11

Stochastic Algorithms Assessment Using Performance Profiles

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13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Costa, Lino Santo, Isabel Espirito Oliveira, Pedro Univ Minho Dept Prod & Syst Engn Campus Gualtar P-4710057 Braga Portugal Univ Porto Biomedicas Abel Salazar Inst Ciencias P-4099003 Porto Portugal

ISBN: (纸本)9781450305570

Optimization with stochastic algorithms has become a relevant approach, specially, in problems with complex search spaces. Due to the stochastic nature of these algorithms, the assessment and comparison is not straightforward. Several performance measures have been proposed to overcome this difficulty. In this work, the use of performance profiles and an analysis integrating a trade-off between accuracy and precision are carried out for the comparison of two stochastic algorithms. Traditionally, performance profiles are used to compare deterministic algorithms. This methodology is applied in the comparison of two stochastic algorithms - genetic algorithms and simulated annealing. The results highlight the advantages and drawbacks of the proposed assessment. Track Name: Genetic algorithms

关键词： Performance measures stochastic algorithms performance profiles

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Analysis of economic values of land use and land cover changes in crisis territories by satellite data: models of socio-economy and population dynamics in war 8

Analysis of economic values of land use and land cover chang...

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17th SPIE Conference on Earth Resources and Environmental Remote Sensing/GIS Applications

作者： Kostyuchenko, Yuriy V. Yuschenko, Maxim Movchan, Dmytro Kopachevsky, Ivan Natl Acad Sci Ukraine Sci Ctr Aerosp Res Earth 22-BO Honchar St UA-01054 Kiev Ukraine

ISBN: (纸本)9781510613218;9781510613201

Problem of remote sensing data harnessing for decision making in conflict territories is considered. Approach for analysis of socio-economic and demographic parameters with a limited set of data and deep uncertainty is described. Number of interlinked techniques to estimate a population and economy in crisis territories are proposed. stochastic method to assessment of population dynamics using multi-source data using remote sensing data is proposed. Adaptive Markov's chain based method to study of land-use changes using satellite data is proposed. Proposed approach is applied to analysis of socio-economic situation in Donbas (East Ukraine) territory of conflict in 2014-2015. Land-use and land-cover patterns for different periods were analyzed using the Landsat and MODIS data. The land-use classification scheme includes the following categories: (1) urban or built-up land, (2) barren land, (3) cropland, (4) horticulture farms, (5) livestock farms, (6) forest, and (7) water. It was demonstrated, that during the period 2014-2015 was not detected drastic changes in land-use structure of study area. Heterogeneously distributed decreasing of horticulture farms (4-6%), livestock farms (5-6%), croplands (3-4%), and increasing of barren land (6-7%) have been observed. Way to analyze land-cover productivity variations using satellite data is proposed. Algorithm is based on analysis of time-series of NDVI and NDWI distributions. Drastic changes of crop area and its productivity were detected. Set of indirect indicators, such as night light intensity, is also considered. Using the approach proposed, using the data utilized, the local and regional GDP, local population, and its dynamics are estimated.

关键词： Crisis management adaptive Markov's chains spectral indices land-use structure crop productivity socio-economic dynamic domestic security stochastic algorithms

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stochastic analysis of the Least Mean Kurtosis algorithm for Gaussian inputs

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DIGITAL SIGNAL PROCESSING 2016年 54卷 35-45页

作者： Bershad, Neil J. Bermudez, Jose C. M. Univ Calif Irvine Dept Elect & Comp Engn Irvine CA 92717 USA Univ Fed Santa Catarina Dept Elect & Elect Engn BR-88040970 Florianopolis SC Brazil

The Least Mean Kurtosis (LMK) algorithm was initially proposed as an adaptive algorithm that is robust to the observation noise distribution. Good performances of this algorithm have been shown for non Gaussian additive measurement noise. However, the complexity of the algorithm imposes difficulties for the development of a reasonably complete theoretical stochastic model for its behavior. The purpose of this paper is to contribute to the development of such a model. We study the stochastic behavior of Least Mean Kurtosis (LMK) algorithm for Gaussian inputs and for additive noises with even probability density functions. Deterministic recursions are derived for the adaptive weight error covariance matrix in a very novel manner, leading to a recursive model for the excess mean square error (EMSE) behavior that is shown to be accurate for Gaussian, uniform and binary noise distributions. The analysis results are then used to compare the performances of LMK with the least mean squares (LMS) and least mean fourth (LMF) algorithms under different circumstances. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Adaptive filters Analysis LMK algorithm stochastic algorithms

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A stochastic algorithm finding p-means on the circle

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BERNOULLI 2016年第4期22卷 2237-2300页

作者： Arnaudon, Marc Miclo, Laurent Univ Bordeaux UMR 5251 Inst Math Bordeaux 351 Cours Liberat F-33405 Talence France CNRS 351 Cours Liberat F-33405 Talence France Univ Toulouse 3 UMR 5219 Inst Math Toulouse 118 Route Narbonne F-31062 Toulouse 9 France CNRS 118 Route Narbonne F-31062 Toulouse 9 France

A stochastic algorithm is proposed, finding some elements from the set of intrinsic p-mean(s) associated to a probability measure nu on a compact Riemannian manifold and to p epsilon [1, infinity). It is fed sequentially with independent random variables (Y-n)n epsilon N distributed according to nu, which is often the only available knowledge of nu. Furthermore, the algorithm is easy to implement, because it evolves like a Brownian motion between the random times when it jumps in direction of one of the Y-n, n epsilon N. Its principle is based on simulated annealing and homogenization, so that temperature and approximations schemes must be tuned up (plus a regularizing scheme if nu does not admit a Holderian density). The analysis of the convergence is restricted to the case where the state space is a circle. In its principle, the proof relies on the investigation of the evolution of a time-inhomogeneous L-2 functional and on the corresponding spectral gap estimates due to Holley, Kusuoka and Stroock. But it requires new estimates on the discrepancies between the unknown instantaneous invariant measures and some convenient Gibbs measures.

关键词： Gibbs measures homogenization instantaneous invariant measures intrinsic p-means probability measures on compact Riemannian manifolds simulated annealing spectral gap at small temperature stochastic algorithms

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