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1

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems  

PubMed Central

Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems. This approach is usually not very practical for large-scale problems because its computational cost is a multiple of solving a single convex problem. In this paper, we propose a General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-convex penalties. The GIST algorithm iteratively solves a proximal operator problem, which in turn has a closed-form solution for many commonly used penalties. At each outer iteration of the algorithm, we use a line search initialized by the Barzilai-Borwein (BB) rule that allows finding an appropriate step size quickly. The paper also presents a detailed convergence analysis of the GIST algorithm. The efficiency of the proposed algorithm is demonstrated by extensive experiments on large-scale data sets.

Gong, Pinghua; Zhang, Changshui; Lu, Zhaosong; Huang, Jianhua Z.; Ye, Jieping

2013-01-01

2

A Faster Algorithm for Quasi-convex Integer Polynomial Optimization  

E-print Network

Jun 23, 2010 ... is the binary encoding length of a bound on that region with r ? ldO(n), ... Kannan improved Lenstra's algorithm for linear integer optimization by ...... Symposium on Symbolic and Algebraic Computation, pages 259–266. ACM.

2010-06-23

3

Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics  

NASA Astrophysics Data System (ADS)

This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques like first-order methods and randomization for scalability, and survey the important role of parallel and distributed computation. The new Big Data algorithms are based on surprisingly simple principles and attain staggering accelerations even on classical problems.

Cevher, Volkan; Becker, Stephen; Schmidt, Mark

2014-09-01

4

From nonlinear optimization to convex optimization through firefly algorithm and indirect approach with applications to CAD/CAM.  

PubMed

Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor's method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently. PMID:24376380

Gálvez, Akemi; Iglesias, Andrés

2013-01-01

5

Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms.  

PubMed

In this paper, we consider the problem of feature selection for linear SVMs on uncertain data that is inherently prevalent in almost all datasets. Using principles of Robust Optimization, we propose robust schemes to handle data with ellipsoidal model and box model of uncertainty. The difficulty in treating ?0-norm in feature selection problem is overcome by using appropriate approximations and Difference of Convex functions (DC) programming and DC Algorithms (DCA). The computational results show that the proposed robust optimization approaches are superior than a traditional approach in immunizing perturbation of the data. PMID:25064040

Le Thi, Hoai An; Vo, Xuan Thanh; Pham Dinh, Tao

2014-11-01

6

Advances in Convex Optimization: Conic Programming  

E-print Network

Advances in Convex Optimization: Conic Programming Arkadi Nemirovski Abstract. During the last two decades, major developments in Convex Optimization were focusing on Conic Programming, primarily, on Linear, Conic Quadratic and Semidef- inite optimization. Conic Programming allows to reveal rich

Nemirovski, Arkadi

7

A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem  

Microsoft Academic Search

Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder–Mead

Taher Niknam

2010-01-01

8

Parallel MRI Reconstruction by Convex Optimization  

E-print Network

In parallel magnetic resonance imaging (pMRI), to find a joint solution for the image and coil sensitivity functions is a nonlinear and nonconvex problem. A class of algorithms reconstruct sensitivity encoded images of the coils first followed by the magnitude only image reconstruction, e.g. GRAPPA. It is shown in this paper that, if only the magnitude image is reconstructed, there exists a convex solution space for the magnitude image and sensitivity encoded images. This solution space enables formulation of a regularized convex optimization problem and leads to a globally optimal and unique solution for the magnitude image reconstruction. Its applications to in-vivo MRI data sets result in superior reconstruction performance compared with other algorithms.

Zhang, Cishen

2014-01-01

9

Generalized convex functions: properties, optimality and duality  

SciTech Connect

It is shown that a set which is closed and locally star shaped at each of its points is convex. Moreover, a semilocally convex function on a closed set which is also lower semi-continuous is a convex function. A characterization is given for a function to be semilocally convex. For a nonlinear programming problem involving semilocally convex functions, a dual is associated and necessary optimality conditions derived.

Kaul, R.N.; Kaur, S.

1984-08-01

10

A conic approach for separable convex optimization  

E-print Network

' & $ % A conic approach for separable convex optimization Fran#24;cois Glineur Aspirant F programmation math#19;ematique Han-sur-Lesse, February 2, 2001 #12; A conic approach for separable convex optimization ' & $ % #11; #8; Outline Introduction #5; Conic optimization #5; Geometric optimization

Glineur, François

11

Conic optimization: an elegant framework for convex optimization  

E-print Network

Conic optimization: an elegant framework for convex optimization Fran¸cois Glineur Service de Math the reader to a very elegant formu- lation of convex optimization problems called conic optimization is in- troduced, which leads to the conic formulation of convex optimization problems. This formulation

Glineur, François

12

Optimization Algorithms  

Microsoft Academic Search

\\u000a The right choice of an optimization algorithm can be crucially important in finding the right solutions for a given optimization\\u000a problem. There exist a diverse range of algorithms for optimization, including gradient-based algorithms, derivative-free\\u000a algorithms and metaheuristics. Modern metaheuristic algorithms are often nature-inspired, and they are suitable for global\\u000a optimization. In this chapter, we will briefly introduce optimization algorithms such

Xin-She Yang

13

Advances in dual algorithms and convex approximation methods  

NASA Technical Reports Server (NTRS)

A new algorithm for solving the duals of separable convex optimization problems is presented. The algorithm is based on an active set strategy in conjunction with a variable metric method. This first order algorithm is more reliable than Newton's method used in DUAL-2 because it does not break down when the Hessian matrix becomes singular or nearly singular. A perturbation technique is introduced in order to remove the nondifferentiability of the dual function which arises when linear constraints are present in the approximate problem.

Smaoui, H.; Fleury, C.; Schmit, L. A.

1988-01-01

14

Solving convex problems involving powers using conic optimization  

E-print Network

Solving convex problems involving powers using conic optimization and a new self-concordant barrier CFG 07 Heidelberg University CFG 07 Solving convex problems involving powers using conic optimization 1 #12;Overview 1. Motivation Why convex optimization? Why a conic formulation? 2. Unified conic

Glineur, François

15

Greedy approximation in convex optimization  

E-print Network

Jun 2, 2012 ... continuous functions. One more important argument that motivates us to ... In optimization theory an energy function E(x) is given and we should find an approximate ..... of matrices with nuclear norm not exceeding 1. We are ...

2012-06-02

16

A simplicial branch and duality bound algorithm for the sum of convex-convex ratios problem  

NASA Astrophysics Data System (ADS)

This article presents a simplicial branch and duality bound algorithm for globally solving the sum of convex-convex ratios problem with nonconvex feasible region. To our knowledge, little progress has been made for globally solving this problem so far. The algorithm uses a branch and bound scheme where the Lagrange duality theory is used to obtain the lower bounds. As a result, the lower-bounding subproblems during the algorithm search are all ordinary linear programs that can be solved very efficiently. It has been proved that the algorithm possesses global convergence. Finally, the numerical experiments are given to show the feasibility of the proposed algorithm.

Shen, Pei-Ping; Duan, Yun-Peng; Pei, Yong-Gang

2009-01-01

17

Universal Duality in Conic Convex Optimization Simon P. Schurr  

E-print Network

Universal Duality in Conic Convex Optimization Simon P. Schurr Andr´e L. Tits Dianne P. O is feasible. For a pair of dual conic convex programs, we provide simple conditions on the "constraint) they are metrically and topologically generic; and (iii) they can be verified by solving a single conic convex program

O'Leary, Dianne P.

18

Universal Duality in Conic Convex Optimization Simon P. Schurr  

E-print Network

Universal Duality in Conic Convex Optimization Simon P. Schurr Andr´e L. Tits Dianne P. O either the primal or dual is feasible. For a pair of dual conic convex programs, we provide simple be verified by solving a single conic convex program. We relate to universal duality the fact

Tits, André

19

Optimization Online - Convex Relaxations of Non-Convex Mixed ...  

E-print Network

Nov 14, 2008 ... ... of Non-Convex Mixed Integer Quadratically Constrained Programs: Projected ... We also propose a new "eigen reformulation" for MIQCP, and a cut ... take up to a couple of hours to solve using a state-of-the-art SDP solver.

Anureet Saxena

2008-11-14

20

A Pyramidal Approach to Convex Hull and Filling Algorithms  

Microsoft Academic Search

In the paper, a class of algorithms for filling concavities in binary images is presented. The paradigm of computation is a serial, multi-resolution approach. Among the algorithms which have been implemented, the most significant one is fully described, while for the others, some hints are given on the computational complexity. The algorithm for the approximation of the convex hull has

Maria Grazia Albanesi; Marco Ferretti; L. Zangrandi

1995-01-01

21

Formulating Cyber-Security as Convex Optimization Problems  

E-print Network

Formulating Cyber-Security as Convex Optimization ProblemsĂ? Kyriakos G. Vamvoudakis1 , Jo~ao P,vigna}@cs.ucsb.edu Abstract. Mission-centric cyber-security analysts require a complete overview and understanding The Flag (iCTF) hacking competition. Keywords: Cyber-Security, Convex Optimization, System Identifica- tion

Vigna, Giovanni

22

Formulating Cyber-Security as Convex Optimization Problems  

E-print Network

Formulating Cyber-Security as Convex Optimization Problems Kyriakos G. Vamvoudakis, Jo~ao P. Mission-centric cyber-security analysts require a complete overview and understanding of the state. Keywords: Cyber-Security, Convex Optimization, System Identifica- tion, iCTF 1 Introduction Guaranteeing

Hespanha, JoĂŁo Pedro

23

Motion Planning with Sequential Convex Optimization and Convex Collision Checking  

E-print Network

considers continuous-time safety Our algorithm is implemented in a software package called TrajOpt. We for delivering radiation to OB/GYN tumors [Garg et al., 2013]. plays two important roles in robot motion planning

North Carolina at Chapel Hill, University of

24

6.253 Convex Analysis and Optimization, Spring 2010  

E-print Network

This course will focus on fundamental subjects in (deterministic) optimization, connected through the themes of convexity, geometric multipliers, and duality. The aim is to develop the core analytical and computational ...

Bertsekas, Dimitri

25

Stochastic Convex Optimization Shai Shalev-Shwartz  

E-print Network

-trivial learnability. 1 Introduction We consider the stochastic convex minimization problem argmin wW F(w) (1) where F(w) = EZ [f(w; Z)] is the expectation, with re- spect to Z, of a random objective that is convex in w is to choose w based on the sample and full knowledge of f(·, ·) and W so as to minimize F(w). Alternatively

26

Newton-Raphson consensus for distributed convex optimization  

E-print Network

Newton-Raphson consensus for distributed convex optimization Luca Schenato joint work with A of Padova April 28th, 2011 schenato@dei.unipd.it (DEI - UniPD) Distrib. Newton-Raphson optimization April 28PD) Distrib. Newton-Raphson optimization April 28th, 2011 2 / 26 #12;Introduction Distribution optimization

Schenato, Luca

27

Convex Optimization Theory Athena Scientific, 2009  

E-print Network

for Conic Programming . . p. 346 6.8. Approximate Subgradient Methods . . . . . . . . . p. 347 6 is typically a conjugate function (cf. Section 4.2.1), which is generically closed and convex, but often non together with its special case, conic duality. Both of these duality structures arise often in applications

Recht, Ben

28

Derivative-free generation and interpolation of convex Pareto optimal IMRT plans.  

PubMed

In inverse treatment planning for intensity-modulated radiation therapy (IMRT), beamlet intensity levels in fluence maps of high-energy photon beams are optimized. Treatment plan evaluation criteria are used as objective functions to steer the optimization process. Fluence map optimization can be considered a multi-objective optimization problem, for which a set of Pareto optimal solutions exists: the Pareto efficient frontier (PEF). In this paper, a constrained optimization method is pursued to iteratively estimate the PEF up to some predefined error. We use the property that the PEF is convex for a convex optimization problem to construct piecewise-linear upper and lower bounds to approximate the PEF from a small initial set of Pareto optimal plans. A derivative-free Sandwich algorithm is presented in which these bounds are used with three strategies to determine the location of the next Pareto optimal solution such that the uncertainty in the estimated PEF is maximally reduced. We show that an intelligent initial solution for a new Pareto optimal plan can be obtained by interpolation of fluence maps from neighbouring Pareto optimal plans. The method has been applied to a simplified clinical test case using two convex objective functions to map the trade-off between tumour dose heterogeneity and critical organ sparing. All three strategies produce representative estimates of the PEF. The new algorithm is particularly suitable for dynamic generation of Pareto optimal plans in interactive treatment planning. PMID:17148822

Hoffmann, Aswin L; Siem, Alex Y D; den Hertog, Dick; Kaanders, Johannes H A M; Huizenga, Henk

2006-12-21

29

IMPROVED ALGORITHMS FOR CONVEX MINIMIZATION IN ...  

E-print Network

A clear advantage of dealing with bounded sets is the availability of a scale in which one can ...... motivation for this problem is the computation of the value of a two-person zero-sum .... Optimization, Kluwer Academic Publishers, Boston, 2004.

2009-01-18

30

Efficient Convex Optimization Approaches to Variational Image Fusion  

E-print Network

Efficient Convex Optimization Approaches to Variational Image Fusion Jing Yuan1 , Brandon Miles1 of Bergen Bergen, Norway tai@mi.uib.no Abstract. Image fusion is an imaging technique to visualize informa imaging etc. In this work, we study two variational approaches to image fusion which are closely related

Soatto, Stefano

31

Convex Formulations of Aggregate Network Air Traffic Flow Optimization Problems  

E-print Network

Control Center. I. INTRODUCTION Research on the steady increase in air traffic volume has triggeredConvex Formulations of Aggregate Network Air Traffic Flow Optimization Problems Daniel B. Work, Student Member, IEEE, Alexandre M. Bayen, Member, IEEE Abstract--The problem of regulating air traffic

32

Newton-Raphson consensus for distributed convex optimization  

E-print Network

Newton-Raphson consensus for distributed convex optimization Luca Schenato Department;Presentation outline Motivations State-of-the-art Centralized Newton-Raphson: a quick overview Consensus-based Newton-Raphson Convergence properties (theory + simulations) Future directions 6 #12;Presentation outline

Schenato, Luca

33

Operation and Configuration of a Storage Portfolio via Convex Optimization  

E-print Network

is equally broad, and includes pumped hydro, compressed air energy storage (CAES), battery energy storage sysOperation and Configuration of a Storage Portfolio via Convex Optimization Matt Kraning, Yang Wang consider a portfolio of storage devices which is used to modify a commodity flow so as to minimize

34

Operation and Configuration of a Storage Portfolio via Convex Optimization  

E-print Network

is equally broad, and includes pumped hydro, compressed air energy storage (CAES), battery energy storage sysOperation and Configuration of a Storage Portfolio via Convex Optimization Matt Kraning, Yang Wang University; email: {mkraning, yw224, ekine, boyd}@stanford.edu Abstract: We consider a portfolio of storage

35

sensitivity analysis in convex quadratic optimization: simultaneous ...  

E-print Network

of using optimal bases in parametric LO showing by an example that different ...... maximization game correspond to optimal solutions of the following quadratic minimization .... mization, Springer Science+Business Media, New York, USA.

2007-08-30

36

A Global Optimization Algorithm for Nonconvex Generalized Disjunctive Programming and Applications to Process Systems  

E-print Network

. Keywords: Nonconvex GDP, nonconvex MINLP, convex hull relaxation, branch and bound, global optimization1 A Global Optimization Algorithm for Nonconvex Generalized Disjunctive Programming Carnegie Mellon University Pittsburgh, PA 15213 Abstract A global optimization algorithm for nonconvex

Grossmann, Ignacio E.

37

An approximation algorithm for cutting out convex polygons Adrian Dumitrescu y  

E-print Network

of cutting, Stock cutting, Convex polygons. 1 Introduction Overmars and Welzl considered the followingAn approximation algorithm for cutting out convex polygons #3; Adrian Dumitrescu y Abstract We on a convex piece of paper, cut P out of the piece of paper in the cheapest possible way. No polynomial

Dumitrescu, Adrian

38

Optimality conditions for nondifferentiable convex semi-infinite programming  

Microsoft Academic Search

This paper gives characterizations of optimal solutions to the nondifferentiable convex semi-infinite programming problem,\\u000a which involve the notion of Lagrangian saddlepoint. With the aim of giving the necessary conditions for optimality, local\\u000a and global constraint qualifications are established. These constraint qualifications are based on the property of Farkas-Minkowski,\\u000a which plays an important role in relation to certain systems obtained by

M. A. López; E. Vercher

1983-01-01

39

Convex Optimization of Centralized Inventory Operations  

E-print Network

Jan 10, 2005 ... There are many real-life examples where companies attempt to reduce inventory and ... Sciences, University of Iowa, Iowa City, IA 52242-1000, USA. ...... The effect is a tightening, in the sense that the optimal value of the new ...

2005-01-10

40

Optimal partitions having disjoint convex and conic hulls  

Microsoft Academic Search

LetA1,?,An be distinctk-dimensional vectors. We consider the problem of partitioning these vectors intom sets so as to maximize an objective which is a quasi-convex function of the sum of vectors in each set. We show that there exists an optimal partition whose sets have (pairwise) disjoint conic hulls. We also show that if the number of vectors in each of

E. R. Barnes; Alan J. Hoffman; Uriel G. Rothblum

1992-01-01

41

Optimal Monetary Policy with a Convex Phillips Curve  

E-print Network

assumption that nominal wages are flexible upwards but rigid downwards, so that inflation is a decreasing and convex function of the unemployment rate–equivalently, an increasing function of the output gap; see Layard et al. (1991) and Nickell (1997... bias even when policymakers target the natural unemployment rate, that is when they operate with pru- dent discretion, and their loss function is symmetric. Optimal mon- etary policy also induces positive co-movement between average in- flation, average...

Tambakis, Demosthenes N

42

FIR Filter Design via Spectral Factorization and Convex Optimization 1 FIR Filter Design via Spectral Factorization  

E-print Network

is optimization variable fi are convex: for 0 1, fix + 1 ,y fix + 1 ,fiy examples: linear & convex quadratic 1000s variables, 10000s constraints feasible on PC FIR Filter Design via Spectral FactorizationFIR Filter Design via Spectral Factorization and Convex Optimization 1 FIR Filter Design via

43

On an Extension of Condition Number Theory to Non-Conic Convex Optimization  

E-print Network

On an Extension of Condition Number Theory to Non-Conic Convex Optimization Robert M. Freund, the modern theory of condition numbers for conic convex optimization: z := minx ctx s.t. Ax - b CY x CX , to the more general non-conic format: (GPd) z := minx ctx s.t. Ax - b CY x P , where P is any closed convex

Ordóñez, Fernando

44

A Linear time algorithm for computing the Voronoi diagram of a convex polygon  

Microsoft Academic Search

We present an algorithm for computing certain kinds of three-dimensional convex hulls in linear time. Using this algorithm, we show that the Voronoi diagram of n points in the plane can be computed in &THgr;(n) time when these points form the vertices of a convex polygon in, say, counterclockwise order. This settles an outstanding open problem in computational geometry. Our

Alok Aggarwal; Leonidas J. Guibas; James B. Saxe; Peter W. Shor

1987-01-01

45

A Linear-Time Algorithm for Computing the Voronoi Diagram of a Convex Polygon  

Microsoft Academic Search

We present an algorithm for computing certain kinds of three-dimensional convex hulls in linear time. Using this algorithm, we show that the Voronoi diagram ofn sites in the plane can be computed in ?(n) time when these sites form the vertices of a convex polygon in, say, counterclockwise order. This settles an open problem in computational geometry. Our techniques can

Alok Aggarwal; Leonidas J. Guibas; James B. Saxe; Peter W. Shor

1989-01-01

46

VeriQuickhull: fast sequential and parallel algorithms for computing the planar convex hull  

E-print Network

Computing the convex hull of a set of points in the plane is one of the most studied problems in computational geometry. The Quickhull algorithm is a popular convex hull algorithm. While the main structure of Quickhull is axed, many different...

Sambasivam, Mashilamani

2012-06-07

47

Optimal Stochastic Approximation Algorithms for Strongly Convex ...  

E-print Network

Jul 1, 2010 ... To motivate our discussion, let us mention a few concrete examples in statistical learning which help to represent massive data in a compact way [13]. Consider a set of ...... Mathematical Programming, 102:407–456, 2005.

2012-06-18

48

Optimal Stochastic Approximation Algorithms for Strongly Convex ...  

E-print Network

Specifically, by introducing a domain shrinking procedure, we significantly improve ... ‡Department of Industrial and Systems Engineering, University of Florida, ..... of this subsection will be dedicated to the convergence analysis of the above.

2012-06-18

49

Algorithms for bilevel optimization  

NASA Technical Reports Server (NTRS)

General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

50

Target position localization in a passive radar system through convex optimization  

NASA Astrophysics Data System (ADS)

This paper proposes efficient target localization methods for a passive radar system using bistatic time-of-arrival (TOA) information measured at multiple synthetic array locations, where the position of these synthetic array locations is subject to random errors. Since maximum likelihood (ML) formulation of this target localization problem is a non-convex optimization problem, semi-definite relaxation (SDR)-based optimization methods in general do not provide satisfactory performance. As a result, approximated ML optimization problems are proposed and solved with SDR plus bisection methods. For the case without position errors, it is shown that the relaxation guarantees a rank-one solution. The optimization problem for the case with position errors involves only a relaxation of a scalar quadratic term. Simulation results show that the proposed algorithms outperform existing methods and provide mean square position error performance very close to the Cramer-Rao lower bound even for larger values of noise and position estimation errors.

Chalise, Batu K.; Zhang, Yimin D.; Amin, Moeness G.; Himed, Braham

2013-05-01

51

Complexity of convex optimization using geometry-based measures and a reference point  

E-print Network

Our concern lies in solving the following convex optimization problem: minimize cx subject to Ax=b, x \\in P, where P is a closed convex set. We bound the complexity of computing an almost-optimal solution of this problem ...

Freund, Robert M.

2001-01-01

52

Firefly Algorithms for Multimodal Optimization  

Microsoft Academic Search

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms.

Xin-She Yang

2010-01-01

53

Strong conical hull intersection property, bounded linear regularity, Jameson's property (G), and error bounds in convex optimization  

Microsoft Academic Search

The strong conical hull intersection property and bounded linear regularity are propertiesof a collection of finitely many closed convex intersecting sets in Euclidean space.These fundamental notions occur in various branches of convex optimization (constrainedapproximation, convex feasibility problems, linear inequalities, for instance).It is shown that the standard constraint qualification from convex analysis impliesbounded linear regularity, which in turn yields the strong

Heinz H. Bauschke; Jonathan M. Borwein; Wu Li

1997-01-01

54

A block coordinate gradient descent method for regularized convex separable optimization and covariance selection  

Microsoft Academic Search

We consider a class of unconstrained nonsmooth convex optimization problems, in which the objective function is the sum of\\u000a a convex smooth function on an open subset of matrices and a separable convex function on a set of matrices. This problem\\u000a includes the covariance selection problem that can be expressed as an ?\\u000a 1-penalized maximum likelihood estimation problem. In this

Sangwoon Yun; Paul Tseng; Kim-Chuan Toh

55

Ultrafast Quantum Process Tomography via Continuous Measurement and Convex Optimization  

NASA Astrophysics Data System (ADS)

Quantum process tomography (QPT) is an essential tool to diagnose the implementation of a dynamical map. However, the standard protocol is extremely resource intensive. For a Hilbert space of dimension d, it requires d^2 different input preparations followed by state tomography via the estimation of the expectation values of d^2-1 orthogonal observables. We show that when the process is nearly unitary, we can dramatically improve the efficiency and robustness of QPT through a collective continuous measurement protocol on an ensemble of identically prepared systems. Given the measurement history we obtain the process matrix via a convex program that optimizes a desired cost function. We study two estimators: least-squares and compressive sensing. Both allow rapid QPT due to the condition of complete positivity of the map; this is a powerful constraint to force the process to be physical and consistent with the data. We apply the method to a real experimental implementation, where optimal control is used to perform a unitary map on a d=8 dimensional system of hyperfine levels in cesium atoms, and obtain the measurement record via Faraday spectroscopy of a laser probe.

Baldwin, Charles; Riofrio, Carlos; Deutsch, Ivan

2013-03-01

56

BROADBAND SENSOR LOCATION SELECTION USING CONVEX OPTIMIZATION IN VERY LARGE SCALE ARRAYS  

E-print Network

BROADBAND SENSOR LOCATION SELECTION USING CONVEX OPTIMIZATION IN VERY LARGE SCALE ARRAYS Yenming M pattern design, sensor location selection, very large scale arrays, convex op- timization, simulated annealing 1. INTRODUCTION Consider a large scale sensor array having N sensors that monitors a surveillance

Balan, Radu V.

57

Practical iterative image reconstruction in digital breast tomosynthesis by non-convex TpV optimization  

E-print Network

Practical iterative image reconstruction in digital breast tomosynthesis by non-convex Tp tomosynthesis (DBT) is a rapidly developing imaging modality that gives some tomographic information for breast reconstruction, non-convex optimization, tomosynthesis 1. INTRODUCTION Digital breast tomosynthesis (DBT)1

Kurien, Susan

58

Firefly Algorithms for Multimodal Optimization  

E-print Network

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.

Yang, Xin-She

2010-01-01

59

Worst-Case Violation of Sampled Convex Programs for Optimization ...  

E-print Network

convex programs and consider the relation between the probability of violation and worst-case violation. ... Key words. Uncertainty, Sampled ... Estimation of the number of random samples N is important to guarantee that the resulting solution

2008-12-23

60

NON-SMOOTH CONVEX OPTIMIZATION FOR AN EFFICIENT RECONSTRUCTION IN STRUCTURED ILLUMINATION MICROSCOPY  

E-print Network

.boulanger@curie.fr ABSTRACT This work aims at proposing a new reconstruction procedure for structured illumination microscopy reconstruction techniques. Index Terms-- Structured illumination microscopy, image restoration, deconvolutionNON-SMOOTH CONVEX OPTIMIZATION FOR AN EFFICIENT RECONSTRUCTION IN STRUCTURED ILLUMINATION

Condat, Laurent

61

Adaptively Constrained Convex Optimization for Accurate Fiber Orientation Estimation with High Order Spherical Harmonics  

PubMed Central

Diffusion imaging data from the Human Connectome Project (HCP) provides a great opportunity to map the whole brain white matter connectivity to unprecedented resolution in vivo. In this paper we develop a novel method for accurately reconstruct fiber orientation distribution from cutting-edge diffusion data by solving the spherical deconvolution problem as a constrained convex optimization problem. With a set of adaptively selected constraints, our method allows the use of high order spherical harmonics to reliably resolve crossing fibers with small separation angles. In our experiments, we demonstrate on simulated data that our algorithm outperforms a popular spherical deconvolution method in resolving fiber crossings. We also successfully applied our method to the multi-shell and diffusion spectrum imaging (DSI) data from HCP to demonstrate its ability in using state-of-the-art diffusion data to study complicated fiber structures. PMID:24505797

Tran, Giang; Shi, Yonggang

2014-01-01

62

Firefly Algorithms for Multimodal Optimization  

Microsoft Academic Search

Nature-inspired algorithms are among the most powerful algorithms for\\u000aoptimization. This paper intends to provide a detailed description of a new\\u000aFirefly Algorithm (FA) for multimodal optimization applications. We will\\u000acompare the proposed firefly algorithm with other metaheuristic algorithms such\\u000aas particle swarm optimization (PSO). Simulations and results indicate that the\\u000aproposed firefly algorithm is superior to existing metaheuristic algorithms.

Xin-she Yang

2009-01-01

63

Portfolio Optimization under Small Transaction Costs: a Convex Duality Approach  

E-print Network

. Keywords: utility maximization, small transaction costs, duality, shadow price MSC Subject Classification of consistent price systems or shadow price processes, which allow to translate the original problem into a more costs. In the present paper, we carry out a convex duality approach facilitated by the concept of shadow

Kallsen, Jan

64

Strong conical hull intersection property, bounded linear regularity, Jameson’s property (G), and error bounds in convex optimization  

Microsoft Academic Search

.   The strong conical hull intersection property and bounded linear regularity are properties of a collection of finitely many\\u000a closed convex intersecting sets in Euclidean space. These fundamental notions occur in various branches of convex optimization\\u000a (constrained approximation, convex feasibility problems, linear inequalities, for instance). It is shown that the standard\\u000a constraint qualification from convex analysis implies bounded linear regularity,

Heinz H. Bauschke; Jonathan M. Borwein; Wu Li

1999-01-01

65

A relaxed customized proximal point algorithm for separable convex ...  

E-print Network

Aug 22, 2011 ... our theoretical analysis and it is easier to expose our motivation of ...... Optimization and Lagrange Multiplier Methods, Academic. Press ... [16] B. S. He, M. H. Xu and X. M. Yuan, Solving large-scale least squares covariance.

2011-08-22

66

Analog circuit optimization using evolutionary algorithms and convex optimization  

E-print Network

In this thesis, we analyze state-of-art techniques for analog circuit sizing and compare them on various metrics. We ascertain that a methodology which improves the accuracy of sizing without increasing the run time or the ...

Aggarwal, Varun

2007-01-01

67

Automatic Algorithm for Correcting Motion Artifacts In Time-Resolved 2D MR Angiography Using Convex Projections  

E-print Network

Automatic Algorithm for Correcting Motion Artifacts In Time-Resolved 2D MR Angiography Using Convex-Resolved 2D MR Angiography Using Convex Projections Abstract Time-resolved contrast enhanced Magnetic Resonance Angiography (MRA) may suffer from involuntary patient motion. It is noted that while MR signal

Zabih, Ramin

68

libCreme: An optimization library for evaluating convex-roof entanglement measures  

NASA Astrophysics Data System (ADS)

We present the software library libCreme which we have previously used to successfully calculate convex-roof entanglement measures of mixed quantum states appearing in realistic physical systems. Evaluating the amount of entanglement in such states is in general a non-trivial task requiring to solve a highly non-linear complex optimization problem. The algorithms provided here are able to achieve to do this for a large and important class of entanglement measures. The library is mostly written in the MATLAB programming language, but is fully compatible to the free and open-source OCTAVE platform. Some inefficient subroutines are written in C/C++ for better performance. This manuscript discusses the most important theoretical concepts and workings of the algorithms, focusing on the actual implementation and usage within the library. Detailed examples in the end should make it easy for the user to apply libCreme to specific problems. Program summaryProgram title:libCreme Catalogue identifier: AEKD_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEKD_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU GPL version 3 No. of lines in distributed program, including test data, etc.: 4323 No. of bytes in distributed program, including test data, etc.: 70 542 Distribution format: tar.gz Programming language: Matlab/Octave and C/C++ Computer: All systems running Matlab or Octave Operating system: All systems running Matlab or Octave Classification: 4.9, 4.15 Nature of problem: Evaluate convex-roof entanglement measures. This involves solving a non-linear (unitary) optimization problem. Solution method: Two algorithms are provided: A conjugate-gradient method using a differential-geometric approach and a quasi-Newton method together with a mapping to Euclidean space. Running time: Typically seconds to minutes for a density matrix of a few low-dimensional systems and a decent implementation of the pure-state entanglement measure.

Röthlisberger, Beat; Lehmann, Jörg; Loss, Daniel

2012-01-01

69

On the Complexity of Optimization Problems for 3Dimensional Convex Polyhedra and Decision Trees \\Lambda  

E-print Network

]). They are the product of convex hull algorithms, and are key components for problems in robot motion planning and computer­aided geometric design. Moreover, due to a beautiful theorem of Steinitz [20, 38], they provide at the design of small linear decision trees to represent a multi­category point set (e.g., see [5, 6, 8, 21, 29

Goodrich, Michael T.

70

Maximizing protein translation rate in the non-homogeneous ribosome flow model: a convex optimization approach.  

PubMed

Translation is an important stage in gene expression. During this stage, macro-molecules called ribosomes travel along the mRNA strand linking amino acids together in a specific order to create a functioning protein. An important question, related to many biomedical disciplines, is how to maximize protein production. Indeed, translation is known to be one of the most energy-consuming processes in the cell, and it is natural to assume that evolution shaped this process so that it maximizes the protein production rate. If this is indeed so then one can estimate various parameters of the translation machinery by solving an appropriate mathematical optimization problem. The same problem also arises in the context of synthetic biology, namely, re-engineer heterologous genes in order to maximize their translation rate in a host organism. We consider the problem of maximizing the protein production rate using a computational model for translation-elongation called the ribosome flow model (RFM). This model describes the flow of the ribosomes along an mRNA chain of length n using a set of n first-order nonlinear ordinary differential equations. It also includes n + 1 positive parameters: the ribosomal initiation rate into the mRNA chain, and n elongation rates along the chain sites. We show that the steady-state translation rate in the RFM is a strictly concave function of its parameters. This means that the problem of maximizing the translation rate under a suitable constraint always admits a unique solution, and that this solution can be determined using highly efficient algorithms for solving convex optimization problems even for large values of n. Furthermore, our analysis shows that the optimal translation rate can be computed based only on the optimal initiation rate and the elongation rate of the codons near the beginning of the ORF. We discuss some applications of the theoretical results to synthetic biology, molecular evolution, and functional genomics. PMID:25232050

Poker, Gilad; Zarai, Yoram; Margaliot, Michael; Tuller, Tamir

2014-11-01

71

A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation  

Microsoft Academic Search

We propose and test a new approach for modeling consumer heterogeneity in conjoint estimation based on convex optimization and statistical machine learning. We develop methods both for metric and choice data. Like hierarchical Bayes (HB), our methods shrink individual-level partworth estimates towards a population mean. However, while HB samples from a posterior distribution that is influenced by exogenous parameters (the

Theodoros Evgeniou; Massimiliano Pontil; Olivier Toubia

2007-01-01

72

A NOTE ON STATE ESTIMATION AS A CONVEX OPTIMIZATION PROBLEM Thomas Schon, Fredrik Gustafsson, Anders Hansson  

E-print Network

- formation of some kind it is often impossible to incorporate this in the Kalman filter framework. We will give a very brief introduction to con- vex optimization (see also [2]). The main message in convex of a stochastic variable z that maximizes the conditional density p(z|y), given the observation y (y Rny and z

Schön, Thomas

73

On an Extension of Condition Number Theory to Non-Conic Convex Optimization  

E-print Network

The purpose of this paper is to extend, as much as possible, the modern theory of condition numbers for conic convex optimization: z* := minz ctx s.t. Ax - b Cy C Cx , to the more general non-conic format: z* := minx ctx ...

Freund, Robert M.

74

ON THE RELATION BETWEEN OPTION AND STOCK PRICES: A CONVEX OPTIMIZATION APPROACH  

E-print Network

ON THE RELATION BETWEEN OPTION AND STOCK PRICES: A CONVEX OPTIMIZATION APPROACH DIMITRIS BERTSIMAS of option and stock prices based just on the no-arbitrage assumption, but without assuming any model on this relation. For the single stock problem, given moments of the prices of the underlying assets, we show

Bertsimas, Dimitris

75

BROADBAND SENSOR LOCATION SELECTION USING CONVEX OPTIMIZATION IN VERY LARGE SCALE ARRAYS  

E-print Network

BROADBAND SENSOR LOCATION SELECTION USING CONVEX OPTIMIZATION IN VERY LARGE SCALE ARRAYS Yenming M ABSTRACT Consider a sensing system using a large number of N microphones placed in multiple dimensions to monitor a broadband acoustic field. Using all the microphones at once is impractical because of the amount

Yorke, James

76

10-725: Convex Optimization Fall 2013 Lecture 9: Newton Method  

E-print Network

10-725: Convex Optimization Fall 2013 Lecture 9: Newton Method Lecturer: Barnabas Poczos.1 Motivation Newton method is originally developed for finding a root of a function. It is also known as Newton- Raphson method. The problem can be formulated as, given a function f : R R, finding the point x

Tibshirani, Ryan

77

Convex Optimization: Fall 2013 Machine Learning 10-725/Statistics 36-725  

E-print Network

learning can be posed as optimization tasks that have special properties--such as convexity, smoothness topics: · Uses of duality, dual methods · Coordinate-based methods · Nonconvex methods · Large is http://www.stat.cmu.edu/~ryantibs/convexopt/. The class schedule, lecture notes, homeworks, etc

Tibshirani, Ryan

78

An Exact Solution to the Transistor Sizing Problem for CMOS Circuits Using Convex Optimization  

E-print Network

An Exact Solution to the Transistor Sizing Problem for CMOS Circuits Using Convex Optimization topology, the delay can be controlled by varying the sizes of transistors in the circuit. Here, the size of a transistor is measured in terms of its channel width, since the channel lengths in a digital circuit

Sapatnekar, Sachin

79

Optimal sets for a class of minimization problems with convex constraints  

E-print Network

We look for the minimizers of the functional $\\jla{\\la}(\\oo)=\\la|\\oo|-P(\\oo)$ among planar convex domains constrained to lie into a given ring. We prove that, according to the values of the parameter $\\la$, the solutions are either a disc or a polygon. In this last case, we describe completely the polygonal solutions by reducing the problem to a finite dimensional optimization problem. We recover classical inequalities for convex sets involving area, perimeter and inradius or circumradius and find a new one.

Bianchini, Chiara

2010-01-01

80

Ant Algorithms for Discrete Optimization  

E-print Network

. Ants can smell pheromone and, when choosing their way, they tend to choose, in probability, pathsAnt Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, Universit#19;e, Switzerland luca@idsia.ch Abstract This paper overviews recent work on ant algorithms, that is, algorithms

Ducatelle, Frederick

81

Ant Algorithms for Discrete Optimization  

E-print Network

called pheromone, forming in this way a pheromone trail. Ants can smell pheromone and, when choosingAnt Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, Universit´e Libre, Switzerland luca@idsia.ch Abstract This paper overviews recent work on ant algorithms, that is, algorithms

Gambardella, Luca Maria

82

Cutting-Set Methods for Robust Convex Optimization with ...  

E-print Network

without any known distribution, and we choose a design whose worst-case ... cations includes robust control [31,32], robust portfolio optimization [33–36], robust ..... of course, be used as the starting point for a local optimization method. .... the authors give a convergence proof that can be applied here without much change.

2008-03-31

83

1 Automatic Code Generation for Real-Time Convex Optimization  

E-print Network

family. We describe a preliminary implementation, built on the Python-based modeling framework CVXMOD.3.1 Adaptive filtering and equalization 16 1.3.2 Optimal order execution 17 1.3.3 Sliding window smoothing 18 1

84

A conic representation of the convex hull of disjunctive sets and conic cuts for integer second order cone optimization  

E-print Network

A conic representation of the convex hull of disjunctive sets and conic cuts for integer second No. (will be inserted by the editor) A conic representation of the convex hull of disjunctive sets and conic cuts for integer second order cone optimization Pietro Belotti · Julio C. G´oez · Imre P

Snyder, Larry

85

Level Bundle Methods for Constrained Convex Optimization with ...  

E-print Network

May 23, 2013 ... Many optimization problems arising from real-life applications cast into the ... Level bundle methods have at disposal lower bounds (self-built) ... (CV@R?) with confidence level ?; see [27], [28], [7]. ..... (b) Let Kl be the index set belonging to the l-th cycle, it then follows that both ..... Therefore, by developing.

2013-05-23

86

Applications of convex optimization in signal processing and digital communication  

Microsoft Academic Search

In the last two decades, the mathematical programming community has witnessed some spectacular advances in interior point methods and robust optimization. These advances have recently started to signifi- cantly impact various fields of applied sciences and engineering where computational efficiency is essential. This paper focuses on two such fields: digital signal processing and communication. In the past, the widely used

Zhi-Quan Luo

2003-01-01

87

5. Greedy and other efficient optimization algorithms  

E-print Network

5. Greedy and other efficient optimization algorithms David Keil Analysis of Algorithms 1/12 David algorithms 1. Optimal-substructure property 2. Greedy graph algorithms 1David Keil Analysis of Algorithms 5. Greedy algorithms 1/12 2. Greedy graph algorithms 3. Compression and packing 4. Space/time tradeoffs

Keil, David M.

88

Firefly Algorithm, Levy Flights and Global Optimization  

Microsoft Academic Search

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications

Xin-She Yang

2010-01-01

89

Ant Algorithms for Discrete Optimization  

E-print Network

ant-based al- gorithms to many different discrete optimization problems [5, 21]. Recent applications. Ants can smell pheromone, and when choosing their way, they tend to choose, in probability, pathsAnt Algorithms for Discrete Optimization Marco Dorigo Gianni Di Caro IRIDIA CP 194/6 Universit

Libre de Bruxelles, Université

90

Convexity-Based Optimization for Power-Delay Tradeoff using Transistor Sizing Mahesh Ketkar, and Sachin S. Sapatnekar  

E-print Network

Convexity-Based Optimization for Power-Delay Tradeoff using Transistor Sizing Mahesh Ketkar. In [3], the power optimization problem is solved by transistor sizing and ordering. Power dissipation of transistor sizing is not considered. Recently an accurate technique for circuit optimization has been

Sapatnekar, Sachin

91

Constrained multiobjective biogeography optimization algorithm.  

PubMed

Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA. PMID:25006591

Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping

2014-01-01

92

A variable smoothing algorithm for solving convex optimization ...  

E-print Network

Jul 13, 2012 ... by using both variable and constant smoothing parameters. ... Lipschitz continuous functions and the operator K : H?K is linear and ...... The function fspecial returns a rotationally symmetric Gaussian lowpass filter of size.

2012-07-13

93

Fairness in optimal routing algorithms  

E-print Network

. Tsei Dr. Pierce E. Cantrell A study of fairness in multiple path optimal routing algorithms is discussed. Fair- ness measures are developed to evaluate multiple path routing in virtual circuit and datagram implementations. Several objective.... One objective function is shown to have perfect fairness for virtual circuits. The objective function optimized was shown to have little effect on the average packet delay. To my parents and my brother ACKNOWLEDGMENTS I wish to express my...

Goos, Jeffrey Alan

2012-06-07

94

An intelligent genetic algorithm designed for global optimization of multi-minima functions  

Microsoft Academic Search

Many practical problems often lead to large non-convex non-linear programming problems that have multi-minima. The global optimization algorithms of these problems have received much attention over the last few years. Generally, stochastic algorithms are suitable for these problems, but not efficient when there are too many minima. Genetic algorithms are stochastic search approaches based on randomized operators, such as selection,

Li-ning Xing; Ying-wu Chen; Huai-ping Cai

2006-01-01

95

Global optimization of a nonconvex single facility location problem by sequential unconstrained convex minimization  

Microsoft Academic Search

The problem of maximizing the sum of certain composite functions, where each term is the composition of a convex decreasing function, bounded from below, with a convex function having compact level sets arises in certain single facility location problems with gauge distance functions. We show that this problem is equivalent to a convex maximization problem over a compact convex set

Hoang Tuy; Faiz A. Al-Khayyal

1992-01-01

96

Improving beampatterns of two-dimensional random arrays using convex optimization.  

PubMed

Sensors are becoming ubiquitous and can be combined in arrays for source localization purposes. If classical conventional beamforming is used, then random arrays have poor beampatterns. By pre-computing sensor weights, these beampatterns can be improved significantly. The problem is formulated in the frequency domain as a desired look direction, a frequency-independent transition region, and the power minimized in a rejection-region. Using this formulation, the frequency-dependent sensor weights can be obtained using convex optimization. Since the weights are data independent they can be pre-computed, the beamforming has similar computational complexity as conventional beamforming. The approach is demonstrated for real 2D arrays. PMID:21476620

Gerstoft, Peter; Hodgkiss, William S

2011-04-01

97

Multilevel algorithms for nonlinear optimization  

NASA Technical Reports Server (NTRS)

Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

98

Ant Algorithms for Discrete Optimization  

E-print Network

a substance called pheromone, forming in this way a pheromone trail. Ants can smell pheromone, and whenAnt Algorithms for Discrete Optimization Marco Dorigo Gianni Di Caro IRIDIA CP 194/6 Universit@iridia.ulb.ac.be Luca M. Gambardella IDSIA Corso Elvezia 36 CH-6900 Lugano Switzerland luca@idsia.ch Keywords ant

Hutter, Frank

99

A fast adaptive convex hull algorithm on two-dimensional processor arrays with a reconfigurable BUS system  

NASA Technical Reports Server (NTRS)

A bus system that can change dynamically to suit computational needs is referred to as reconfigurable. We present a fast adaptive convex hull algorithm on a two-dimensional processor array with a reconfigurable bus system (2-D PARBS, for short). Specifically, we show that computing the convex hull of a planar set of n points taken O(log n/log m) time on a 2-D PARBS of size mn x n with 3 less than or equal to m less than or equal to n. Our result implies that the convex hull of n points in the plane can be computed in O(1) time in a 2-D PARBS of size n(exp 1.5) x n.

Olariu, S.; Schwing, J.; Zhang, J.

1991-01-01

100

Firefly Algorithm, Lévy Flights and Global Optimization  

Microsoft Academic Search

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly\\u000aAlgorithm are among the most powerful algorithms for optimization. In this\\u000apaper, we intend to formulate a new metaheuristic algorithm by combining Levy\\u000aflights with the search strategy via the Firefly Algorithm. Numerical studies\\u000aand results suggest that the proposed Levy-flight firefly algorithm is superior\\u000ato existing metaheuristic algorithms. Finally implications

Xin-She Yang

2009-01-01

101

Firefly Algorithm, Levy Flights and Global Optimization  

E-print Network

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

Yang, Xin-She

2010-01-01

102

Firefly Algorithm, Lévy Flights and Global Optimization  

NASA Astrophysics Data System (ADS)

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

Yang, Xin-She

103

VI. Convexity Convex sets  

E-print Network

to convex sets. The unit ball B as well as the closed unit ball B - in a nls X are convex since #(1 - #)x ** Let X ls. For any two x, y # X, we call [x . . y] := {(1 - #)x + #y : # # [0 . . 1]} the (closed of the unit ball in a nls plays an important role. In this chapter, IF = IR. ** convexity is local linearity

Liblit, Ben

104

Mixed variable structural optimization using Firefly Algorithm  

Microsoft Academic Search

In this study, a recently developed metaheuristic optimization algorithm, the Firefly Algorithm (FA), is used for solving mixed continuous\\/discrete structural optimization problems. FA mimics the social behavior of fireflies based on their flashing characteristics. The results of a trade study carried out on six classical structural optimization problems taken from literature confirm the validity of the proposed algorithm. The unique

Amir Hossein Gandomi; Xin-She Yang; Amir Hossein Alavi

2011-01-01

105

NIPS workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice (Lake Tahoe, December 9, 2013)  

E-print Network

Tahoe, December 9, 2013) Discrete Convex Analysis: Basics, DC Programming, and Submodular Welfare Algorithm Kazuo Murota (U. Tokyo) 131209NIPSlakeTahoe 1 #12;Discrete Convex Analysis Convexity Paradigm in Discrete Optimization Matroid Theory + Convex Analysis Submodular fn Matroid base L-convex fn M

Murota, Kazuo

106

Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images.  

PubMed

We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a "global" 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2%±2.0% for 3-D TRUS images and 88.5%±3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers. PMID:24710163

Qiu, Wu; Yuan, Jing; Ukwatta, Eranga; Sun, Yue; Rajchl, Martin; Fenster, Aaron

2014-04-01

107

A NOTE ON STATE ESTIMATION AS A CONVEX OPTIMIZATION PROBLEM Thomas Sch on, Fredrik Gustafsson, Anders Hansson  

E-print Network

­ formation of some kind it is often impossible to incorporate this in the Kalman filter framework. We will give a very brief introduction to con­ vex optimization (see also [2]). The main message in convex of a stochastic variable z that maximizes the conditional density p(zjy), given the observation y (y 2 R ny and z

Gustafsson, Fredrik

108

IEEE TRANS. ON CONTROL OF NETWORK SYSTEMS, JUNE 2014 (WITH PROOFS) 1 Convex Relaxation of Optimal Power Flow  

E-print Network

IEEE TRANS. ON CONTROL OF NETWORK SYSTEMS, JUNE 2014 (WITH PROOFS) 1 Convex Relaxation of Optimal Engineering and Applied Science, Caltech slow@caltech.edu June 8, 2014 Abstract This tutorial summarizes, June 2014. This is an extended version with Appendex VI that proves the main results in this tutorial

Low, Steven H.

109

An efficient algorithm for function optimization: modified stem cells algorithm  

NASA Astrophysics Data System (ADS)

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

2013-03-01

110

The Optimal Solution of a Non-Convex State-Dependent LQR Problem and Its Applications  

PubMed Central

This paper studies a Non-convex State-dependent Linear Quadratic Regulator (NSLQR) problem, in which the control penalty weighting matrix in the performance index is state-dependent. A necessary and sufficient condition for the optimal solution is established with a rigorous proof by Euler-Lagrange Equation. It is found that the optimal solution of the NSLQR problem can be obtained by solving a Pseudo-Differential-Riccati-Equation (PDRE) simultaneously with the closed-loop system equation. A Comparison Theorem for the PDRE is given to facilitate solution methods for the PDRE. A linear time-variant system is employed as an example in simulation to verify the proposed optimal solution. As a non-trivial application, a goal pursuit process in psychology is modeled as a NSLQR problem and two typical goal pursuit behaviors found in human and animals are reproduced using different control weighting . It is found that these two behaviors save control energy and cause less stress over Conventional Control Behavior typified by the LQR control with a constant control weighting , in situations where only the goal discrepancy at the terminal time is of concern, such as in Marathon races and target hitting missions. PMID:24747417

Xu, Xudan; Zhu, J. Jim; Zhang, Ping

2014-01-01

111

The optimal solution of a non-convex state-dependent LQR problem and its applications.  

PubMed

This paper studies a Non-convex State-dependent Linear Quadratic Regulator (NSLQR) problem, in which the control penalty weighting matrix [Formula: see text] in the performance index is state-dependent. A necessary and sufficient condition for the optimal solution is established with a rigorous proof by Euler-Lagrange Equation. It is found that the optimal solution of the NSLQR problem can be obtained by solving a Pseudo-Differential-Riccati-Equation (PDRE) simultaneously with the closed-loop system equation. A Comparison Theorem for the PDRE is given to facilitate solution methods for the PDRE. A linear time-variant system is employed as an example in simulation to verify the proposed optimal solution. As a non-trivial application, a goal pursuit process in psychology is modeled as a NSLQR problem and two typical goal pursuit behaviors found in human and animals are reproduced using different control weighting [Formula: see text]. It is found that these two behaviors save control energy and cause less stress over Conventional Control Behavior typified by the LQR control with a constant control weighting [Formula: see text], in situations where only the goal discrepancy at the terminal time is of concern, such as in Marathon races and target hitting missions. PMID:24747417

Xu, Xudan; Zhu, J Jim; Zhang, Ping

2014-01-01

112

On convergence and optimality of genetic algorithms  

Microsoft Academic Search

An action of genetic algorithm could be represented in the search space as a random Markovian process. The question concerning its asymptotic stability properties is discussed. Conditions under which genetic algorithm is convergent, are formulated. Then the existence of an operator to which infinite long iterations of the genetic algorithms tend, is shown. This operator describes optimal genetic algorithm in

Witold Kosinski; Stefan Kotowski; Zbyszek Michalewicz

2010-01-01

113

Metaheuristic Optimization: Algorithm Analysis and Open Problems  

Microsoft Academic Search

\\u000a Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have\\u000a emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular.\\u000a Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved\\u000a for the majority of metaheuristic algorithms, while efficiency analysis is

Xin-She Yang

2011-01-01

114

A Genetic Algorithm for Packing ThreeDimensional NonConvex Objects Having Cavities and Holes  

E-print Network

packing, non­ convex objects, selective laser sintering, rapid proto­ typing 1 INTRODUCTION 1.1 SELECTIVE, the selective laser sintering (SLS) machine produces parts directly from CAD files through an additive manufac­ tions of parts at each layer. Selective laser sintering uses fine, heat­fusible powder to build a part

Coello, Carlos A. Coello

115

Linear prices for non-convex electricity markets: models and algorithms  

Microsoft Academic Search

Strict Linear Pricing in non-convex markets is a mathematical impossibility. In the context of electricity markets, two different classes of solutions have been proposed to this conundrum on both sides of the Atlantic. We formally describe these two approaches in a common framework, review and analyze their main properties, and discuss their shortcomings. In US, some orders are not settled

Mathieu VAN VYVE

2011-01-01

116

Memetic firefly algorithm for combinatorial optimization  

E-print Network

Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.

Fister, Iztok; Fister, Iztok; Brest, Janez

2012-01-01

117

Genetic Algorithms for Optimal Reservoir Dispatching  

Microsoft Academic Search

The fundamental guidelines for genetic algorithm to optimal reservoir dispatching have been introduced. It is concluded that with three basic generators selection, crossover and mutation genetic algorithm could search the optimum solution or near-optimal solution to a complex water resources problem. Alternative formulation schemes of a GA are considered. The real-value coding is proved significantly faster than binary coding, and

Chang Jian-Xia; Huang Qiang; Wang Yi-min

2005-01-01

118

Genetic algorithms approach to voltage optimization  

Microsoft Academic Search

The authors consider the use of genetic algorithms as a measure of voltage optimization of electric power system. Genetic algorithms are optimization and learning techniques based on natural selection and natural population genetics. A formation of a power system is encoded to a string of characters called an artificial chromosome the initial population of strings are generated at random, and

Takeshi Haida; Yoshiakira Akimoto

1991-01-01

119

Optimization of Transform Coefficients via Genetic Algorithm  

E-print Network

Optimization of Transform Coefficients via Genetic Algorithm Steven Becke CS 470 ­Project Write.................................................................................................................... 22 #12;1 Optimization of Transform Coefficients via Genetic Algorithm Steven Becke Abstract discovered in recent years for image compression is Wavelet Transforms. Wavelet transforms can dramatically

Mock, Kenrick

120

An Optimal Class Association Rule Algorithm  

NASA Astrophysics Data System (ADS)

Classification and association rule mining algorithms are two important aspects of data mining. Class association rule mining algorithm is a promising approach for it involves the use of association rule mining algorithm to discover classification rules. This paper introduces an optimal class association rule mining algorithm known as OCARA. It uses optimal association rule mining algorithm and the rule set is sorted by priority of rules resulting into a more accurate classifier. It outperforms the C4.5, CBA, RMR on UCI eight data sets, which is proved by experimental results.

Jean Claude, Turiho; Sheng, Yang; Chuang, Li; Kaia, Xie

121

A fast optimization algorithm for multicriteria intensity modulated proton therapy planning  

SciTech Connect

Purpose: To describe a fast projection algorithm for optimizing intensity modulated proton therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT planning. Methods: The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user. Results: The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK's interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead. Conclusions: The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization.

Chen Wei; Craft, David; Madden, Thomas M.; Zhang, Kewu; Kooy, Hanne M.; Herman, Gabor T. [Department of Computer Science, Graduate Center, City University of New York, New York, New York 10016 (United States); Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114 (United States); Department of Computer Science, Graduate Center, City University of New York, New York, New York 10016 (United States)

2010-09-15

122

Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions  

E-print Network

We analyze a class of estimators based on convex relaxation for solving high-dimensional matrix decomposition problems. The observations are noisy realizations of a linear transformation [bar through "X" symbol] of the sum ...

Agarwal, Alekh

123

Implicit optimality criterion for convex SIP problem with box constrained index set  

Microsoft Academic Search

We consider a convex problem of Semi-Infinite Programming (SIP) with a multidimensional index set defined by a finite number\\u000a of box constraints. In study of this problem we apply the approach suggested in Kostyukova et al. (Int. J. Math. Stat. 13(J08):13–33,\\u000a 2008) for convex SIP problems with one-dimensional index sets and based on the notions of immobile indices and their

O. I. Kostyukova; T. V. Tchemisova

2012-01-01

124

Intelligent perturbation algorithms to space scheduling optimization  

NASA Technical Reports Server (NTRS)

The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed.

Kurtzman, Clifford R.

1991-01-01

125

An Algorithmic Framework for Multiobjective Optimization  

PubMed Central

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

2013-01-01

126

An algorithmic framework for multiobjective optimization.  

PubMed

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

2013-01-01

127

Adaptable optimization : theory and algorithms  

E-print Network

Optimization under uncertainty is a central ingredient for analyzing and designing systems with incomplete information. This thesis addresses uncertainty in optimization, in a dynamic framework where information is revealed ...

Caramanis, Constantine (Constantine Michael), 1977-

2006-01-01

128

Acoustic Radiation Optimization Using the Particle Swarm Optimization Algorithm  

NASA Astrophysics Data System (ADS)

The present paper describes a fundamental study on structural bending design to reduce noise using a new evolutionary population-based heuristic algorithm called the particle swarm optimization algorithm (PSOA). The particle swarm optimization algorithm is a parallel evolutionary computation technique proposed by Kennedy and Eberhart in 1995. This algorithm is based on the social behavior models for bird flocking, fish schooling and other models investigated by zoologists. Optimal structural design problems to reduce noise are highly nonlinear, so that most conventional methods are difficult to apply. The present paper investigates the applicability of PSOA to such problems. Optimal bending design of a vibrating plate using PSOA is performed in order to minimize noise radiation. PSOA can be effectively applied to such nonlinear acoustic radiation optimization.

Jeon, Jin-Young; Okuma, Masaaki

129

A data locality optimizing algorithm  

Microsoft Academic Search

This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation rrlgorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifies the various transforms as unimodular matrix tmnsfonnations. The algorithm haa been implemented in the

Michael E. Wolf; Monica S. Lam

1991-01-01

130

Models for optimal harvest with convex function of growth rate of a population  

SciTech Connect

Two models for growth of a population, which are described by a Cauchy problem for an ordinary differential equation with right-hand side depending on the population size and time, are investigated. The first model is time-discrete, i.e., the moments of harvest are fixed and discrete. The second model is time-continuous, i.e., a crop is harvested continuously in time. For autonomous systems, the second model is a particular case of the variational model for optimal control with constraints investigated in. However, the prerequisites and the method of investigation are somewhat different, for they are based on Lemma 1 presented below. In this paper, the existence and uniqueness theorem for the solution of the discrete and continuous problems of optimal harvest is proved, and the corresponding algorithms are presented. The results obtained are illustrated by a model for growth of the light-requiring green alga Chlorella.

Lyashenko, O.I.

1995-12-10

131

Algorithms for Optimizing Hydropower System Operation  

Microsoft Academic Search

Successive linear programming, an optimal control algorithm, and a combination of linear programming and dynamic programming (LP-DP) are employed to optimize the operation of multireservoir hydrosystems given a deterministic inflow forecast. The algorithm maximize the value of energy produced at on-peak and off-peak rates, plus the estimated value of water remaining in storage at the end of the 12-month planning

Jan C. Grygier; Jery R. Stedinger

1985-01-01

132

Adaptive cuckoo search algorithm for unconstrained optimization.  

PubMed

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971

Ong, Pauline

2014-01-01

133

Adaptive Cuckoo Search Algorithm for Unconstrained Optimization  

PubMed Central

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

2014-01-01

134

Functional Sized Population Magnetic Optimization Algorithm  

NASA Astrophysics Data System (ADS)

Magnetic Optimization Algorithm (MOA) is a recently novel optimization algorithm inspired by the principles of magnetic field theory whose possible solutions are magnetic particles scattered in the search space. In order improve the performance of MOA, a Functional Size population MOA (FSMOA) is proposed here. To find the best function for the size of the population, several functions for MOA are considered and investigated and the best parameters for the functions will be derived. In order to test the proposed algorithm and operators, the proposed algorithm will be compared with GA, PSO, QEA and saw-tooth GA on 14 numerical benchmark functions. Experimental results show that the proposed algorithm consistently has a better performance than those of other algorithms in most benchmark function.

Torshizi, Mehdi; Tayarani-N., M.

135

Finding Tradeoffs by Using Multiobjective Optimization Algorithms  

Microsoft Academic Search

The objective of the present study is to demonstrate performances of Evolutionary Algorithms (EAs) and conventional gradient-based methods for finding Pareto fronts. The multiobjective optimization algorithms are applied to analytical test problems as well as to the real-world problems of a compressor design. The comparison results clearly indicate the superiority of EAs in finding tradeoffs.

Shigeru Obayashi; Daisuke Sasaki; Akira Oyama

2005-01-01

136

An Emotional Particle Swarm Optimization Algorithm  

Microsoft Academic Search

\\u000a This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to introduce some psychology\\u000a factor of emotion into the algorithm. In the new algorithm, which is based on a simple perception and emotion psychology model,\\u000a each particle has its own feeling and reaction to the current position, and it also has specified emotional factor towards\\u000a the sense

Yang Ge; Zhang Rubo

2005-01-01

137

Optimizing alphabet using genetic algorithms  

Microsoft Academic Search

Data compression algorithms were usually designed for data processing symbol by symbol. The input symbols of these algorithms are usually taken from the ASCII table, i.e. the size of the input alphabet is 256 symbols which are representable by 8-bit numbers. Several other techniques were developed-syllable-based compression, which uses the syllable as a basic compression symbol, and word-based compression, which

Jan Platos; Pavel Kromer

2011-01-01

138

Pathwise coordinate optimization  

Microsoft Academic Search

We consider ``one-at-a-time'' coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the $L_1$-penalized regression (lasso) in the literature, but it seems to have been largely ignored. Indeed, it seems that coordinate-wise algorithms are not often used in convex optimization. We show that this algorithm is very competitive with the

Jerome Friedman; Trevor Hastie; Holger Höfling; Robert Tibshirani

2007-01-01

139

OPTIMAL STEEPEST DESCENT ALGORITHMS FOR ...  

E-print Network

for the first time methods for differentiable optimization had their practical efficiency motivated by .... method ensures ?k = O(1/k2), and this means optimal complexity: an error of ? > 0 is achieved in ...... codes are written in Matlab. We solved 60 ...

2008-08-02

140

Online Convex Programming and Generalized Infinitesimal GradientAscent  

Microsoft Academic Search

Convex programming involves a convex set F Rn and a convex cost function c : F ! R. The goal of convex programming is to nd a point in F which minimizes c. In online convex programming, the convex set is known in advance, but in each step of some repeated optimization problem, one must select a point inF before

Martin Zinkevich

2003-01-01

141

Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems  

NASA Astrophysics Data System (ADS)

Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.

Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao

142

Algorithms for optimal dyadic decision trees  

SciTech Connect

A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory

2009-01-01

143

A multi-local optimization algorithm  

Microsoft Academic Search

The development of efficient algorithms that provide all the local minima of a function is crucial to solve certain subproblems\\u000a in many optimization methods. A “multi-local” optimization procedure using inexact line searches is presented, and numerical\\u000a experiments are also reported. An application of the method to a semi-infinite programming procedure is included.

Teresa León; Susana Sanmatías; Enriqueta Vercher

1998-01-01

144

Variable-Metric Algorithm For Constrained Optimization  

NASA Technical Reports Server (NTRS)

Variable Metric Algorithm for Constrained Optimization (VMACO) is nonlinear computer program developed to calculate least value of function of n variables subject to general constraints, both equality and inequality. First set of constraints equality and remaining constraints inequalities. Program utilizes iterative method in seeking optimal solution. Written in ANSI Standard FORTRAN 77.

Frick, James D.

1989-01-01

145

Algorithm selection in structural optimization  

E-print Network

Structural optimization is largely unused as a practical design tool, despite an extensive academic literature which demonstrates its potential to dramatically improve design processes and outcomes. Many factors inhibit ...

Clune, Rory P. (Rory Patrick)

2013-01-01

146

A novel bee swarm optimization algorithm for numerical function optimization  

NASA Astrophysics Data System (ADS)

The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO-RP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.

Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush

2010-10-01

147

BMI optimization by using parallel UNDX real-coded genetic algorithm with Beowulf cluster  

NASA Astrophysics Data System (ADS)

This paper deals with the global optimization algorithm of the Bilinear Matrix Inequalities (BMIs) based on the Unimodal Normal Distribution Crossover (UNDX) GA. First, analyzing the structure of the BMIs, the existence of the typical difficult structures is confirmed. Then, in order to improve the performance of algorithm, based on results of the problem structures analysis and consideration of BMIs characteristic properties, we proposed the algorithm using primary search direction with relaxed Linear Matrix Inequality (LMI) convex estimation. Moreover, in these algorithms, we propose two types of evaluation methods for GA individuals based on LMI calculation considering BMI characteristic properties more. In addition, in order to reduce computational time, we proposed parallelization of RCGA algorithm, Master-Worker paradigm with cluster computing technique.

Handa, Masaya; Kawanishi, Michihiro; Kanki, Hiroshi

2007-12-01

148

A cuckoo search algorithm for multimodal optimization.  

PubMed

Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850

Cuevas, Erik; Reyna-Orta, Adolfo

2014-01-01

149

A Cuckoo Search Algorithm for Multimodal Optimization  

PubMed Central

Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850

2014-01-01

150

Convexity, Classification, and Risk Bounds  

Microsoft Academic Search

Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 0-1 loss function. The convexity makes these algorithms computationally efficient. The use of a surrogate, however, has statistical consequences that must be balanced against the computational virtues of

Peter L. Bartlett; Michael I. Jordan; Jon D. McAuliffe

2006-01-01

151

Firefly Algorithm for Continuous Constrained Optimization Tasks  

Microsoft Academic Search

The paper provides an insight into the improved novel metaheuristics of the Firefly Algorithm for constrained continuous optimization\\u000a tasks. The presented technique is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication.\\u000a The first part of the paper is devoted to the detailed description of the existing algorithm. Then some suggestions for extending\\u000a the simple scheme of

Szymon ?ukasik; S?awomir ?ak

152

Hessian approximation algorithms for hybrid optimization methods  

Microsoft Academic Search

This article introduces Hessian approximation algorithms to estimate the search direction of the quasi-Newton methods for solving optimization problems of continuous parameters. The proposed algorithms are quite different from other well-known quasi-Newton methods, such as symmetric rank-one, Davidon–Fletcher–Powell, and Broyden–Fletcher–Goldfarb–Shanno, in that the Hessian matrix is not calculated from the gradient information, rather directly from the function values. The proposed

Min-Jea Tahk; Moon-Su Park; Hyun-Wook Woo; Hyoun-Jin Kim

2009-01-01

153

Space-efficient algorithms for computing the convex hull of a simple polygonal line  

E-print Network

of them that has been solved in almost every respect in the past twenty years: there are optimal, output polygonal chains as well, in an online fashion). The problem is two-fold: the polygonal line can be either

Chan, Timothy M.

154

Algorithms with conic termination for nonlinear optimization  

SciTech Connect

This paper describes algorithms for unconstrained optimization which have the property of minimizing conic objective functions in a finite number of steps, when line searches are exact. This work extends the algorithms of Davidon and Gourgeon and Nocedal to general nonlinear objective functions, paying much attention to the practical behavior of the new methods. Three types of algorithms are described; they are extensions of the conjugate gradient method, the BFGS method and a limited memory BFGS method. The numerical results show that new methods are very effective in solving practical problems. 19 refs., 4 tabs.

Liu, D.C.; Nocedal, J.

1987-12-01

155

Algorithm Optimally Allocates Actuation of a Spacecraft  

NASA Technical Reports Server (NTRS)

A report presents an algorithm that solves the following problem: Allocate the force and/or torque to be exerted by each thruster and reaction-wheel assembly on a spacecraft for best performance, defined as minimizing the error between (1) the total force and torque commanded by the spacecraft control system and (2) the total of forces and torques actually exerted by all the thrusters and reaction wheels. The algorithm incorporates the matrix vector relationship between (1) the total applied force and torque and (2) the individual actuator force and torque values. It takes account of such constraints as lower and upper limits on the force or torque that can be applied by a given actuator. The algorithm divides the aforementioned problem into two optimization problems that it solves sequentially. These problems are of a type, known in the art as semi-definite programming problems, that involve linear matrix inequalities. The algorithm incorporates, as sub-algorithms, prior algorithms that solve such optimization problems very efficiently. The algorithm affords the additional advantage that the solution requires the minimum rate of consumption of fuel for the given best performance.

Motaghedi, Shi

2007-01-01

156

Unsupervised Learning by Convex and Conic Coding  

E-print Network

Unsupervised Learning by Convex and Conic Coding D. D. Lee and H. S. Seung Bell Laboratories algorithms based on convex and conic en- coders are proposed. The encoders nd the closest convex or conic the conic algorithm discovers features. Both al- gorithms are used to model handwritten digits and compared

Seung, Sebastian

157

GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS  

E-print Network

GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS Cyril Allauzen and Mehryar provide a common representation for the components of a speech recognition system. In previous work, we, determinization. However, not all weighted automata and transducers used in large- vocabulary speech recognition

Allauzen, Cyril

158

GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS  

E-print Network

GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS Cyril Allauzen and Mehryar provide a common representation for the components of a speech recognition system. In previous work, we, determinization. However, not all weighted automata and transducers used in large­ vocabulary speech recognition

Mohri, Mehryar

159

Lightweight telescope structure optimized by genetic algorithm  

Microsoft Academic Search

We designed the optics supporting structure (OSS) of a 3.8 m segmented mirror telescope by applying genetic algorithm optimization. The telescope is the first segmented mirror telescope in Japan whose primary mirror consists of 18 petal shaped segment mirrors. The whole mirror is supported by 54 actuators (3 actuators per each segment). In order to realize light-weight and stiff telescope

Mikio Kurita; Hiroshi Ohmori; Masashi Kunda; Hiroaki Kawamura; Noriaki Noda; Takayuki Seki; Yuji Nishimura; Michitoshi Yoshida; Shuji Sato; Tetsuya Nagata

2010-01-01

160

Algorithm for fixed-range optimal trajectories  

NASA Technical Reports Server (NTRS)

An algorithm for synthesizing optimal aircraft trajectories for specified range was developed and implemented in a computer program written in FORTRAN IV. The algorithm, its computer implementation, and a set of example optimum trajectories for the Boeing 727-100 aircraft are described. The algorithm optimizes trajectories with respect to a cost function that is the weighted sum of fuel cost and time cost. The optimum trajectory consists at most of a three segments: climb, cruise, and descent. The climb and descent profiles are generated by integrating a simplified set of kinematic and dynamic equations wherein the total energy of the aircraft is the independent or time like variable. At each energy level the optimum airspeeds and thrust settings are obtained as the values that minimize the variational Hamiltonian. Although the emphasis is on an off-line, open-loop computation, eventually the most important application will be in an on-board flight management system.

Lee, H. Q.; Erzberger, H.

1980-01-01

161

A Simulative Bionic Intelligent Optimization Algorithm: Artificial Searching Swarm Algorithm and Its Performance Analysis  

Microsoft Academic Search

In this paper, a novel optimization algorithm - artificial searching swarm algorithm (ASSA) is presented by analyzing the operating principle and uniform framework of the bionic intelligent optimization algorithm. ASSA simulates the process of solving optimal design problem to the process of searching optimal goal by searching swarm with the set rules, and finds the optimal solution through the search

Tanggong Chen

2009-01-01

162

Energy minimization for real-time systems with non-convex and discrete operation modes  

Microsoft Academic Search

We present an optimal methodology for dynamic voltage scheduling problem in the presence of realistic assumption such as leakage-power and intra-task overheads. Our contri- bution is an optimal algorithm for energy minimization that concurrently assumes the presence of (1) non-convex energy-speed models as opposed to previously studied convex models, (2) dis- crete set of operational modes (voltages) and (3) intra-task

Foad Dabiri; Alireza Vahdatpour; Miodrag Potkonjak; Majid Sarrafzadeh

2009-01-01

163

?minimax: An Optimally Randomized MINIMAX Algorithm.  

PubMed

This paper proposes a simple extension of the celebrated MINIMAX algorithm used in zero-sum two-player games, called ?minimax. The ?minimax algorithm allows controlling the strength of an artificial rival by randomizing its strategy in an optimal way. In particular, the randomized shortest-path framework is applied for biasing the artificial intelligence (AI) adversary toward worse or better solutions, therefore controlling its strength. In other words, our model aims at introducing/implementing bounded rationality to the MINIMAX algorithm. This framework takes into account all possible strategies by computing an optimal tradeoff between exploration (quantified by the entropy spread in the tree) and exploitation (quantified by the expected cost to an end game) of the game tree. As opposed to other tree-exploration techniques, this new algorithm considers complete paths of a tree (strategies) where a given entropy is spread. The optimal randomized strategy is efficiently computed by means of a simple recurrence relation while keeping the same complexity as the original MINIMAX. As a result, the ?minimax implements a nondeterministic strength-adapted AI opponent for board games in a principled way, thus avoiding the assumption of complete rationality. Simulations on two common games show that ?minimax behaves as expected. PMID:22893439

García Díez, Silvia; Laforge, Jérôme; Saerens, Marco

2012-08-01

164

Intelligent perturbation algorithms for space scheduling optimization  

NASA Technical Reports Server (NTRS)

The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.

Kurtzman, Clifford R.

1990-01-01

165

A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy  

NASA Astrophysics Data System (ADS)

Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives.

Lahanas, M.; Baltas, D.; Zamboglou, N.

2003-02-01

166

A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy.  

PubMed

Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives. PMID:12608615

Lahanas, M; Baltas, D; Zamboglou, N

2003-02-01

167

EVALUATING THE QUALITY OF PIPELINE OPTIMIZATION ALGORITHMS  

Microsoft Academic Search

Abstract This paper,discusses,how,to,generate,good,lower,bounds,for the fuel cost minimization problem,arising from,the steady-state,gas pipeline,network,flows. These lower,bounds,may be wed,to evaluate,the quality of the solutions,provided,by the present,generation,of pipeline optimization,algorithms. Mathematical,models,of steady-state gas pipeline network,flows are complicated,by the existence

E. Andrew Boyd; L. Ridgway Scott; Suming Wu

168

Optical flow optimization using parallel genetic algorithm  

NASA Astrophysics Data System (ADS)

A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.

Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe

2011-06-01

169

Global optimization: Algorithms, complexity, and applications  

SciTech Connect

Global optimization problems appear in many diverse areas of operations research, management science, economics and engineering. Typical applications include allocation and location problems, economies of scale, transportation problems, engineering design and control chip design and database problems. Standard nonlinear optimization methods will usually obtain a local solution or a stationary point when applied to a global optimization problem. The problem of designing algorithms that compute global solutions is in general very difficult because of the lack of criteria in deciding whether a local solution is global or not. Moreover, nonlinear problems may have an exponential number of local solutions, which are not global. Active research in the past two decades has produced many deterministic and stochastic methods for computing global solutions. In this talk, we will focus on deterministic methods which include branch and bound algorithms, homotopy methods, path following techniques, interval analysis methods, and a variety of approximate techniques. In addition, we are going to discuss related complexity questions and implementation issues regarding many of the proposed algorithms.

Pardalos, P.; Gibbons, L.; Hearn, D.

1994-12-31

170

Hybrid genetic algorithm research and its application in problem optimization  

Microsoft Academic Search

There is a lot of research in genetic algorithm about structural optimization. But as far as the large multi-goal program is concerned, it limits the application of genetic algorithm for the reason of its specialty and large calculation. In order to explore a new resolution, the author proposed a combining algorithm for structural optimization, which is based on genetic algorithm

Weijin Jiang I; Dingti Luol; Yusheng Xu; Xingming Sun

2004-01-01

171

Model results of optimized convex shapes for a solar thermal rocket thruster  

SciTech Connect

A computational, 3-D model for evaluating the performance of solar thermal thrusters is under development. The model combines Monte-Carlo and ray-tracing techniques to follow the ray paths of concentrated solar radiation through an axially symmetric heat-exchanger surface for both convex and concave cavity shapes. The enthalpy of a propellant, typically hydrogen gas, increases as it flows over the outer surface of the absorber/exchanger cavity. Surface temperatures are determined by the requirement that the input radiant power to surface elements balance with the reradiated power and heat conducted to the propellant. The model uses tabulated forms of surface emissivity and gas enthalpy. Temperature profiles result by iteratively calculating surface and propellant temperatures until the solutions converge to stable values. The model provides a means to determine the effectiveness of incorporating a secondary concentrator into the heat-exchanger cavity. A secondary concentrator increases the amount of radiant energy entering the cavity. The model will be used to evaluate the data obtained from upcoming experiments. Characteristics of some absorber/exchanger cavity shapes combined with optionally attached conical secondary concentrators for various propellant flow rates are presented. In addition, shapes that recover some of the diffuse radiant energy which would otherwise not enter the secondary concentrator are considered.

Cartier, S.L. [Sparta Inc., Edwards AFB, CA (United States). Phillips Lab.

1995-11-01

172

Crowding clustering genetic algorithm for multimodal function optimization  

Microsoft Academic Search

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs standard crowding strategy to form multiple niches

Qing Ling; Gang Wu; Zaiyue Yang; Qiuping Wang

2008-01-01

173

A New Active Set Algorithm for Box Constrained Optimization  

Microsoft Academic Search

An active set algorithm (ASA) for box constrained optimization is developed. The algorithm consists ofa nonmonotone gradient projection step, an unconstrained optimization step, and a set ofrules f or branching between the two steps. Global convergence to a stationary point is established. For a nondegenerate stationary point, the algorithm eventually reduces to uncon- strained optimization without restarts. Similarly, for a

William W. Hager; Hongchao Zhang

2006-01-01

174

Optimal hydrogenerator governor tuning with a genetic algorithm  

Microsoft Academic Search

The authors investigate the application of a genetic algorithm for optimizing the gains of a proportional-plus-integral controller for a hydrogenerator plant. The genetic algorithm was applied to optimal tuning of a governor for a hydrogenerator plant. Analog and digital simulation methods are compared for use in conjunction with the genetic algorithm optimization process. It is shown that analog plant simulation

J. E. Lansberry; L. Wozniak; D. E. Goldberg

1992-01-01

175

Improved Genetic Algorithms to Solving Constrained Optimization Problems  

Microsoft Academic Search

The slow convergence speed and the lack of effective constraint handling strategies are the major concerns when applying genetic algorithms (Gas) to constrained optimization problem. An improved genetic algorithm was proposed by dividing population into three parts: optimal subpopulation, elitists subpopulation and spare subpopulation. We applied genetic algorithm on three subpopulations with different evolutionary strategies. Isolation of optimal subpopulation was

Zhu Can; Liang Xi-Ming; Zhou Shu-renhu

2009-01-01

176

Bell-Curve Based Evolutionary Optimization Algorithm  

NASA Technical Reports Server (NTRS)

The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.

Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.

1998-01-01

177

Fast spectroscopic imaging using online optimal sparse k-space acquisition and projections onto convex sets reconstruction.  

PubMed

Long acquisition times, low resolution, and voxel contamination are major difficulties in the application of magnetic resonance spectroscopic imaging (MRSI). To overcome these difficulties, an online-optimized acquisition of k-space, termed sequential forward array selection (SFAS), was developed to reduce acquisition time without sacrificing spatial resolution. A 2D proton MRSI region of interest (ROI) was defined from a scout image and used to create a region of support (ROS) image. The ROS was then used to optimize and obtain a subset of k-space (i.e., a subset of nonuniform phase encodings) and hence reduce the acquisition time for MRSI. Reconstruction and processing software was developed in-house to process and reconstruct MRSI using the projections onto convex sets method. Phantom and in vivo studies showed that good-quality MRS images are obtainable with an approximately 80% reduction of data acquisition time. The reduction of the acquisition time depends on the area ratio of ROS to FOV (i.e., the smaller the ratio, the greater the time reduction). It is also possible to obtain higher-resolution MRS images within a reasonable time using this approach. MRSI with a resolution of 64 x 64 is possible with the acquisition time of the same as 24 x 24 using the traditional full k-space method. PMID:16680731

Gao, Yun; Strakowski, Stephen M; Reeves, Stanley J; Hetherington, Hoby P; Chu, Wen-Jang; Lee, Jing-Huei

2006-06-01

178

Convex reformulation of biologically-based multi-criteria intensity-modulated radiation therapy optimization including fractionation effects.  

PubMed

Finding fluence maps for intensity-modulated radiation therapy (IMRT) can be formulated as a multi-criteria optimization problem for which Pareto optimal treatment plans exist. To account for the dose-per-fraction effect of fractionated IMRT, it is desirable to exploit radiobiological treatment plan evaluation criteria based on the linear-quadratic (LQ) cell survival model as a means to balance the radiation benefits and risks in terms of biologic response. Unfortunately, the LQ-model-based radiobiological criteria are nonconvex functions, which make the optimization problem hard to solve. We apply the framework proposed by Romeijn et al (2004 Phys. Med. Biol. 49 1991-2013) to find transformations of LQ-model-based radiobiological functions and establish conditions under which transformed functions result in equivalent convex criteria that do not change the set of Pareto optimal treatment plans. The functions analysed are: the LQ-Poisson-based model for tumour control probability (TCP) with and without inter-patient heterogeneity in radiation sensitivity, the LQ-Poisson-based relative seriality s-model for normal tissue complication probability (NTCP), the equivalent uniform dose (EUD) under the LQ-Poisson model and the fractionation-corrected Probit-based model for NTCP according to Lyman, Kutcher and Burman. These functions differ from those analysed before in that they cannot be decomposed into elementary EUD or generalized-EUD functions. In addition, we show that applying increasing and concave transformations to the convexified functions is beneficial for the piecewise approximation of the Pareto efficient frontier. PMID:18941280

Hoffmann, Aswin L; den Hertog, Dick; Siem, Alex Y D; Kaanders, Johannes H A M; Huizenga, Henk

2008-11-21

179

On convexity of H-infinity Riccati solutions  

NASA Technical Reports Server (NTRS)

The authors revealed several important eigen properties of the stabilizing solutions of the two H-infinity Riccati equations and their product. Among them, the most prominent one is that the spectral radius of the product of these two Riccati solutions is a continuous, nonincreasing, convex function of gamma in the domain of interest. Based on these properties, quadratically convergent algorithms are developed to compute the optimal H-infinity norm. Two examples are used to illustrate the algorithms.

Li, X. P.; Chang, B. C.

1991-01-01

180

Application of the hybrid algorithm combining ant colony optimization algorithm with microgenetic algorithm to the optimization of multilayered radar absorbing coating  

Microsoft Academic Search

A new optimization technique based on the hybrid algorithm combining ant colony optimization algorithm with microgenetic algorithm is presented for the design of multilayered radar absorbing materials. During the optimization procedure the optimization constrained conditions are different in order to meet the practical requirements in the different frequency bands between 2 GHz and 18 GHz, and the multilayered radar absorbing

Kun Chao; Yunlin Liu; Rugui Yang

2008-01-01

181

Casting riser design optimization using genetic algorithms  

SciTech Connect

The design of rigging systems for castings in the foundry is largely based on past experience and empirical rules. Recent literature shows that attempts are being made to adopt a more scientific approach towards rigging design (location and size of risers, proper orientation of the casting, determination of parting plane, etc.) through the use of rule-based expert systems, process simulation and other tools. Riser design is a key element in the optimization of the overall rigging system since riser designs with large safety margins reduce yield and increase cost. This paper describes a methodology to optimize the riser design. A genetic algorithm is used for simplicity as well as robustness. Values of selected riser design parameters are examined using a modulus based approach to optimize the riser yield while achieving functional performance (i.e. effectiveness of the riser to adequately feed regions where shrinkage-type defects have a tendency of forming). Since the optimization is carried out on the solid model of the riser, the resultant design of the riser, together with the casting can be directly sent to a rapid prototyping system for production of a pattern.

Guleyupoglu, S.; Upadhya, G.; Paul, A.J.; Yu, K.O. [Concurrent Technologies Corp., Johnstown, PA (United States); Hill, J.L. [Univ. of Alabama, Tuscaloosa, AL (United States). Engineering Science and Mechanics Dept.

1995-12-31

182

Instrument design and optimization using genetic algorithms  

SciTech Connect

This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the genetic algorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of 'nonstandard' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.

Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter [Institut Laue-Langevin, BP 156, F-38042 Grenoble Cedex 9 (France); Hahn-Meitner Institut, Glienicker Strasse 100, D-14109 Berlin (Germany); Institut Laue-Langevin, BP 156, F-38042, Grenoble Cedex 9 (France)

2006-10-15

183

The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects  

E-print Network

The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects C. Yal#24;c#16;n Kaya School@maths.uwa.edu.au Abstract The Leap-Frog Algorithm was originally devised to #12;nd geodesics in connected complete with generalizing the mathematical rigour of the leap-frog algorithm to a class of optimal control problems

Noakes, Lyle

184

Search the Optimal Preference of Affinity Propagation Algorithm  

Microsoft Academic Search

In order to improve the clustering quality of the Affinity Propagation algorithm further and get more accurate number of clusters, this paper proposed a novel algorithm based on the Particles Swarm Optimization, which used In-Group Proportion index as fitness function to search the optimal preference of Affinity Propagation algorithm. Experimental results show that the predicted results had been tested with

Yi Zhong; Ming Zheng; Jianan Wu; Wei Shen; You Zhou; Chunguang Zhou

2012-01-01

185

Experimental Comparisons of Derivative Free Optimization Algorithms1  

E-print Network

-Box Optimization (BBO). 1 Invited Paper at the 8th International Symposium on Experimental Algorithms, June 3 International Symposium on Experimental Algorithms, Dortmund : Germany (2009)" #12;Because BBO is a frequent situation, many optimization methods (aka search algorithms) have been proposed to tackle BBO problems

Paris-Sud XI, Université de

186

GADO: A GENETIC ALGORITHM FOR CONTINUOUS DESIGN OPTIMIZATION  

E-print Network

of the Seventh International Conference on Genetic Algorithms, with Haym Hirsh as co­author [ Rasheed and HirshGADO: A GENETIC ALGORITHM FOR CONTINUOUS DESIGN OPTIMIZATION BY KHALED MOHAMED RASHEED GADO: A Genetic Algorithm for Continuous Design Optimization by Khaled Mohamed Rasheed Dissertation

Rasheed, Khaled

187

Real Coded Genetic Algorithm Optimization of Long Term Reservoir Operation  

Microsoft Academic Search

An optimization and simulation model holds promise as an efficient and robust method for long term reservoir operation, an increasingly important facet of managing water resources. Recently, genetic algorithms have been demonstrated to be highly effective optimization methods. According to previous studies, a real coded genetic algorithm (RGA) has many advantages over a binary coded genetic algorithm. Accordingly, this work

Li Chen

2003-01-01

188

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform  

E-print Network

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform Marcos L@eee.ufg.br, granato@eee.ufg.br Abstract-- A robust genetic circuit optimizer using Unscented Transform and Non-dominated Sorting Genetic Algorithm-II is presented. The algorithm provides significant decrease in compu- tational

Paris-Sud XI, Université de

189

Minimax algorithm for constructing an optimal control strategy in differential games with a Lipschitz payoff  

NASA Astrophysics Data System (ADS)

For a zero-sum differential game, an algorithm is proposed for computing the value of the game and constructing optimal control strategies with the help of stepwise minimax. It is assumed that the dynamics can be nonlinear and the cost functional of the game is the sum of an integral term and a terminal payoff function that satisfies the Lipschitz condition but can be neither convex nor concave. The players' controls are chosen from given sets that are generally time-dependent and unbounded. An error estimate for the algorithm is obtained depending on the number of partition points in the time interval and on the fineness of the spatial triangulation. Numerical results for an illustrative example are presented.

Ivanov, G. E.; Kazeev, V. A.

2011-04-01

190

Optimizing and evaluating algorithms for replicated data concurrency control  

SciTech Connect

Techniques for optimizing a static voting type algorithm are presented. Our optimization model is based on minimizing communications cost subject to a given availability constraint. We describe a semi-exhaustive algorithm for solving this model. This algorithm utilizes a novel signature-based method for identifying equivalent vote combinations, and also an efficient algorithm for computing availability. Static algorithms naturally have the advantage of simplicity; however, votes and quorum sizes are not allowed to vary. Therefore, the optimized static algorithm was compared against the available copies method, a dynamic algorithm, to understand the relative performance of the two types of algorithms. We found that if realistic reconfiguration times are assumed, then no one type of algorithm is uniformly better. The factors that influence relative performance have been identified. The available copies algorithm does better when the update traffic ratio is small, and the variability in the inter-site communications cost is low. 15 refs., 1 fig., 3 tabs.

Kumar, A.; Segev, A.

1989-02-01

191

Multiview stereo and silhouette consistency via convex functionals over convex domains.  

PubMed

We propose a convex formulation for silhouette and stereo fusion in 3D reconstruction from multiple images. The key idea is to show that the reconstruction problem can be cast as one of minimizing a convex functional, where the exact silhouette consistency is imposed as convex constraints that restrict the domain of feasible functions. As a consequence, we can retain the original stereo-weighted surface area as a cost functional without heuristic modifications of this energy by balloon terms or other strategies, yet still obtain meaningful (non-empty) reconstructions which are guaranteed to be silhouette-consistent. We prove that the proposed convex relaxation approach provides solutions that lie within a bound of the optimal solution. Compared to existing alternatives, the proposed method does not depend on initialization and leads to a simpler and more robust numerical scheme for imposing silhouette consistency obtained by projection onto convex sets. We show that this projection can be solved exactly using an efficient algorithm. We propose a parallel implementation of the resulting convex optimization problem on a graphics card. Given a photo-consistency map and a set of image silhouettes, we are able to compute highly accurate and silhouette-consistent reconstructions for challenging real-world data sets. In particular, experimental results demonstrate that the proposed silhouette constraints help to preserve fine-scale details of the reconstructed shape. Computation times depend on the resolution of the input imagery and vary between a few seconds and a couple of minutes for all experiments in this paper. PMID:20820076

Cremers, Daniel; Kolev, Kalin

2011-06-01

192

Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals  

Microsoft Academic Search

Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization

B. Sareni; L. Krahenbuhl; A. Nicolas

1998-01-01

193

An Overview of Evolutionary Algorithms for Parameter Optimization  

E-print Network

), and Genetic Algorithms (GAs). The comparison is performed with respect to certain characteristic components, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. FinallyAn Overview of Evolutionary Algorithms for Parameter Optimization Thomas BË?ack \\Lambda Hans

Hoffmann, Frank

194

A cross-layer optimization algorithm for wireless sensor network  

NASA Astrophysics Data System (ADS)

Energy is critical for typical wireless sensor networks (WSN) and how to energy consumption and maximize network lifetime are big challenges for Wireless sensor networks; cross layer algorithm is main method to solve this problem. In this paper, firstly, we analyze current layer-based optimal methods in wireless sensor network and summarize the physical, link and routing optimization techniques. Secondly we compare some strategies in cross-layer optimization algorithms. According to the analysis and summary of the current lifetime algorithms in wireless sensor network A cross layer optimization algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional Leach routing protocol. Simulation results show that this algorithm is an excellent cross layer algorithm for reducing energy consumption.

Wang, Yan; Liu, Le Qing

2010-07-01

195

Hybrid Approach to Optimal Packing Using Genetic Algorithm and Coulomb Potential Algorithm  

Microsoft Academic Search

It is difficult and computationally time-consuming to find the best possible solutions for blank packing problems, because they include a lot of underlying combinational conditions. This paper presents two approaches for packing two-dimensional irregular-shaped polygonal elements—a real-encoded genetic algorithm and a hybrid algorithm using a real-encoded genetic algorithm and a local optimization algorithm. The local optimization algorithm presented is a

Biswajit Mahanty; Rajneesh Kumar Agrawal; Shrikrishna Shrin; Sourish Chakravarty

2007-01-01

196

Convex initialization of the H2-optimal static output feedback problem Henrik Manum, Sigurd Skogestad, and Johannes Jaschke  

E-print Network

can be found by solving an iterative Riccati equation. For the case with white noise assumption on x0 is unsolved [4] so one cannot expect to find an analytic or convex numerical solution. The contribution

Skogestad, Sigurd

197

Optimizing System Performance Through Dynamic Disk Scheduling Algorithm Selection  

E-print Network

Optimizing System Performance Through Dynamic Disk Scheduling Algorithm Selection DANIEL L performance. New approaches and algorithms for disk scheduling have been developed in recent years scheduling of disk requests. Unfortunately, there has yet to be developed a single universal disk

Katchabaw, Michael James

198

Optimization Online Digest -- August 2012  

E-print Network

A Newton-Fixed Point Homotopy Algorithm for Nonlinear Complementarity Problems with Generalized ... An adaptive accelerated first-order method for convex optimization ... Bounds for nested law invariant coherent risk measures. Linwei Xin ...

199

Fast Approximate Convex Decomposition  

E-print Network

Approximate convex decomposition (ACD) is a technique that partitions an input object into "approximately convex" components. Decomposition into approximately convex pieces is both more efficient to compute than exact convex decomposition and can...

Ghosh, Mukulika

2012-10-19

200

An optimal adiabatic quantum query algorithm  

E-print Network

Quantum query complexity is known to be characterized by the so-called quantum adversary bound. While this result has been proved in the standard discrete-time model of quantum computation, it also holds for continuous-time (or Hamiltonian-based) quantum computation, due to a known equivalence between these two query complexity models. In this work, we revisit this result by providing a direct proof in the continuous-time model. One originality of our proof is that it draws new connections between the adversary bound, a modern theoretical computer science technique, and early theorems of quantum mechanics. Indeed, the proof of the lower bound is based on Ehrenfest's theorem, while the upper bound relies on the Adiabatic theorem, as we construct an optimal adiabatic quantum query algorithm.

Mathieu Brandeho; Jérémie Roland

2014-09-11

201

PDE Nozzle Optimization Using a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.

Billings, Dana; Turner, James E. (Technical Monitor)

2000-01-01

202

Application of the dual active set algorithm to quadratic network optimization  

Microsoft Academic Search

A new algorithm, the dual active set algorithm, is presented for solving a minimization problem with equality constraints and bounds on the variables. The algorithm identifies the active bound constraints by maximizing an unconstrained dual function in a finite number of iterations. Convergence of the method is established, and it is applied to convex quadratic programming. In its implementable form,

William W. Hager; Donald W. Hearn

1993-01-01

203

Greedy algorithms Algorithms for solving (optimization) problems typically go through a  

E-print Network

Greedy algorithms Algorithms for solving (optimization) problems typically go through a sequence of steps, with a set of choices at each step. A greedy algorithm always makes the choice that looks best at the moment, without regard for future consequence "take what you can get now" strategy Greedy algorithms do

Bai, Zhaojun

204

Optimization of computer vision algorithms for real time platforms  

Microsoft Academic Search

Real time computer vision applications like video streaming on cell phones, remote surveillance and virtual reality have stringent performance requirements but can be severely restrained by limited resources. The use of optimized algorithms is vital to meet real-time requirements especially on popular mobile platforms. This paper presents work on performance optimization of common computer vision algorithms such as correlation on

Pramod Poudel; Mukul Shirvaikar

2010-01-01

205

An Improved Genetic Algorithm for Reactive Power Optimization  

Microsoft Academic Search

In this paper, an Improve Genetic Algorithm (IGA) is applied to solve reactive power optimization (RPO) problem. The PRO problem is a highly nonlinear complex optimization problem and can be solved by enumeration method if without the advantages of evolutionary algorithms. The IGA modifies chromosomes based on the fundamentals of virtual population method. Stochastic crossover schemes are also employed in

Guang Ya Yang; Zhao Yang Dong

206

A Recursive Random Search Algorithm for Network Parameter Optimization  

E-print Network

considered a black art and is normally performed based on network administrators' experience, trial and error Simulator Network Network Model Optimization Black-box Algorithm Experiment Parameters Performance MetricA Recursive Random Search Algorithm for Network Parameter Optimization Tao Ye 1 Shivkumar

Kalyanaraman, Shivkumar

207

Hybrid Particle Swarm - Evolutionary Algorithm for Search and Optimization  

Microsoft Academic Search

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search prob- lems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO - evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations

Crina Grosan; Ajith Abraham; Sangyong Han; Alexander F. Gelbukh

2005-01-01

208

Particle swarm optimization-based algorithm for lightning location estimation  

Microsoft Academic Search

Lightning early warning requires lightning location systems to process sensors' measurements in near real time. A new algorithm based on particle swarm optimization (PSO) is developed to provide reliable and immediate solutions of lightning location and occurrence time. Comparing with iterative-type algorithms, the PSO-based algorithm does not require initial value and is easy to program. Different parameter choice schemes for

Zhixiang Hu; Yinping Wen; Wenguang Zhao; Hongping Zhu

2010-01-01

209

Adaptive branch and bound algorithm for selecting optimal features  

Microsoft Academic Search

We propose a new adaptive branch and bound algorithm for selecting the optimal subset of features in pattern recognition applications. The algorithm improves the search speed by avoiding unnecessary criterion function calculations at nodes in the solution tree. Our algorithm includes the following new properties: (i) ordering the tree nodes by the significance of features during construction of the tree,

Songyot Nakariyakul; David P. Casasent

2007-01-01

210

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN  

EPA Science Inventory

While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...

211

An optimal online algorithm for metrical task systems  

Microsoft Academic Search

In practice, almost all dynamic systems require decisions to be made online, without full knowledge of their future impact on the system. We introduce a general model for the processing of sequences of tasks and develop a general online decision algorithm. We show that, for an important class of special cases, this algorithm is optimal among all online algorithms.Specifically, a

Allan Borodin; Nathan Linial; Michael E. Saks

1987-01-01

212

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization  

NASA Technical Reports Server (NTRS)

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.

2004-01-01

213

A New Optimized GA-RBF Neural Network Algorithm  

PubMed Central

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid. PMID:25371666

Zhao, Dean; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

214

An improved marriage in honey bees optimization algorithm for single objective unconstrained optimization.  

PubMed

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416

Celik, Yuksel; Ulker, Erkan

2013-01-01

215

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization  

PubMed Central

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416

Celik, Yuksel; Ulker, Erkan

2013-01-01

216

An Efficient Hybrid Algorithm for Optimization of Discrete Structures  

Microsoft Academic Search

Presented in this paper is a hybrid algorithm for the design of discrete structures like trusses. The proposed algorithm called\\u000a Discrete Structures Optimization (DSO) is based on the Evolutionary Structural Optimization (ESO) [1,2]. In DSO, material\\u000a is removed from the structural elements based on the strain energy. DSO is a two stage process. First stage is the topology\\u000a optimization where

Amitay Isaacs; Tapabrata Ray; Warren Smith

2008-01-01

217

Genetic-Algorithm Tool For Search And Optimization  

NASA Technical Reports Server (NTRS)

SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.

Wang, Lui; Bayer, Steven

1995-01-01

218

Global Tree Optimization: A Non-greedy Decision Tree Algorithm  

Microsoft Academic Search

A non-greedy approach for constructing globally optimalmultivariate decision trees with fixed structure is proposed.Previous greedy tree construction algorithms arelocally optimal in that they optimize some splitting criterionat each decision node, typically one node at a time.In contrast, global tree optimization explicitly considersall decisions in the tree concurrently. An iterative linearprogramming algorithm is used to minimize the classificationerror of the entire

Kristin P. Bennett

1994-01-01

219

Online Convex Optimization-Based Algorithm for Thermal Management of MPSoCs  

E-print Network

Embedded Systems Laboratory (ESL) EPFL Lausanne, Switzerland david.atienza@epfl.ch Giovanni De Micheli architecture, which show that the proposed method outperforms state-of-the-art thermal management approaches of attention. Many state-of-the-art thermal control policies manage power con- sumption via dynamic frequency

De Micheli, Giovanni

220

A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and  

E-print Network

A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and Electromagnetics R. The objective functions in the optimization problem measure the aerodynamic feasibil­ ity based on the drag been optimized with respect to only one discipline such as aerodynamics or electromagnetics. Although

Coello, Carlos A. Coello

221

SEARCH OPTIMIZATION USING HYBRID PARTICLE SUB SWARMS AND EVOLUTIONARY ALGORITHMS  

Microsoft Academic Search

Particle Swarm Optimization (PSO) technique proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a variant of the PSO technique named Independent Neighborhoods Particle Swarm Optimization (INPSO) dealing with sub-swarms for solving the well known geometrical place problems. Finding the geometrical place can be sometimes

CRINA GROSAN; AJITH ABRAHAM; MONICA NICOARA

2005-01-01

222

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization  

NASA Technical Reports Server (NTRS)

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.

2005-01-01

223

Crystal-structure prediction via the Floppy-Box Monte Carlo algorithm: Method and application to hard (non)convex particles  

NASA Astrophysics Data System (ADS)

In this paper, we describe the way to set up the floppy-box Monte Carlo (FBMC) method [L. Filion, M. Marechal, B. van Oorschot, D. Pelt, F. Smallenburg, and M. Dijkstra, Phys. Rev. Lett. 103, 188302 (2009)] to predict crystal-structure candidates for colloidal particles. The algorithm is explained in detail to ensure that it can be straightforwardly implemented on the basis of this text. The handling of hard-particle interactions in the FBMC algorithm is given special attention, as (soft) short-range and semi-long-range interactions can be treated in an analogous way. We also discuss two types of algorithms for checking for overlaps between polyhedra, the method of separating axes and a triangular-tessellation based technique. These can be combined with the FBMC method to enable crystal-structure prediction for systems composed of highly shape-anisotropic particles. Moreover, we present the results for the dense crystal structures predicted using the FBMC method for 159 (non)convex faceted particles, on which the findings in [J. de Graaf, R. van Roij, and M. Dijkstra, Phys. Rev. Lett. 107, 155501 (2011)] were based. Finally, we comment on the process of crystal-structure prediction itself and the choices that can be made in these simulations.

de Graaf, Joost; Filion, Laura; Marechal, Matthieu; van Roij, René; Dijkstra, Marjolein

2012-12-01

224

Optimization Online - Integer Programming Submissions - 2012  

E-print Network

A conic representation of the convex hull of disjunctive sets and conic cuts for integer ... Two-stage Models and Algorithms for Optimizing Infrastructure Design and ... Solving mixed integer nonlinear programming problems for mine production ...

225

Genetic algorithms for multicriteria shape optimization of induction furnace  

NASA Astrophysics Data System (ADS)

In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.

K?s, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

2012-09-01

226

An efficient algorithm for the earliness-tardiness scheduling problem  

E-print Network

Sep 7, 2005 ... jobs and that can solve problems with even more general non-convex cost functions. The ... Fry et al. [12] who proposed algorithms viable for 20 and 25 jobs respectively. ...... www.optimization-online.org, October 2004.

2005-09-07

227

EFFICIENT ALGORITHMS FOR THE OPTIMAL-RATIO REGION DETECTION PROBLEMS IN DISCRETE GEOMETRY WITH APPLICATIONS*  

PubMed Central

In this paper, we study several interesting optimal-ratio region detection (ORD) problems in d-D (d ? 3) discrete geometric spaces, which arise in high dimensional medical image segmentation. Given a d-D voxel grid of n cells, two classes of geometric regions that are enclosed by a single or two coupled smooth heighfield surfaces defined on the entire grid domain are considered. The objective functions are normalized by a function of the desired regions, which avoids a bias to produce an overly large or small region resulting from data noise. The normalization functions that we employ are used in real medical image segmentation. To our best knowledge, no previous results on these problems in high dimensions are known. We develop a unified algorithmic framework based on a careful characterization of the intrinsic geometric structures and a nontrivial graph transformation scheme, yielding efficient polynomial time algorithms for solving these ORD problems. Our main ideas include the following. We observe that the optimal solution to the ORD problems can be obtained via the construction of a convex hull for a set of O(n) unknown 2-D points using the hand probing technique. The probing oracles are implemented by computing a minimum s-t cut in a weighted directed graph. The ORD problems are then solved by O(n) calls to the minimum s-t cut algorithm. For the class of regions bounded by a single heighfield surface, our further investigation shows that the O(n) calls to the minimum s-t cut algorithm are on a monotone parametric flow network, which enables to detect the optimal-ratio region in the complexity of computing a single maximum flow.

Wu, Xiaodong

2014-01-01

228

Optimized algorithm for synthetic aperture imaging  

Microsoft Academic Search

We present a novel synthetic aperture imaging algorithm based on concepts used in synthetic aperture radar (SAR) and sonar (SAS). The algorithm, based on a convolution model of the imaging system developed in the frequency domain, accounts for the beam-pattern of the finite sized transducer used in the synthetic aperture. A 2D Fourier transform is used for the calculation of

T. Stepinski; F. Lingvall

2004-01-01

229

Cellular Probabilistic Evolutionary Algorithms for Real-Coded Function Optimization  

NASA Astrophysics Data System (ADS)

We propose a novel Cellular Probabilistic Evolutionary Algorithm (CPEA) based on a probabilistic representation of solutions for real coded problems. In place of binary integers, the basic unit of information here is a probability density function. This probabilistic coding allows superposition of states for a more efficient algorithm. Furthermore, the cellular structure of the proposed algorithm aims to provide an appropriate tradeoff between exploitation and exploration. Experimental results show that the performance of CPEA in several numerical benchmark problems is improved when compared with other evolutionary algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA).

Akbarzadeh T., M. R.; Tayarani N., M.

230

Parallel projected variable metric algorithms for unconstrained optimization  

NASA Technical Reports Server (NTRS)

The parallel variable metric optimization algorithms of Straeter (1973) and van Laarhoven (1985) are reviewed, and the possible drawbacks of the algorithms are noted. By including Davidon (1975) projections in the variable metric updating, researchers can generalize Straeter's algorithm to a family of parallel projected variable metric algorithms which do not suffer the above drawbacks and which retain quadratic termination. Finally researchers consider the numerical performance of one member of the family on several standard example problems and illustrate how the choice of the displacement vectors affects the performance of the algorithm.

Freeman, T. L.

1989-01-01

231

PCB drill path optimization by combinatorial cuckoo search algorithm.  

PubMed

Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process. PMID:24707198

Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G

2014-01-01

232

Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with  

E-print Network

Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with Decoupled-7501 Patrick Xavier Sandia National Laboratories, Albuquerque NM 87185-0951 Keywords: robot motion planning, kinodynamics, polyhedral obstacles Abstract: We consider the following problem: given a robot system, nd

Richardson, David

233

Dutch Named Entity Recognition: Optimizing Features, Algorithms, and Output  

E-print Network

Dutch Named Entity Recognition: Optimizing Features, Algorithms, and Output Toine Bogers a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Named Entity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Applications of Named Entity Recognition . . . . . . . . . . . . . . . . . . . . 4 1

Bogers, Toine

234

A Sequential Quadratic Optimization Algorithm with Rapid Infeasibility Detection  

E-print Network

, Frank E. Curtis, and Hao Wang Lehigh Industrial and Systems Engineering COR@L Technical Report 2012T-12, FRANK E. CURTIS, AND HAO WANG Abstract. We present a sequential quadratic optimization (SQO) algorithm

Snyder, Larry

235

Application of a gradient-based algorithm to structural optimization  

E-print Network

Optimization methods have shown to be efficient at improving structural design, but their use is limited in the engineering practice by the difficulty of adapting state-of-the-art algorithms to particular engineering ...

Ghisbain, Pierre

2009-01-01

236

Optimization of image processing algorithms on mobile platforms  

Microsoft Academic Search

This work presents a technique to optimize popular image processing algorithms on mobile platforms such as cell phones, net-books and personal digital assistants (PDAs). The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has a mandate to optimize them so as to meet real-time

Pramod Poudel; Mukul Shirvaikar

2011-01-01

237

Standard Harmony Search Algorithm for Structural Design Optimization  

Microsoft Academic Search

Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial\\u000a gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however,\\u000a reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the\\u000a problem, the result may depend on

Kang Seok Lee

238

Parameters optimization on DHSVM model based on a genetic algorithm  

Microsoft Academic Search

Due to the multiplicity of factors including weather, the underlying surface and human activities, the complexity of parameter\\u000a optimization for a distributed hydrological model of a watershed land surface goes far beyond the capability of traditional\\u000a optimization methods. The genetic algorithm is a new attempt to find a solution to this problem. A genetic algorithm design\\u000a on the Distributed-Hydrology-Soil-Vegetation model

Changqing Yao; Zhifeng Yang

2009-01-01

239

A scaled nonlinear conjugate gradient algorithm for unconstrained optimization  

Microsoft Academic Search

The best spectral conjugate gradient algorithm by (Birgin, E. and Martínez, J.M., 2001, A spectral conjugate gradient method for unconstrained optimization. Applied Mathematics and Optimization, 43, 117–128). which is mainly a scaled variant of (Perry, J.M., 1977, A class of Conjugate gradient algorithms with a two step varaiable metric memory, Discussion Paper 269, Center for Mathematical Studies in Economics and

Neculai Andrei

2008-01-01

240

Truncated-newtono algorithms for large-scale unconstrained optimization  

Microsoft Academic Search

We present an algorithm for large-scale unconstrained optimization based onNewton's method. In large-scale optimization, solving\\u000a the Newton equations at each iteration can be expensive and may not be justified when far from a solution. Instead, an inaccurate\\u000a solution to the Newton equations is computed using a conjugate gradient method. The resulting algorithm is shown to have strong\\u000a convergence properties and

Ron S. Dembo; Trond Steihaug

1983-01-01

241

A chaotic firefly algorithm applied to reliability-redundancy optimization  

Microsoft Academic Search

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer non- linear programming. On the other hand, a broad class of meta- heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA

Leandro dos Santos Coelho; Diego Luis de Andrade Bernert; Viviana Cocco Mariani

2011-01-01

242

Using Genetic Algorithms to Optimize Operating System Parameters  

E-print Network

Using Genetic Algorithms to Optimize Operating System Parameters Dror G. Feitelson Michael Naaman files containing information about the local workload, and genetic algorithms are used to select of parame­ ters that can be modified by the system administrator in order to tune system performance

Feitelson, Dror

243

Optimal design of plant lighting system by genetic algorithms  

Microsoft Academic Search

A genetic algorithm technique is developed for the optimal design of a supplemental lighting system for greenhouse crop production. The approach uses the evolutionary parallel search capabilities of genetic algorithms to design the pattern layout of the lamps (luminaires), their mounting heights and their wattages. The total number and the exact positions of luminaires are not predefined (even though possible

Konstantinos P. Ferentinos; L. D. Albright

2005-01-01

244

Optimized View Frustum Culling Algorithms Ulf Assarsson and Tomas Moller  

E-print Network

. First we develop a fast basic VFC algorithm. Then we suggest and eval- uate four further optimizations, which are independent of each other and works for all kinds of VFC algorithms that test the bounding of the scene graph. A view frustum culler (VFC) culls away the nodes that lie outside the view frustum, i

Assarsson, Ulf

245

Optimal network problem: a branch-and-bound algorithm  

Microsoft Academic Search

The problem of selecting a subset of links so as to minimize the sum of shortest path distances between all pairs of nodes, subject to a budget constraint on total length of links, may be solved by a modification of a branch-and-bound algorithm developed for optimal variable selection problems in statistics. The modified algorithm is described in detail, and encouraging

D E Boyce; A Farhi; R Weischedel

1973-01-01

246

A Hybrid Genetic Algorithm for Routing Optimization in IP Networks  

E-print Network

engineering are demonstrated. Keywords ­ IP Traffic engineering, genetic algorithm, bandwidth-delay sensitiveA Hybrid Genetic Algorithm for Routing Optimization in IP Networks Utilizing Bandwidth and Delay traffic engineering, which relies on conventional, destination-based routing protocols. We introduce

Riedl, Anton

247

NORTHWESTERN UNIVERSITY Algorithms for LargeScale Nonlinear Optimization  

E-print Network

of Computer Engineering By Richard Alan Waltz EVANSTON, ILLINOIS June 2002 #12; c fl Copyright by Richard Alan Waltz 2002 All Rights Reserved ii #12; ABSTRACT Algorithms for Large­Scale Nonlinear Optimization Richard Alan Waltz Ph.D. advisor: Jorge Nocedal We investigate two algorithmic approaches

Waltz, Richard A.

248

A novel Fly Optimization Algorithm for swarming application  

Microsoft Academic Search

This paper presents an initial development stage of Fly Optimization Algorithm which will be used for the path planning system of a swarm of autonomous surface vehicles. This algorithm was initially designed to be implemented for a swarm of robots which would be able to locate the deepest portion of lakes. The ability of the robots to reach the designated

Zulkifli Zainal Abidin; Umi Kalthum Ngah; Mohd Rizal Arshad; Ong Boon Ping

2010-01-01

249

A Filter-Based Evolutionary Algorithm for Constrained Optimization  

Microsoft Academic Search

We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent

Lauren M. Clevenger; Lauren Ferguson; William E. Hart

2005-01-01

250

Time screening optimization algorithm for ROSAT PSPC/HRI observations  

E-print Network

Time screening optimization algorithm for ROSAT PSPC/HRI observations F. Bocchino 1 , M. Barbera 1 an algorithm for time screening PSPC/HRI observations in order to maximize the signal to noise (SNR) ratio signal to noise ratio (SNR) computed in a time screened observation (SNR) s and in the entire unscreened

251

A new discrete filled function algorithm for discrete global optimization  

Microsoft Academic Search

A definition of the discrete filled function is given in this paper. Based on the definition, a discrete filled function is proposed. Theoretical properties of the proposed discrete filled function are investigated, and an algorithm for discrete global optimization is developed from the new discrete filled function. The implementation of the algorithms on several test problems is reported with satisfactory

Yang Yongjian; Liang Yumei

2007-01-01

252

Genetic algorithm optimization applied to electromagnetics: a review  

Microsoft Academic Search

Genetic algorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic genetic algorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are

Daniel S. Weile; Eric Michielssen

1997-01-01

253

A parallel variable metric optimization algorithm  

NASA Technical Reports Server (NTRS)

An algorithm, designed to exploit the parallel computing or vector streaming (pipeline) capabilities of computers is presented. When p is the degree of parallelism, then one cycle of the parallel variable metric algorithm is defined as follows: first, the function and its gradient are computed in parallel at p different values of the independent variable; then the metric is modified by p rank-one corrections; and finally, a single univariant minimization is carried out in the Newton-like direction. Several properties of this algorithm are established. The convergence of the iterates to the solution is proved for a quadratic functional on a real separable Hilbert space. For a finite-dimensional space the convergence is in one cycle when p equals the dimension of the space. Results of numerical experiments indicate that the new algorithm will exploit parallel or pipeline computing capabilities to effect faster convergence than serial techniques.

Straeter, T. A.

1973-01-01

254

An Ellipsoidal Branch and Bound Algorithm for Global Optimization  

Microsoft Academic Search

A branch and bound algorithm is developed for global optimization. Branching\\u000ain the algorithm is accomplished by subdividing the feasible set using\\u000aellipses. Lower bounds are obtained by replacing the concave part of the\\u000aobjective function by an affine underestimate. A ball approximation algorithm,\\u000aobtained by generalizing of a scheme of Lin and Han, is used to solve the\\u000aconvex

William W. Hager; Dzung T. Phan

2009-01-01

255

Genetic algorithm-based optimization for cognitive radio networks  

Microsoft Academic Search

Genetic algorithms are well suited for optimization problems involving large search spaces. In this paper, we present several approaches designed to enhance the convergence time and\\/or improve the performance results of genetic algorithm-based search engine for cognitive radio networks, including techniques such as population adaptation, variable quantization, variable adaptation, and multi-objective genetic algorithms (MOGA). Note that the time required for

Si Chen; Timothy R. Newman; Joseph B. Evans; Alexander M. Wyglinski

2010-01-01

256

A General Greedy Approximation Algorithm with Applications  

E-print Network

A General Greedy Approximation Algorithm with Applications Tong Zhang IBM T.J. Watson Research Center Yorktown Heights, NY 10598 tzhang@watson.ibm.com Abstract Greedy approximation algorithms have a general greedy algorithm for solving a class of convex optimization problems. We derive a bound

Zhang, Tong

257

New evolutionary algorithm for EBG materials optimization  

NASA Astrophysics Data System (ADS)

EBG structures are typically two or three dimensional periodic media characterized by the capability to inhibit the electromagnetic wave propagation for each angle and each polarization in a specific frequency band. These complex structures present different degrees of freedom, that can be used to optimize the performances of the application. On the other hand, the management of different degrees of freedom can result in the increasing of the complexity in the entire device-design procedure. The aim of this research is to analyse the optimization of EBG materials by means of a new technique: the Genetical Swarm Optimization (GSO). This approach consists of a co-operation of GA and PSO. The GSO results in a fast method for optimization of complex nonlinear objective functions and its wider potential makes it suitable for every electromagnetic applications. These optimized synthetic materials can represent an opportunity for the development and design of innovative electromagnetic devices.

Gandelli, Alessandro; Grimaccia, Francesco; Mussetta, Marco; Pirinoli, Paola; Zich, Riccardo E.

2004-02-01

258

Branch-and-Cut Algorithms for Combinatorial Optimization Problems1  

E-print Network

Branch-and-Cut Algorithms for Combinatorial Optimization Problems1 John E. Mitchell2 Mathematical of optimality. We describe how a branch-and-cut method can be tailored to a specific integer programming problem://www.math.rpi.edu/~mitchj April 19, 1999, revised September 7, 1999. Abstract Branch-and-cut methods are very successful

Mitchell, John E.

259

BranchandCut Algorithms for Combinatorial Optimization Problems 1  

E-print Network

Branch­and­Cut Algorithms for Combinatorial Optimization Problems 1 John E. Mitchell 2 Mathematical of optimality. We describe how a branch­and­cut method can be tailored to a specific integer programming problem://www.math.rpi.edu/�mitchj April 19, 1999, revised September 7, 1999. Abstract Branch­and­cut methods are very successful

Mitchell, John E.

260

Model Specification Searches Using Ant Colony Optimization Algorithms  

ERIC Educational Resources Information Center

Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.

Marcoulides, George A.; Drezner, Zvi

2003-01-01

261

Algorithm for the Optimal Riding Scheme Problem in Public traffic  

Microsoft Academic Search

A two-stage algorithm is proposed for the optimal riding scheme problem in public traffic querying system. The first stage is to find out the least transfer schemes, in which bus line network model is presented to convert the least transfer scheme problem into the shortest path problem. The second stage is to search out the optimal riding scheme from the

Dong Jiyang; Chen Luzhuo

2005-01-01

262

Optimizing the reservoir operating rule curves by genetic algorithms  

Microsoft Academic Search

Genetic algorithms, founded upon the principle of evolution, are applicable to many optimization problems, especially popular for solving parameter optimization problems. Reservoir operating rule curves are the most common way for guiding and managing the reservoir operation. These rule curves traditionally are derived through intensive simulation techniques. The main aim of this study is to investigate the efficiency and effectiveness

Fi-John Chang; Li Chen; Li-Chiu Chang

2005-01-01

263

Design optimization of electrical machines using genetic algorithms  

Microsoft Academic Search

The application of genetic algorithms (GAs) to the design optimization of electromagnetic devices is presented in detail. The method is demonstrated on a magnetizer by optimizing its pole face to obtain the desired magnetic flux density distribution. The shape of the pole face is constructed from the control points by means of uniform nonrational b-splines

G. F. Uler; O. A. Mohammed; Chang-Seop Koh

1995-01-01

264

Multi-swingby optimization of mission to Saturn using global optimization algorithms  

NASA Astrophysics Data System (ADS)

Based on the trajectory design of a mission to Saturn, this paper discusses four different trajectories in various swingby cases. We assume a single impulse to be applied in each case when the spacecraft approaches a celestial body. Some optimal trajectories of EJS, EMS, EVEJS and EVVEJS flying sequences are obtained using five global optimization algorithms: DE, PSO, DP, the hybrid algorithm PSODE and another hybrid algorithm, DPDE. DE is proved to be superior to other non-hybrid algorithms in the trajectory optimization problem. The hybrid algorithm of PSO and DE can improve the optimization performance of DE, which is validated by the mission to Saturn with given swingby sequences. Finally, the optimization results of four different swingby sequences are compared with those of the ACT of ESA.

Zhu, Kaijian; Li, Junfeng; Baoyin, Hexi

2009-12-01

265

A Discrete Lagrangian Algorithm for Optimal Routing Problems  

SciTech Connect

The ideas of discrete Lagrangian methods for conservative systems are exploited for the construction of algorithms applicable in optimal ship routing problems. The algorithm presented here is based on the discretisation of Hamilton's principle of stationary action Lagrangian and specifically on the direct discretization of the Lagrange-Hamilton principle for a conservative system. Since, in contrast to the differential equations, the discrete Euler-Lagrange equations serve as constrains for the optimization of a given cost functional, in the present work we utilize this feature in order to minimize the cost function for optimal ship routing.

Kosmas, O. T.; Vlachos, D. S.; Simos, T. E. [University of Peloponnese, 22100 Tripoli (Greece)

2008-11-06

266

Approximation algorithms for trilinear optimization with nonconvex ...  

E-print Network

Apr 2, 2011 ... mization problems, which has a close relationship with the trilinear optimization problems. ..... rational numbers 0 encoding lengths are ..... From linear algebra, we immediately have another conclusion.

2011-04-02

267

Approximate algorithms for Space Station Maneuver Optimization  

E-print Network

. Torque smoothing is used to avoid discontinuous jumps in the control which would excite vibrational modes in the structure. The switch times, maximum thrust magnitudes, and optimal final maneuver times are determined using the MATLAB built-in function...

Mur-Dongil, Andres

2012-06-07

268

Approximation algorithms for combinatorial optimization under uncertainty  

E-print Network

Combinatorial optimization problems arise in many fields of industry and technology, where they are frequently used in production planning, transportation, and communication network design. Whereas in the context of classical ...

Minkoff, Maria, 1976-

2003-01-01

269

Parallel Algorithms for Big Data Optimization  

E-print Network

Index Terms—Parallel optimization, Distributed methods, Ja- cobi method ... Usually the nonsmooth term is used to ..... dard Armijo-like line-search procedure or a (suitably small) constant ..... enter the identification phase xk i is not zero, the

2014-02-21

270

OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM  

SciTech Connect

This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.

Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam [Queensland University of Technology, Brisbane, Queensland (Australia); Ivanovich, Grujica [Ergon Energy, Toowoomba, Queensland (Australia)

2010-06-15

271

Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms  

E-print Network

(1995) optimized the drilling schedule and well location in an oil reservoir through a traveling salesman structure with the use of Simulated Annealing. Bittencourt and Horne (1997) approached the well placement optimization problem using a genetic... robust, stochastic, and streamlined optimization method. Genetic Algorithms ?efficiently exploit historical information to speculate on new search points with expected improved performance.? (Goldberg 1989) The GA population is represented by a...

Gibbs, Trevor Howard

2011-08-08

272

Artificial bee colony algorithm for solving optimal power flow problem.  

PubMed

This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem. PMID:24470790

Le Dinh, Luong; Vo Ngoc, Dieu; Vasant, Pandian

2013-01-01

273

A Hybrid Ant Colony Algorithm for Loading Pattern Optimization  

NASA Astrophysics Data System (ADS)

Electricité de France (EDF) operates 58 nuclear power plant (NPP), of the Pressurized Water Reactor (PWR) type. The loading pattern (LP) optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R&D has developed automatic optimization tools that assist the experts. The latter can resort, for instance, to a loading pattern optimization software based on ant colony algorithm. This paper presents an analysis of the search space of a few realistic loading pattern optimization problems. This analysis leads us to introduce a hybrid algorithm based on ant colony and a local search method. We then show that this new algorithm is able to generate loading patterns of good quality.

Hoareau, F.

2014-06-01

274

Binary wavefront optimization using a genetic algorithm  

NASA Astrophysics Data System (ADS)

We demonstrate the use of a genetic algorithm with binary amplitude modulation of light through turbid media. We apply this method to binary amplitude modulation with a digital micromirror device. We achieve the theoretical maximum enhancement of 64 with 384 segments, and an enhancement of 320 with 6144 segments.

Zhang, Xiaolong; Kner, Peter

2014-12-01

275

Benchmarking Derivative-Free Optimization Algorithms  

E-print Network

problems, ordinary or partial differential equations) that describe the ... The computational noise associated with these complex simulations means that ... algorithms are compared by their trajectories (plot of the best function value against the ..... We now explore an extension of Theorem 2.1 to nonlinear functions that is ...

2008-05-13

276

Genetic algorithm optimization of feedback control systems  

Microsoft Academic Search

In this paper we are concerned with Smart Materials that contain many actuators and sensors along with digital signal processing electronics that allow for the implementation of a control algorithm. Smart Materials have been proposed for the active control of sound from a vibrating structure. Here we investigate the design of structural control systems for these Smart Structures for noise

Douglas K. Lindner; Gregory A. Zvonar; George C. Kirby; Grant M. Emery

1996-01-01

277

Searching for Pareto-optimal Randomised Algorithms  

E-print Network

the potential benefits of allowing randomised algorithms to use non-uniform probability distributions. We show. Clark1 1 Department of Computer Science, University of York, UK {millard, jac}@cs.york.ac.uk 2 School traditionally make stochastic deci- sions based on the result of sampling from a uniform probability dis

White, David R.

278

A training algorithm for optimal margin classifiers  

Microsoft Academic Search

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of

Bernhard E. Boser; Isabelle M. Guyon; Vladimir N. Vapnik

1992-01-01

279

Computational Experience with a New Class of Convex Underestimators: Box-constrained NLP Problems  

Microsoft Academic Search

In Akrotirianakis and Floudas (2004) we presented the theoretical foundations of a new class of convex underestimators for C2 nonconvex functions. In this paper, we present computational experience with those underestimators incorporated within a Branch-and-Bound algorithm for box-conatrained problems. The algorithm can be used to solve global optimization problems that involve C2 functions. We discuss several ways of incorporating the

Ioannis G. Akrotirianakis; Christodoulos A. Floudas

2004-01-01

280

A solution quality assessment method for swarm intelligence optimization algorithms.  

PubMed

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method. PMID:25013845

Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

2014-01-01

281

Engineering Optimization Using a Simple Evolutionary Algorithm  

Microsoft Academic Search

This paper presents a simple Evolution Strat- egy and three simple selection criteria to solve engineer- ing optimization problems. This approach avoids the use of a penalty function to deal with constraints. Its main advan- tage is that it does not require the definition of extra pa- rameters, other than those used by the evolution strategy. A self-adaptation mechanism allows

Efrén Mezura-montes; Carlos A. Coello Coello; Ricardo Landa-Becerra

2003-01-01

282

Performance Trend of Different Algorithms for Structural Design Optimization  

NASA Technical Reports Server (NTRS)

Nonlinear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Center, a project was initiated to assess performance of different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with the sequential unconstrained minimizations technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.

1996-01-01

283

Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications  

NASA Technical Reports Server (NTRS)

Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.

1996-01-01

284

Optimization of reliability allocation strategies through use of genetic algorithms  

SciTech Connect

This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.

Campbell, J.E.; Painton, L.A.

1996-08-01

285

Bracketing to speed convergence illustrated on the von Newmann algorithm for finding a feasible solution to a linear program with a convexity contraint. Technical report  

SciTech Connect

Analogous to gunners firing trial shots to bracket a target in order to adjust direction and distance, we demonstate that it is sometimes faster not to apply an algorithm directly, but to roughly approximately solve several perturbations of the problem and then combine these rough approximations to get an exact solution. To find a feasible solution to an m-equation linear program with a convexity constraint, the von Neumann Algorithm generates a sequence of approximate solutions which converge very slowly to the right hand side b{sup 0}. However, it can be redirected so that in the first few iterations it is guaranteed to move rapidly towards the neighborhood of one of m + 1 perturbed right hand sides {cflx b}{sup i}, then redirected in turn to the next {cflx b}{sup i}. Once within the neighborhood of each {cflx b}{sup i}, a weighted sum of the approximate solutions. {bar x}{sup i} yields the exact solution of the unperturbed problem where the weights are found by solving a system of m + 1 equations in m + 1 unknowns. It is assumed an r > 0 is given for which the problem is feasible for all right hand sides b whose distance {parallel}b - b{sup 0}{parallel}{sub 2} {le} r. The feasible solution is found in less than 4(m+ 1){sup 3}/r{sup 2} iterations. The work per iteration is {delta}mn + 2m + n + 9 multiplications plus {delta}mn + m + n + 9 additions or comparisons where {delta} is the density of nonzero coeffients in the matrix.

Dantzig, G.B.

1992-10-01

286

Bracketing to speed convergence illustrated on the von Newmann algorithm for finding a feasible solution to a linear program with a convexity contraint  

SciTech Connect

Analogous to gunners firing trial shots to bracket a target in order to adjust direction and distance, we demonstate that it is sometimes faster not to apply an algorithm directly, but to roughly approximately solve several perturbations of the problem and then combine these rough approximations to get an exact solution. To find a feasible solution to an m-equation linear program with a convexity constraint, the von Neumann Algorithm generates a sequence of approximate solutions which converge very slowly to the right hand side b[sup 0]. However, it can be redirected so that in the first few iterations it is guaranteed to move rapidly towards the neighborhood of one of m + 1 perturbed right hand sides [cflx b][sup i], then redirected in turn to the next [cflx b][sup i]. Once within the neighborhood of each [cflx b][sup i], a weighted sum of the approximate solutions. [bar x][sup i] yields the exact solution of the unperturbed problem where the weights are found by solving a system of m + 1 equations in m + 1 unknowns. It is assumed an r > 0 is given for which the problem is feasible for all right hand sides b whose distance [parallel]b - b[sup 0][parallel][sub 2] [le] r. The feasible solution is found in less than 4(m+ 1)[sup 3]/r[sup 2] iterations. The work per iteration is [delta]mn + 2m + n + 9 multiplications plus [delta]mn + m + n + 9 additions or comparisons where [delta] is the density of nonzero coeffients in the matrix.

Dantzig, G.B.

1992-10-01

287

Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization.  

PubMed

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Wang, Peng; Zhu, Zhouquan; Huang, Shuai

2013-01-01

288

Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization  

PubMed Central

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Zhu, Zhouquan

2013-01-01

289

Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces  

NASA Technical Reports Server (NTRS)

Robonaut, the humanoid robot developed at the Dexterous Robotics Laboratory at NASA Johnson Space Center serves as a testbed for human-robot collaboration research and development efforts. One of the primary efforts investigates how adjustable autonomy can provide for a safe and more effective completion of manipulation-based tasks. A predictive algorithm developed in previous work was deployed as part of a software interface that can be used for long-distance tele-operation. In this paper we provide the details of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmic approach. We show that all of the algorithms presented can be optimized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. Judicious feature selection also plays a significant role in the conclusions drawn.

Martin, Rodney A.; Wheeler, Kevin R.; SunSpiral, Vytas; Allan, Mark B.

2006-01-01

290

A local stability supported parallel distributed constraint optimization algorithm.  

PubMed

This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. PMID:25105166

Peibo, Duan; Changsheng, Zhang; Bin, Zhang

2014-01-01

291

A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm  

PubMed Central

This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. PMID:25105166

Peibo, Duan; Changsheng, Zhang; Bin, Zhang

2014-01-01

292

Intelligent Parallel Particle Swarm Optimization Algorithms  

Microsoft Academic Search

Some social systems of natural species, such as flocks of birds and schools of fish, possess interesting collective behavior.\\u000a In these systems, globally sophisticated behavior emerges from local, indirect communication amongst simple agents with only\\u000a limited capabilities. In an attempt to simulate this flocking behavior by computers, Kennedy and Eberthart (1995) realized\\u000a that an optimization problem can be formulated as

Shu-chuan Chu; Jeng-shyang Pan

2006-01-01

293

An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization  

Microsoft Academic Search

In this paper, an evolutionary algorithm with spatially distributed surrogates (EASDS) for multiobjective optimization is\\u000a presented. The algorithm performs actual analysis for the initial population and periodically every few generations. An external\\u000a archive of the unique solutions evaluated using the actual analysis is maintained to train the surrogate models. The data\\u000a points in the archive are split into multiple partitions

Amitay Isaacs; Tapabrata Ray; Warren Smith

2007-01-01

294

An optimal on-line algorithm for metrical task system  

Microsoft Academic Search

In practice, almost all dynamic systems require decisions to be made on-line, without full knowledge of their future impact on the system. A general model for the processing of sequences of tasks is introduced, and a general on-line decision algorithm is developed. It is shown that, for an important class of special cases, this algorithm is optimal among all on-line

Allan Borodin; Nathan Linial; Michael E. Saks

1992-01-01

295

Three Parallel Algorithms for Solving Nonlinear Systems and Optimization Problems  

Microsoft Academic Search

\\u000a In this work we describe three sequential algorithms and their parallel counterparts for solving nonlinear systems, when the\\u000a Jacobian matrix is symmetric and positive definite. This case appears frequently in unconstrained optimization problems. Two\\u000a of the three algorithms are based on Newton’s method. The first solves the inner iteration with Cholesky decomposition while\\u000a the second is based on the inexact

Jesús Peinado; Antonio M. Vidal

2004-01-01

296

Shape Optimization of Rubber Bushing Using Differential Evolution Algorithm  

PubMed Central

The objective of this study is to design rubber bushing at desired level of stiffness characteristics in order to achieve the ride quality of the vehicle. A differential evolution algorithm based approach is developed to optimize the rubber bushing through integrating a finite element code running in batch mode to compute the objective function values for each generation. Two case studies were given to illustrate the application of proposed approach. Optimum shape parameters of 2D bushing model were determined by shape optimization using differential evolution algorithm. PMID:25276848

2014-01-01

297

[Optimizing algorithm design of piecewise linear classifier for spectra].  

PubMed

Being able to identify pollutant gases quickly and accurately is a basic request of spectroscopic technique for envirment monitoring for spectral classifier. Piecewise linear classifier is simple needs less computational time and approachs nonlinear boundary beautifully. Combining piecewise linear classifier and linear support vector machine which is based on the principle of maximizing margin, an optimizing algorithm for single side piecewise linear classifier was devised. Experimental results indicate that the piecewise linear classifier trained by the optimizing algorithm proposed in this paper can approach nonolinear boundary with fewer super_planes and has higher veracity for classification and recognition. PMID:19271528

Lan, Tian-Ge; Fang, Yong-Hua; Xiong, Wei; Kong, Chao; Li, Da-Cheng; Dong, Da-Ming

2008-11-01

298

Fuzzy Adaptive Swarm Optimization Algorithm for Discrete Environments  

NASA Astrophysics Data System (ADS)

The heuristic methods have been widely developed for solution of complicated optimization methods. Recently hybrid methods that are based on combination of different approaches have shown more potential in this regard. Fuzzy simulation and Particle Swarm Optimization algorithm are integrated to design a hybrid intelligent algorithm to solve the np-hard problem such as travelling salesman problem in efficient and faster way of solutions. The results obtained with the proposed method show its potential in achieving both accuracy and speed in small and medium size problems, compared to many advanced methods.

Zahedi, M. Hadi; S. Haghighi, M. Mehdi

299

Computer-aided design of optimal structures with uncertainty  

Microsoft Academic Search

Uncertainties in applied loads are introduced into the theory of optimization by use of ellipsoidal convex models. Mathematical derivations for quantifying uncertainty with the convex model are presented and are incorporated into an optimization computer algorithm. The algorithm is used in two design examples—a continuous two-span reinforced concrete beam and a steel ten-bar truss—with varying levels of uncertainty. Beam and

C. P. Pantelides; Berkeley C. Booth

2000-01-01

300

Benchmarking derivative-free optimization algorithms.  

SciTech Connect

We propose data profiles as a tool for analyzing the performance of derivative-free optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems. Our results provide estimates for the performance difference between these solvers, and show that on these problems, the model-based solver tested performs better than the two direct search solvers tested.

More', J. J.; Wild, S. M.; Mathematics and Computer Science; Cornell Univ.

2009-01-01

301

An efficient cuckoo search algorithm for numerical function optimization  

NASA Astrophysics Data System (ADS)

Cuckoo search algorithm which reproduces the breeding strategy of the best known brood parasitic bird, the cuckoos has demonstrated its superiority in obtaining the global solution for numerical optimization problems. However, the involvement of fixed step approach in its exploration and exploitation behavior might slow down the search process considerably. In this regards, an improved cuckoo search algorithm with adaptive step size adjustment is introduced and its feasibility on a variety of benchmarks is validated. The obtained results show that the proposed scheme outperforms the standard cuckoo search algorithm in terms of convergence characteristic while preserving the fascinating features of the original method.

Ong, Pauline; Zainuddin, Zarita

2013-04-01

302

The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization  

E-print Network

This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top ...

Jackson, Daniel

2009-07-03

303

A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm  

NASA Astrophysics Data System (ADS)

Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.

Mohanty, Prases K.; Parhi, Dayal R.

2014-08-01

304

Control optimization, stabilization and computer algorithms for aircraft applications  

NASA Technical Reports Server (NTRS)

Research related to reliable aircraft design is summarized. Topics discussed include systems reliability optimization, failure detection algorithms, analysis of nonlinear filters, design of compensators incorporating time delays, digital compensator design, estimation for systems with echoes, low-order compensator design, descent-phase controller for 4-D navigation, infinite dimensional mathematical programming problems and optimal control problems with constraints, robust compensator design, numerical methods for the Lyapunov equations, and perturbation methods in linear filtering and control.

1975-01-01

305

Global path planning approach based on ant colony optimization algorithm  

Microsoft Academic Search

Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies,\\u000a according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell\\u000a area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the

Zhi-qiang Wen; Zi-xing Cai

2006-01-01

306

A mesh adaptive direct search algorithm for multiobjective optimization  

Microsoft Academic Search

This work studies multiobjective optimization (MOP) of nonsmooth functions subject to general constraints. We first present definitions and optimality conditions as well as some single-objective formulations of MOP, parameterized with respect to some reference point in the space of objective functions. Next, we propose a new algorithm called MultiMads (multiobjective mesh adaptive direct search) for MOP. MultiMads generates an approximation

Charles Audet; Gilles Savard; Walid Zghal

2010-01-01

307

Convex Approximations of Chance Constrained Programs  

E-print Network

Key words: stochastic programming, chance constraints, convex programming, ... Chance constrained optimization problems were introduced in Charnes et al ... Typically, the only way to estimate the probability for a chance constraint to be ...

2005-12-20

308

An algorithm for optimal water resources planning  

E-print Network

. m' Given these values, the problem is to find the maximum expected value of r and the corresponding optimal value of v, i. e. , v*. For v = x where x = s m m m' 2 2 r = ? a(xl pl+ x2 p2+ m +x 1 p +x ) p) 2 2k j=m +b(xp +xp + ? cx +d m k + x... is not present p 3 3p s = s + R, if %3 is present p 3 3p 3p I+I s +s =s pb spb bl b 2 bl b 2 ! bl vb1 b 2 I R P s3 I s 8 S pm 3 2 "4 x3 x2 V4 v3 V 2 F' ure 4. 5. Simplified functional diagram equivalent to that igure xl shown in Figure 4. 4. 3g...

Raju, Indukuri Venkata Satyanarayana

2012-06-07

309

Minimum Convex Partitions and Maximum Empty Polytopes  

E-print Network

Let S be a set of n points in d-space. A convex Steiner partition is a tiling of CH(S) with empty convex bodies. For every integer d, we show that S admits a convex Steiner partition with at most (n-1)/d tiles. This bound is the best possible for affine independent points in the plane, and it is best possible apart from constant factors in every dimension d>= 3. We also give the first constant-factor approximation algorithm for computing a minimum Steiner convex partition of an affine independent point set in the plane. Determining the maximum possible volume of a single tile in a Steiner partition is equivalent to a famous problem of Danzer and Rogers. We give a (1-epsilon)-approximation for the maximum volume of an empty convex body when S lies in the d-dimensional unit box [0,1]^d.

Dumitrescu, Adrian; Tóth, Csaba D

2011-01-01

310

Intelligent Optimization Scheduling Algorithm for Professional Sports Games  

Microsoft Academic Search

The world financial crisis has caused a great impact to human beings’ daily life. The significant evidence is that the oil price has hit, more than, 90 U.S. according to the report of ministry of economic affairs. The price reflects the difficulty not only to transportation but finance status. In this paper, an optimization algorithm concerning the scheduling issues was

Jason C. Hung; Miller K. Chien; Neil Y. Yen

2011-01-01

311

Optimizing Interleaver for Turbo Codes by Genetic Algorithms  

Microsoft Academic Search

Since the appearance in 1993, first approaching the Shannon limit, the Turbo Codes give a new direction for the channel encoding field, especially since they were adopted for multiple norms of telecommunications, such as deeper communication. To obtain an excellent performance it is necessary to design robust turbo code interleaver. We are investigating genetic algorithms as a promising optimization method

P. Kromer; V. Snasel; J. Platos; P. N. Ouddane

2007-01-01

312

GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS  

Microsoft Academic Search

This paper presents results from the first known application of the genetic algorithm (GA) technique for optimizing the performance of a laser system (chemical, solid-state, or gaseous). The effects of elitism, single point and uniform crossover, creep mutation, different random number seeds, population size, niching and the number of children per pair of parents on the performance of the GA

David L. Carroll

1996-01-01

313

GLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION  

E-print Network

GLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION CARL OLSSON Faculty Vision Abstract Computer Vision is today a wide research area including topics like robot vision, image there has been a rapid development in understanding and modeling different computer vision applications

Lunds Universitet

314

A Clustering Genetic Algorithm for Actuator Optimization in Flow Control  

Microsoft Academic Search

Active flow control can provide a leap in the perform- nace of engineering configurations. Although a number of sensor and actuator configurations have been proposed the task of identifying optimal parameters for control devices is based on engineering intuition usually gathered from un- controlled flow experiments. Here we propose a clustering genetic algorithm that adaptively identifies critical points in the

Michele Milano; Petros Koumoutsakos

2000-01-01

315

Genetic Algorithms Are NOT Function Optimizers Kenneth A. De Jong  

E-print Network

IS AGENETIC ALGORITHM? Figure 1 provides a flow diagram of a fairly generic version of a GA. If we ask what functions. The level of interest and success in this area has led to a number of improvements to GA optimization. However, the motivating context of Holland's initial GA work was the design and implementation

George Mason University

316

Automatic optimal design algorithm for the foundation of tower cranes  

Microsoft Academic Search

As buildings become taller and larger, the lifting plan safety review has become more important in construction project management. However, the cost and safety aspects of the lifting plan are contradictory to each other. Therefore, an optimization algorithm needs to be devised as a solution to this problem. In many cases at construction sites, the selection and stability review of

Sun-Kuk Kim; Jang-Young Kim; Dong-Hoon Lee; Sang-Yeon Ryu

2011-01-01

317

Field-Programmable Gate Array Architectures and Algorithms Optimized  

E-print Network

Circuits Andy Gean Ye November 2004 #12;Field-Programmable Gate Array Architectures and Algorithms Optimized for Implementing Datapath Circuits by Andy Gean Ye A thesis submitted in conformity of Electrical and Computer Engineering University of Toronto Toronto, Ontario, Canada © Copyright by Andy Gean

Ye, Andy G.

318

A tutorial on optimization techniques applied to DSM algorithms  

NASA Astrophysics Data System (ADS)

xDSL systems are widely used nowadays. Services such as VDSL2 can achieve high bitrates over copper wires. The usage of dynamic spectrum management techniques (DSM) can result in even better bitrates, through mitigation of crosstalk, the worst interference in such systems. This tutorial surveys the recent progress in DSM, covering the main algorithms and optimization concepts used by then.

Neves, Darlene Maciel; Klautau, Aldebaro Barreto da, Jr.; Conte, Marcio Murilo; Medeiros, Eduardo Lins de; Reis, Jacklyn Dias; Dortschy, Boris

2007-09-01

319

Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms  

E-print Network

of the characteristics needed to adopt complex goal-oriented behaviors. Bubbles can indeed shape the attractor landscape: they either lead to the saturation of the field, the lack of any coherent activity, or the selfExploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms Jean

Boyer, Edmond

320

The Cache Performance and Optimizations of Blocked Algorithms  

Microsoft Academic Search

Blocking is a well-known optimization technique for improving the effectiveness of memory hierarchies. Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks, so that data loaded into the faster levels of the memory hierarchy are reused. This paper presents cache performance data for blocked programs and evaluates several op- timizations to

Monica S. Lam; Edward E. Rothberg; Michael E. Wolf

1991-01-01

321

Application of genetic algorithms in resource constrained network optimization  

Microsoft Academic Search

There are limited solution techniques available for resource constrained project scheduling problems with stochastic task durations. Due to computational complexity, scheduling heuristics have been found useful for large deterministic problems. In this paper, the authors demonstrate the use of a genetic algorithm to optimize over a linear combination of scheduling heuristics. A simulation model is used to evaluate the performance

J. Pet-Edwards; M. Mollaghasemi

1995-01-01

322

Evolution-based decision tree optimization using cultural algorithms  

Microsoft Academic Search

Recently decision trees have been used in data mining application to extract new concepts. While current decision tree algorithms exhibit many improvements over earlier versions, there are still problems with the generation of optimal trees in situations that use attributes that vary widely in their possible outcomes. Quinlan's gain-ratio measure has been needed to reduce the bias towards variables with

Hasan A Al-Shehri

1997-01-01

323

Harmonic optimization of multilevel converters using genetic algorithms  

Microsoft Academic Search

In this paper, a genetic algorithm (GA) optimization technique is applied to multilevel inverter to determine optimum switching angles for cascaded multilevel inverters for eliminating some higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any number of levels; as an example in this paper, a 7-level inverter is considered, and

B. Ozpineci; L. M. Tolbert; J. N. Chiasson

2004-01-01

324

EXPERIMENTAL ANALYSIS OF LOCAL SEARCH ALGORITHMS FOR OPTIMAL BASE STATION  

E-print Network

Dominating Set (MDS) problem [1]. Local search techniques 1 , such as Genetic Algo­ 1 There is some confusion unit distance apart. From this set of 100,000 possible points, 51 locations where base transmittingEXPERIMENTAL ANALYSIS OF LOCAL SEARCH ALGORITHMS FOR OPTIMAL BASE STATION LOCATION Bhaskar

Krishnamachari, Bhaskar

325

Matlab coding standard for Stochastic optimization algorithms, FFR105  

E-print Network

Matlab coding standard for Stochastic optimization algorithms, FFR105 v 1.1, 2009-02-02, v 1 clear and highly readable code you reduce the risk of introducing unwanted errors. It is the aim, you should use Matlab, and you should follow the code standard described be- low. Programs

Wolff, Krister

326

Wind Turbine Tower Optimization Method Using a Genetic Algorithm  

Microsoft Academic Search

A wind turbine tower optimization program was developed, using a genetic algorithm. This allowed a rational analysis to reduce the mass of turbine tower, by considering, for example, the distributions of diameter and wall thickness, and the positions of flanges and access ports to navigation lights. Both extreme and fatigue loads were calculated, based on wind turbine design requirements and

Shigeo Yoshida

2006-01-01

327

E cient Approximation and Optimization Algorithms for Computational Metrology  

E-print Network

E cient Approximation and Optimization Algorithms for Computational Metrology Christian A. Duncan in computational metrology, focusing on the fun- damental issues of \\ atness" and \\roundness." Speci c- ally, we-dimensional point set, which corresponds to the metrology notion of \\ atness," giv- ing an approximation method

Goodrich, Michael T.

328

A new filled function algorithm for constrained global optimization problems  

Microsoft Academic Search

A new filled function with one parameter is proposed for solving constrained global optimization problems without the coercive condition, in which the filled function contains neither exponential term nor fractional term and is easy to be calculated. A corresponding filled function algorithm is established based on analysis of the properties of the filled function. At last, we perform numerical experiments

Suxiang He; Weilai Chen; Hui Wang

2011-01-01

329

An optimal algorithm for scheduling checkpoints with variable costs  

E-print Network

An optimal algorithm for scheduling checkpoints with variable costs Mohamed-Slim Bouguerra, Denis are then used to restart computations from the last checkpoint. This last approach called checkpointing is one of the most popular fault tolerance technique in parallel systems. 1.2 Brief review of related works Young

Boyer, Edmond

330

Fast Optimal Load Balancing Algorithms for 1D Partitioning  

SciTech Connect

One-dimensional decomposition of nonuniform workload arrays for optimal load balancing is investigated. The problem has been studied in the literature as ''chains-on-chains partitioning'' problem. Despite extensive research efforts, heuristics are still used in parallel computing community with the ''hope'' of good decompositions and the ''myth'' of exact algorithms being hard to implement and not runtime efficient. The main objective of this paper is to show that using exact algorithms instead of heuristics yields significant load balance improvements with negligible increase in preprocessing time. We provide detailed pseudocodes of our algorithms so that our results can be easily reproduced. We start with a review of literature on chains-on-chains partitioning problem. We propose improvements on these algorithms as well as efficient implementation tips. We also introduce novel algorithms, which are asymptotically and runtime efficient. We experimented with data sets from two different applications: Sparse matrix computations and Direct volume rendering. Experiments showed that the proposed algorithms are 100 times faster than a single sparse-matrix vector multiplication for 64-way decompositions on average. Experiments also verify that load balance can be significantly improved by using exact algorithms instead of heuristics. These two findings show that exact algorithms with efficient implementations discussed in this paper can effectively replace heuristics.

Pinar, Ali; Aykanat, Cevdet

2002-12-09

331

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm  

NASA Astrophysics Data System (ADS)

A propeller performance analysis program has been developed and integrated into a Genetic Algorithm for design optimization. The design tool will produce optimal propeller geometries for a given goal, which includes performance and/or acoustic signature. A vortex lattice model is used for the propeller performance analysis and a subsonic compact source model is used for the acoustic signature determination. Compressibility effects are taken into account with the implementation of Prandtl-Glauert domain stretching. Viscous effects are considered with a simple Reynolds number based model to account for the effects of viscosity in the spanwise direction. An empirical flow separation model developed from experimental lift and drag coefficient data of a NACA 0012 airfoil is included. The propeller geometry is generated using a recently introduced Class/Shape function methodology to allow for efficient use of a wide design space. Optimizing the angle of attack, the chord, the sweep and the local airfoil sections, produced blades with favorable tradeoffs between single and multiple point optimizations of propeller performance and acoustic noise signatures. Optimizations using a binary encoded IMPROVE(c) Genetic Algorithm (GA) and a real encoded GA were obtained after optimization runs with some premature convergence. The newly developed real encoded GA was used to obtain the majority of the results which produced generally better convergence characteristics when compared to the binary encoded GA. The optimization trade-offs show that single point optimized propellers have favorable performance, but circulation distributions were less smooth when compared to dual point or multiobjective optimizations. Some of the single point optimizations generated propellers with proplets which show a loading shift to the blade tip region. When noise is included into the objective functions some propellers indicate a circulation shift to the inboard sections of the propeller as well as a reduction in propeller diameter. In addition the propeller number was increased in some optimizations to reduce the acoustic blade signature.

Burger, Christoph

332

Genetic algorithms for optimal design of underground reinforced concrete tube structure  

Microsoft Academic Search

Applying genetic algorithms to optimal design of underground reinforced concrete tube structure, the author develops the optimal model of structural design based on genetic algorithms for underground reinforced concrete tube. An example of the reinforced concrete tube structure is calculated by the proposed computer programs based on genetic algorithms optimal model, and the result indicates that using genetic algorithms for

Sheng-Li Zhao; Min-Qiang Li; Ji-Song Kou; Yan Liu

2004-01-01

333

Research reactor loading pattern optimization using estimation of distribution algorithms  

SciTech Connect

A new evolutionary search based approach for solving the nuclear reactor loading pattern optimization problems is presented based on the Estimation of Distribution Algorithms. The optimization technique developed is then applied to the maximization of the effective multiplication factor (K{sub eff}) of the Imperial College CONSORT research reactor (the last remaining civilian research reactor in the United Kingdom). A new elitism-guided searching strategy has been developed and applied to improve the local convergence together with some problem-dependent information based on the 'stand-alone K{sub eff} with fuel coupling calculations. A comparison study between the EDAs and a Genetic Algorithm with Heuristic Tie Breaking Crossover operator has shown that the new algorithm is efficient and robust. (authors)

Jiang, S. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); Ziver, K. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); AMCG Group, RM Consultants, Abingdon (United Kingdom); Carter, J. N.; Pain, C. C.; Eaton, M. D.; Goddard, A. J. H. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); Franklin, S. J.; Phillips, H. J. [Imperial College, Reactor Centre, Silwood Park, Buckhurst Road, Ascot, Berkshire, SL5 7TE (United Kingdom)

2006-07-01

334

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm  

Microsoft Academic Search

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize.\\u000a An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive\\u000a is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based

Dervis Karaboga; Bahriye Basturk

2007-01-01

335

Global structual optimizations of surface systems with a genetic algorithm  

SciTech Connect

Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al{sub n} (n up to 23) were performed using a genetic algorithm coupled with a tight-binding potential. Second, a genetic algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of {radical}3 x {radical}3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.

Chuang, Feng-Chuan

2005-05-01

336

Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm  

NASA Technical Reports Server (NTRS)

A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.

Oyama, Akira; Liou, Meng-Sing

2001-01-01

337

Cores of convex games  

Microsoft Academic Search

The core of ann-person game is the set of feasible outcomes that cannot be improved upon by any coalition of players. A convex game is defined as one that is based on a convex set function. In this paper it is shown that the core of a convex game is not empty and that it has an especially regular structure.

Lloyd S. Shapley

1971-01-01

338

Quantum convex support  

Microsoft Academic Search

Convex support, the mean values of a set of random variables, is central in information theory and statistics. Equally central in quantum information theory are mean values of a set of observables in a finite-dimensional C?-algebra A, which we call (quantum) convex support. The convex support can be viewed as a projection of the state space of A and it

Stephan Weis

2011-01-01

339

Using genetic algorithms to search for an optimal investment strategy  

NASA Astrophysics Data System (ADS)

In this experiment we used genetic algorithms to search for an investment strategy by dividing capital among different stocks with varying returns. The algorithm involves having a ``manager'' who divides his capital among various ``experts'' each of whom has a simple investment strategy. The expert strategies act like genes, experiencing mutation and crossover, in a selection process using previous returns as the fitness function. When algorithm was run with test data where the optimal strategy favored non-uniform investment in one stock it consistently beat a simple buy hold. However when the algorithm was run on actual stock data the system overwhelmingly stabilized at a population that closely resembled a simple buy hold portfolio, that is, evenly distribute the capital among all stocks.

Mandere, Edward; Xi, Haowen

2007-10-01

340

Message-passing algorithms for compressed sensing  

PubMed Central

Compressed sensing aims to undersample certain high-dimensional signals yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be recovered is sufficiently sparse in a known basis. Currently, the best known sparsity–undersampling tradeoff is achieved when reconstructing by convex optimization, which is expensive in important large-scale applications. Fast iterative thresholding algorithms have been intensively studied as alternatives to convex optimization for large-scale problems. Unfortunately known fast algorithms offer substantially worse sparsity–undersampling tradeoffs than convex optimization. We introduce a simple costless modification to iterative thresholding making the sparsity–undersampling tradeoff of the new algorithms equivalent to that of the corresponding convex optimization procedures. The new iterative-thresholding algorithms are inspired by belief propagation in graphical models. Our empirical measurements of the sparsity–undersampling tradeoff for the new algorithms agree with theoretical calculations. We show that a state evolution formalism correctly derives the true sparsity–undersampling tradeoff. There is a surprising agreement between earlier calculations based on random convex polytopes and this apparently very different theoretical formalism. PMID:19858495

Donoho, David L.; Maleki, Arian; Montanari, Andrea

2009-01-01

341

Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm  

NASA Astrophysics Data System (ADS)

Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector.

?encan ?ahin, Arzu

2012-11-01

342

Optimization of image processing algorithms on mobile platforms  

NASA Astrophysics Data System (ADS)

This work presents a technique to optimize popular image processing algorithms on mobile platforms such as cell phones, net-books and personal digital assistants (PDAs). The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has a mandate to optimize them so as to meet real-time deadlines. A methodology to take advantage of the asymmetric dual-core processor, which includes an ARM and a DSP core supported by shared memory, is presented with implementation details. The target platform chosen is the popular OMAP 3530 processor for embedded media systems. It has an asymmetric dual-core architecture with an ARM Cortex-A8 and a TMS320C64x Digital Signal Processor (DSP). The development platform was the BeagleBoard with 256 MB of NAND RAM and 256 MB SDRAM memory. The basic image correlation algorithm is chosen for benchmarking as it finds widespread application for various template matching tasks such as face-recognition. The basic algorithm prototypes conform to OpenCV, a popular computer vision library. OpenCV algorithms can be easily ported to the ARM core which runs a popular operating system such as Linux or Windows CE. However, the DSP is architecturally more efficient at handling DFT algorithms. The algorithms are tested on a variety of images and performance results are presented measuring the speedup obtained due to dual-core implementation. A major advantage of this approach is that it allows the ARM processor to perform important real-time tasks, while the DSP addresses performance-hungry algorithms.

Poudel, Pramod; Shirvaikar, Mukul

2011-03-01

343

Harmonic Optimization of Multilevel Converters Using Genetic Algorithms Abstract--In this paper, a genetic algorithm (GA) optimization  

E-print Network

, a genetic algorithm (GA) optimization technique is applied to multilevel inverter to determine optimum switching angles for cascaded multilevel inverters for eliminating some higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any

Tolbert, Leon M.

344

An optimal quantum algorithm for the oracle identification problem  

E-print Network

In the oracle identification problem, we are given oracle access to an unknown N-bit string x promised to belong to a known set C of size M and our task is to identify x. We present a quantum algorithm for the problem that is optimal in its dependence on N and M. Our algorithm considerably simplifies and improves the previous best algorithm due to Ambainis et al. Our algorithm also has applications in quantum learning theory, where it improves the complexity of exact learning with membership queries, resolving a conjecture of Hunziker et al. The algorithm is based on ideas from classical learning theory and a new composition theorem for solutions of the filtered $\\gamma_2$-norm semidefinite program, which characterizes quantum query complexity. Our composition theorem is quite general and allows us to compose quantum algorithms with input-dependent query complexities without incurring a logarithmic overhead for error reduction. As an application of the composition theorem, we remove all log factors from the best known quantum algorithm for Boolean matrix multiplication.

Robin Kothari

2013-11-29

345

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm  

NASA Technical Reports Server (NTRS)

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

2005-01-01

346

Dynamic learning rate optimization of the backpropagation algorithm.  

PubMed

It has been observed by many authors that the backpropagation (BP) error surfaces usually consist of a large amount of flat regions as well as extremely steep regions. As such, the BP algorithm with a fixed learning rate will have low efficiency. This paper considers dynamic learning rate optimization of the BP algorithm using derivative information. An efficient method of deriving the first and second derivatives of the objective function with respect to the learning rate is explored, which does not involve explicit calculation of second-order derivatives in weight space, but rather uses the information gathered from the forward and backward propagation, Several learning rate optimization approaches are subsequently established based on linear expansion of the actual outputs and line searches with acceptable descent value and Newton-like methods, respectively. Simultaneous determination of the optimal learning rate and momentum is also introduced by showing the equivalence between the momentum version BP and the conjugate gradient method. Since these approaches are constructed by simple manipulations of the obtained derivatives, the computational and storage burden scale with the network size exactly like the standard BP algorithm, and the convergence of the BP algorithm is accelerated with in a remarkable reduction (typically by factor 10 to 50, depending upon network architectures and applications) in the running time for the overall learning process. Numerous computer simulation results are provided to support the present approaches. PMID:18263352

Yu, X H; Chen, G A; Cheng, S X

1995-01-01

347

Leveraging off genetic algorithms for optimizing AGRIN lenses  

NASA Astrophysics Data System (ADS)

While researching various gradient index glass families for superb color correction using ZEMAX1 optical design program, the authors found that certain solutions could only be found using the Hammer routine2. Hammer is a genetic algorithm that breeds a particular lens configuration with variations of itself3. It is not intended to be a global search routine. Hammer is typically used after the best performance is obtained using the standard damped least squares (DLS) algorithm with the default merit function (MF) based on minimizing root mean square (RMS) spot size. Upon this discovery, the authors proceeded to explore the benefit of using the genetic Hammer algorithm on three different lens systems. To make the solution space more complicated, two axial gradient index (AGRIN) elements were used in each lens type; a bi- AGRIN cemented doublet; a bi-AGRIN air spaced triplet with CaF2 as the center element, and a double Gauss with four AGRIN elements and two CaF2 elements. AGRIN elements were used in each lens to provide a more complex solution space and to make optimization more difficult. After optimization, the performance of each lens was compared wiht the conventionally optimized counterpart using the default MF with a DLS algorithm. After this comparison was made, another trade study was done between the Hammer and DLS algorithms, but in this case, the optimization used a custom MF instead of the default MF. The authors believe this study shows the importance of MF construction over that of using the default RMS spot size metric. A significant improvement was obtained for all lenses with the default MF using the Hammer over the DLS technique, but that improvement was less obvious when a custom MF was used.

Manhart, Paul K.; Sparrold, Scott W.

2000-10-01

348

Facial skin segmentation using bacterial foraging optimization algorithm.  

PubMed

Nowadays, analyzing human facial image has gained an ever-increasing importance due to its various applications. Image segmentation is required as a very important and fundamental operation for significant analysis and interpretation of images. Among the segmentation methods, image thresholding technique is one of the most well-known methods due to its simplicity, robustness, and high precision. Thresholding based on optimization of the objective function is among the best methods. Numerous methods exist for the optimization process and bacterial foraging optimization (BFO) is among the most efficient and novel ones. Using this method, optimal threshold is extracted and then segmentation of facial skin is performed. In the proposed method, first, the color facial image is converted from RGB color space to Improved Hue-Luminance-Saturation (IHLS) color space, because IHLS has a great mapping of the skin color. To perform thresholding, the entropy-based method is applied. In order to find the optimum threshold, BFO is used. In order to analyze the proposed algorithm, color images of the database of Sahand University of Technology of Tabriz, Iran were used. Then, using Otsu and Kapur methods, thresholding was performed. In order to have a better understanding from the proposed algorithm; genetic algorithm (GA) is also used for finding the optimum threshold. The proposed method shows the better results than other thresholding methods. These results include misclassification error accuracy (88%), non-uniformity accuracy (89%), and the accuracy of region's area error (89%). PMID:23724370

Bakhshali, Mohamad Amin; Shamsi, Mousa

2012-10-01

349

Hierarchical artificial bee colony algorithm for RFID network planning optimization.  

PubMed

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

350

Optimization of an Antenna Array Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

An array of antennas is usually used in long distance communication. The observation of celestial objects necessitates a large array of antennas, such as the Giant Metrewave Radio Telescope (GMRT). Optimizing this kind of array is very important when observing a high performance system. The genetic algorithm (GA) is an optimization solution for these kinds of problems that reconfigures the position of antennas to increase the u-v coverage plane or decrease the sidelobe levels (SLLs). This paper presents how to optimize a correlator antenna array using the GA. A brief explanation about the GA and operators used in this paper (mutation and crossover) is provided. Then, the results of optimization are discussed. The results show that the GA provides efficient and optimum solutions among a pool of candidate solutions in order to achieve the desired array performance for the purposes of radio astronomy. The proposed algorithm is able to distribute the u-v plane more efficiently than GMRT with a more than 95% distribution ratio at snapshot, and to fill the u-v plane from a 20% to more than 68% filling ratio as the number of generations increases in the hour tracking observations. Finally, the algorithm is able to reduce the SLL to -21.75 dB.

Kiehbadroudinezhad, Shahideh; Kamariah Noordin, Nor; Sali, A.; Zainal Abidin, Zamri

2014-06-01

351

Fuel management optimization using genetic algorithms and code independence  

SciTech Connect

Fuel management optimization is a hard problem for traditional optimization techniques. Loading pattern optimization is a large combinatorial problem without analytical derivative information. Therefore, methods designed for continuous functions, such as linear programming, do not always work well. Genetic algorithms (GAs) address these problems and, therefore, appear ideal for fuel management optimization. They do not require derivative information and work well with combinatorial. functions. The GAs are a stochastic method based on concepts from biological genetics. They take a group of candidate solutions, called the population, and use selection, crossover, and mutation operators to create the next generation of better solutions. The selection operator is a {open_quotes}survival-of-the-fittest{close_quotes} operation and chooses the solutions for the next generation. The crossover operator is analogous to biological mating, where children inherit a mixture of traits from their parents, and the mutation operator makes small random changes to the solutions.

DeChaine, M.D.; Feltus, M.A.

1994-12-31

352

Chemical Genetic Algorithms - Evolutionary Optimization of Binary-to-Real-Value Translation in Genetic Algorithms  

Microsoft Academic Search

A chemical genetic algorithm (CGA) in which several types of molecules (information units) react with each other in a cell is proposed. Not only the information in DNA, but also smaller molecules responsible for the transcription and translation of DNA into amino acids, are adaptively changed during evolution, which optimizes the fundamental mapping from binary substrings in DNA (genotype) to

Hideaki Suzuki; Hidefumi Sawai; Wojciech Piaseczny

2006-01-01

353

Genetic Algorithms: A Fundamental Component of an Optimization Toolkit for Improved Engineering Designs  

Microsoft Academic Search

Optimization is being increasing applied to engineering de- sign problems throughout the world. iSIGHT is a generic engineering design environment that provides engineers with an optimization toolkit of leading optimization algorithms and an optimization advisor to solve their optimization needs. This paper focuses on the key role played by the toolkit's genetic algorithm in providing a robust, general purpose solution

Siu Shing Tong; David J. Powell

2003-01-01

354

Stochastic Learning Algorithms for Adaptive Modulation  

Microsoft Academic Search

In this paper we present re-enforcement learning algorithms for adaptive modulation in flat fading channels for reconfigurable, agile wireless communications devices. We derive the dynamical stochastic control model, convexity properties of the stated optimization problem, learning based feedback control optimization and numerical simulations of the designed system. We show how this technique can be applied independently of channel model, error

Anup Misra; Vikram Krishnamurthy; Robert Schober

2006-01-01

355

Space-mapping optimization of microwave circuits exploiting surrogate models  

Microsoft Academic Search

A powerful new space-mapping (SM) optimization algorithm is presented in this paper. It draws upon recent developments in both surrogate model-based optimization and modeling of microwave devices, SM optimization is formulated as a general optimization problem of a surrogate model. This model is a convex combination of a mapped coarse model and a linearized fine model. It exploits, in a

Mohamed H. Bakr; John W. Bandler; Kaj Madsen; José Ernesto Rayas-Sánchez; J. Sondergaard

2000-01-01

356

A Multi-Objective Ant Colony Optimization Algorithm for Infrastructure Routing  

E-print Network

An algorithm is presented that is capable of producing Pareto-optimal solutions for multi-objective infrastructure routing problems: the Multi-Objective Ant Colony Optimization (MOACO). This algorithm offers a constructive search technique...

McDonald, Walter

2012-07-16

357

520 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 8, NO. 3, JULY 2011 A Convex Optimization Framework for Almost  

E-print Network

Optimization Framework for Almost Budget Balanced Allocation of a Divisible Good Anil Kumar Chorppath efficient, strategy proof, nearly budget balanced mechanisms within the Groves class. Near budget balance is attained by returning as much of the received payments as rebates to agents. Two performance criteria

Sundaresan, Rajesh

358

Parallel Algorithms for Graph Optimization using Tree Decompositions  

SciTech Connect

Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.

Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL; Groer, Christopher S [ORNL

2012-06-01

359

A sensing duration optimization algorithm in cognitive radio  

NASA Astrophysics Data System (ADS)

In a periodic spectrum sensing framework where each frame consists of a sensing duration and a data transmitting duration, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization algorithm is proposed, which can dynamically optimize the sensing duration of each frame. Analysis and simulation results reveal that the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.

Liu, Yuexuan; Liang, Shujian; Zhang, Xiao

2013-03-01

360

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm  

E-print Network

Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.

O. T. Kosmas; D. S. Vlachos

2008-11-13

361

Genetic Algorithm Application in Optimization of Wireless Sensor Networks  

PubMed Central

There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235

Norouzi, Ali; Zaim, A. Halim

2014-01-01

362

A hierarchical evolutionary algorithm for multiobjective optimization in IMRT  

PubMed Central

Purpose: The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front. Results: The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12–15 plans, any random plan selected from a MOEA population had a 11.3%±0.7% chance of dominating any random plan selected by a standard genetic package with 0.04%±0.02% chance of domination in reverse. By implementing domination advantage and protocol objectives, small and diverse populations of clinically acceptable plans that approximated the Pareto front could be generated in a fraction of 1 h. Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for multiobjective optimizations. Conclusions: The MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. The final goal is to improve practical aspects of the algorithm and integrate it with a decision analysis tool or human interface for selection of the IMRT plan with the best possible balance of successful treatment of the target with low OAR dose and low risk of complication for any specific patient situation. PMID:20964218

Holdsworth, Clay; Kim, Minsun; Liao, Jay; Phillips, Mark H.

2010-01-01

363

Optimizing phase-estimation algorithms for diamond spin magnetometry  

NASA Astrophysics Data System (ADS)

We present a detailed theoretical and numerical study discussing the application and optimization of phase-estimation algorithms (PEAs) to diamond spin magnetometry. We compare standard Ramsey magnetometry, the nonadaptive PEA (NAPEA), and quantum PEA (QPEA) incorporating error checking. Our results show that the NAPEA requires lower measurement fidelity, has better dynamic range, and greater consistency in sensitivity. We elucidate the importance of dynamic range to Ramsey magnetic imaging with diamond spins, and introduce the application of PEAs to time-dependent magnetometry.

Nusran, N. M.; Dutt, M. V. Gurudev

2014-07-01

364

Mixed Models for the Analysis of Optimization Algorithms  

NASA Astrophysics Data System (ADS)

We review linear statistical models for the analysis of computational experiments on optimization algorithms. The models offer the mathematical framework to separate the effects of algorithmic components and instance features included in the analysis. We regard test instances as drawn from a population and we focus our interest not on those single instances but on the whole population. Hence, instances are treated as a random factor. Overall these experimental designs lead to mixed effects linear models. We present both the theory to justify these models and a computational example in which we analyze and comment on several possible experimental designs. The example is a component-wise analysis of local search algorithms for the 2-edge-connectivity augmentation problem. We use standard statistical software to perform the analysis and report the R commands. Data sets and the analysis in SAS are available in an online compendium.

Chiarandini, Marco; Goegebeur, Yuri

365

Optimizing voting-type algorithms for replicated data  

SciTech Connect

The main objectives of data replication are improved availability and reduced communications cost for queries. Maintaining the various copies consistent, however, increases the communications cost incurred by updates. For a given degree of replication, the choice of a specific concurrency control algorithm can have a significant impact on the total communications cost. In this paper we present various models for analyzing and understanding the trade-offs between the potentially opposing objectives of maximum resiliency and minimum communications cost in the context of the quorum consensus class of algorithms. It is argued that an optional vote assignment is one which meets given resiliency goals and yet incurs the least communications cost compared with all other alternative assignments. A mathematical model for vote assignment is developed, and optimal algorithms are presented. It is demonstrated that significant cost savings can be realized from these approaches. 14 refs., 1 fig., 2 tabs.

Kumar, A.; Segev, A.

1988-03-01

366

Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework  

SciTech Connect

Automation of DC photoinjector designs using a genetic algorithm (GA) based optimization is an accepted practice in accelerator physics. Allowing the gun cavity field profile shape to be varied can extend the utility of this optimization methodology to superconducting and normal conducting radio frequency (SRF/RF) gun based injectors. Finding optimal field and cavity geometry configurations can provide guidance for cavity design choices and verify existing designs. We have considered two approaches for varying the electric field profile. The first is to determine the optimal field profile shape that should be used independent of the cavity geometry, and the other is to vary the geometry of the gun cavity structure to produce an optimal field profile. The first method can provide a theoretical optimal and can illuminate where possible gains can be made in field shaping. The second method can produce more realistically achievable designs that can be compared to existing designs. In this paper, we discuss the design and implementation for these two methods for generating field profiles for SRF/RF guns in a GA based injector optimization scheme and provide preliminary results.

Alicia Hofler, Pavel Evtushenko, Frank Marhauser

2009-09-01

367

Optimizing models based OPC fragmentation using genetic algorithms  

NASA Astrophysics Data System (ADS)

Models Based Optical Proximity Correction (MBOPC) is used extensively in the semiconductor industry to achieve robust pattern fidelity in modern lithographic processes. Much of the complexity in OPC algorithms is handled by advanced commercial software packages. These packages give users the ability to set many parameters in the OPC code decks which are used to customize the recipes for specific design styles and manufacturing process settings. Some of the most important parameters in traditional OPC recipes are the fragmentation rules, which determine how edges of polygons are fragmented in a traditional edge-based correction algorithm. It is important to find settings which can deliver good results on a wide variety of complex layout styles. One approach to setting these parameters is through a Design of Experiments (DOE) approach where many different settings are tested in a systematic fashion, in an attempt to find appropriate fragmentation rules for a wide variety of layouts. This is a very straight-forward and powerful technique, but it can be very computationally expensive, particularly as the number of independent variables becomes large. In this paper we examine the usefulness of Genetic Algorithm (GA) optimization techniques for setting the fragmentation parameters. Our work is focused on using GAs to tune parameters rather than on core algorithms used in mask data correction. We use challenging metal layout patterns and optimize fragmentation rules to try to minimize residual edge placement errors, while trying to generate fragmentation that does not result in excessive runtime, or mask manufacturing challenges.

Dipaola, Domenico A.; Stobert, Ian

2008-10-01

368

Penalty-Optimal Brain Surgeon Process and Its Optimize Algorithm Based on Conjugate Gradient  

Microsoft Academic Search

\\u000a In view of the high complexity of pruning algorithm for OBS (optimal brain surgery) process and the deficiency of its match\\u000a usage with training algorithm, this paper presents a penalty OBS computational model, in which the pruning condition is considered\\u000a as a penalty term integrated in the objective function of NN (neural network). Based on its theoretical convergence, this\\u000a model

Cuijuan Wu; Dong Li; Tian Song

2010-01-01

369

Constrained Multi-Level Algorithm for Trajectory Optimization  

NASA Astrophysics Data System (ADS)

The emphasis on low cost access to space inspired many recent developments in the methodology of trajectory optimization. Ref.1 uses a spectral patching method for optimization, where global orthogonal polynomials are used to describe the dynamical constraints. A two-tier approach of optimization is used in Ref.2 for a missile mid-course trajectory optimization. A hybrid analytical/numerical approach is described in Ref.3, where an initial analytical vacuum solution is taken and gradually atmospheric effects are introduced. Ref.4 emphasizes the fact that the nonlinear constraints which occur in the initial and middle portions of the trajectory behave very nonlinearly with respect the variables making the optimization very difficult to solve in the direct and indirect shooting methods. The problem is further made complex when different phases of the trajectory have different objectives of optimization and also have different path constraints. Such problems can be effectively addressed by multi-level optimization. In the multi-level methods reported so far, optimization is first done in identified sub-level problems, where some coordination variables are kept fixed for global iteration. After all the sub optimizations are completed, higher-level optimization iteration with all the coordination and main variables is done. This is followed by further sub system optimizations with new coordination variables. This process is continued until convergence. In this paper we use a multi-level constrained optimization algorithm which avoids the repeated local sub system optimizations and which also removes the problem of non-linear sensitivity inherent in the single step approaches. Fall-zone constraints, structural load constraints and thermal constraints are considered. In this algorithm, there is only a single multi-level sequence of state and multiplier updates in a framework of an augmented Lagrangian. Han Tapia multiplier updates are used in view of their special role in diagonalised methods, being the only single update with quadratic convergence. For a single level, the diagonalised multiplier method (DMM) is described in Ref.5. The main advantage of the two-level analogue of the DMM approach is that it avoids the inner loop optimizations required in the other methods. The scheme also introduces a gradient change measure to reduce the computational time needed to calculate the gradients. It is demonstrated that the new multi-level scheme leads to a robust procedure to handle the sensitivity of the constraints, and the multiple objectives of different trajectory phases. Ref. 1. Fahroo, F and Ross, M., " A Spectral Patching Method for Direct Trajectory Optimization" The Journal of the Astronautical Sciences, Vol.48, 2000, pp.269-286 Ref. 2. Phililps, C.A. and Drake, J.C., "Trajectory Optimization for a Missile using a Multitier Approach" Journal of Spacecraft and Rockets, Vol.37, 2000, pp.663-669 Ref. 3. Gath, P.F., and Calise, A.J., " Optimization of Launch Vehicle Ascent Trajectories with Path Constraints and Coast Arcs", Journal of Guidance, Control, and Dynamics, Vol. 24, 2001, pp.296-304 Ref. 4. Betts, J.T., " Survey of Numerical Methods for Trajectory Optimization", Journal of Guidance, Control, and Dynamics, Vol.21, 1998, pp. 193-207 Ref. 5. Adimurthy, V., " Launch Vehicle Trajectory Optimization", Acta Astronautica, Vol.15, 1987, pp.845-850.

Adimurthy, V.; Tandon, S. R.; Jessy, Antony; Kumar, C. Ravi

370

Application of vector optimization employing modified genetic algorithm to permanent magnet motor design  

Microsoft Academic Search

This paper presents a method to solve the vector optimization problem that determines both the noninferior solution set and the best compromise solution employing a modified genetic algorithm. The algorithm differs from the conventional one in the definition of fitness value and convergence criterion. Some parameters of the algorithm are adjusted to the vector optimization. The algorithm also contains the

Dong-Joon Sim; Hyun-Kyo Jung; Song-Yop Hahn; Jong-Soo Won

1997-01-01

371

A Honey-bee Mating Optimization Algorithm for Educational Timetabling Problems  

E-print Network

1 A Honey-bee Mating Optimization Algorithm for Educational Timetabling Problems Nasser R. Sabar1 of the Honey-bee Mating Optimization Algorithm for solv- ing educational timetabling problems. The honey-bee algorithm is a nature inspired algorithm which sim- ulates the process of real honey-bees mating

Qu, Rong

372

[Research on and application of hybrid optimization algorithm in Brillouin scattering spectrum parameter extraction problem].  

PubMed

This paper presents a novel algorithm which blends optimize particle swarm optimization (PSO) algorithm and Levenberg-Marquardt (LM) algorithm according to the probability. This novel algorithm can be used for Pseudo-Voigt type of Brillouin scattering spectrum to improve the degree of fitting and precision of shift extraction. This algorithm uses PSO algorithm as the main frame. First, PSO algorithm is used in global search, after a certain number of optimization every time there generates a random probability rand (0, 1). If rand (0, 1) is less than or equal to the predetermined probability P, the optimal solution obtained by PSO algorithm will be used as the initial value of LM algorithm. Then LM algorithm is used in local depth search and the solution of LM algorithm is used to replace the previous PSO algorithm for optimal solutions. Again the PSO algorithm is used for global search. If rand (0, 1) was greater than P, PSO algorithm is still used in search, waiting the next optimization to generate random probability rand (0, 1) to judge. Two kinds of algorithms are alternatively used to obtain ideal global optimal solution. Simulation analysis and experimental results show that the new algorithm overcomes the shortcomings of single algorithm and improves the degree of fitting and precision of frequency shift extraction in Brillouin scattering spectrum, and fully prove that the new method is practical and feasible. PMID:22715752

Zhang, Yan-jun; Zhang, Shu-guo; Fu, Guang-wei; Li, Da; Liu, Yin; Bi, Wei-hong

2012-04-01

373

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm  

PubMed Central

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

Svecko, Rajko

2014-01-01

374

Genetic Algorithm Based Optimization of Clustering in Ad Hoc Networks  

E-print Network

In this paper, we have to concentrate on implementation of Weighted Clustering Algorithm with the help of Genetic Algorithm (GA).Here we have developed new algorithm for the implementation of GA-based approach with the help of Weighted Clustering Algorithm (WCA) (4). ClusterHead chosen is a important thing for clustering in adhoc networks. So, we have shown the optimization technique for the minimization of ClusterHeads(CH) based on some parameter such as degree difference, Battery power (Pv), degree of mobility, and sum of the distances of a node in adhoc networks. ClusterHeads selection of adhoc networks is an important thing for clustering. Here, we have discussed the performance comparison between deterministic approach and GA based approach. In this performance comparison, we have seen that GA does not always give the good result compare to deterministic WCA algorithm. Here we have seen connectivity (connectivity can be measured by the probability that a node is reachable to any other node.) is better th...

Nandi, Bhaskar; Paul, Soumen

2010-01-01

375

Microwave-based medical diagnosis using particle swarm optimization algorithm  

NASA Astrophysics Data System (ADS)

This dissertation proposes and investigates a novel architecture intended for microwave-based medical diagnosis (MBMD). Furthermore, this investigation proposes novel modifications of particle swarm optimization algorithm for achieving enhanced convergence performance. MBMD has been investigated through a variety of innovative techniques in the literature since the 1990's and has shown significant promise in early detection of some specific health threats. In comparison to the X-ray- and gamma-ray-based diagnostic tools, MBMD does not expose patients to ionizing radiation; and due to the maturity of microwave technology, it lends itself to miniaturization of the supporting systems. This modality has been shown to be effective in detecting breast malignancy, and hence, this study focuses on the same modality. A novel radiator device and detection technique is proposed and investigated in this dissertation. As expected, hardware design and implementation are of paramount importance in such a study, and a good deal of research, analysis, and evaluation has been done in this regard which will be reported in ensuing chapters of this dissertation. It is noteworthy that an important element of any detection system is the algorithm used for extracting signatures. Herein, the strong intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems is brought to bear. This task is accomplished through addressing both mathematical and electromagnetic problems. These problems are called benchmark problems throughout this dissertation, since they have known answers. After evaluating the performance of the algorithm for the chosen benchmark problems, the algorithm is applied to MBMD tumor detection problem. The chosen benchmark problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level of complexity and randomness inherent to the selection of electromagnetic benchmark problems, a trend to resort to oversimplification in order to arrive at reasonable solutions has been taken in literature when utilizing analytical techniques. Here, an attempt has been made to avoid oversimplification when using the proposed swarm-based optimization algorithms.

Modiri, Arezoo

376

Size optimization of space trusses using Big Bang–Big Crunch algorithm  

Microsoft Academic Search

A Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm is employed for optimal design of truss structures. HBB–BC is compared to Big Bang–Big Crunch (BB–BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB–BC method compared to other heuristic algorithms.

A. Kaveh; S. Talatahari

2009-01-01

377

Coil optimization for electromagnetic levitation using a genetic like algorithm  

NASA Astrophysics Data System (ADS)

The technique of electromagnetic levitation (EML) provides a means for thermally processing an electrically conductive specimen in a containerless manner. For the investigation of metallic liquids and related melting or freezing transformations, the elimination of substrate-induced nucleation affords access to much higher undercooling than otherwise attainable. With heating and levitation both arising from the currents induced by the coil, the performance of any EML system depends on controlling the balance between lifting forces and heating effects, as influenced by the levitation coil geometry. In this work, a genetic algorithm is developed and utilized to optimize the design of electromagnetic levitation coils. The optimization is targeted specifically to reduce the steady-state temperature of the stably levitated metallic specimen. Reductions in temperature of nominally 70 K relative to that obtained with the initial design are achieved through coil optimization, and the results are compared with experiments for aluminum. Additionally, the optimization method is shown to be robust, generating a small range of converged results from a variety of initial starting conditions. While our optimization criterion was set to achieve the lowest possible sample temperature, the method is general and can be used to optimize for other criteria as well.

Royer, Z. L.; Tackes, C.; LeSar, R.; Napolitano, R. E.

2013-06-01

378

Efficiency Improvements to the Displacement Based Multilevel Structural Optimization Algorithm  

NASA Technical Reports Server (NTRS)

Multilevel Structural Optimization (MSO) continues to be an area of research interest in engineering optimization. In the present project, the weight optimization of beams and trusses using Displacement based Multilevel Structural Optimization (DMSO), a member of the MSO set of methodologies, is investigated. In the DMSO approach, the optimization task is subdivided into a single system and multiple subsystems level optimizations. The system level optimization minimizes the load unbalance resulting from the use of displacement functions to approximate the structural displacements. The function coefficients are then the design variables. Alternately, the system level optimization can be solved using the displacements themselves as design variables, as was shown in previous research. Both approaches ensure that the calculated loads match the applied loads. In the subsystems level, the weight of the structure is minimized using the element dimensions as design variables. The approach is expected to be very efficient for large structures, since parallel computing can be utilized in the different levels of the problem. In this paper, the method is applied to a one-dimensional beam and a large three-dimensional truss. The beam was tested to study possible simplifications to the system level optimization. In previous research, polynomials were used to approximate the global nodal displacements. The number of coefficients of the polynomials equally matched the number of degrees of freedom of the problem. Here it was desired to see if it is possible to only match a subset of the degrees of freedom in the system level. This would lead to a simplification of the system level, with a resulting increase in overall efficiency. However, the methods tested for this type of system level simplification did not yield positive results. The large truss was utilized to test further improvements in the efficiency of DMSO. In previous work, parallel processing was applied to the subsystems level, where the derivative verification feature of the optimizer NPSOL had been utilized in the optimizations. This resulted in large runtimes. In this paper, the optimizations were repeated without using the derivative verification, and the results are compared to those from the previous work. Also, the optimizations were run on both, a network of SUN workstations using the MPICH implementation of the Message Passing Interface (MPI) and on the faster Beowulf cluster at ICASE, NASA Langley Research Center, using the LAM implementation of UP]. The results on both systems were consistent and showed that it is not necessary to verify the derivatives and that this gives a large increase in efficiency of the DMSO algorithm.

Plunkett, C. L.; Striz, A. G.; Sobieszczanski-Sobieski, J.

2001-01-01

379

Dominating Sets for Convex Functions with some Applications  

Microsoft Academic Search

A number of optimization methods require as a first step the construction of a dominating set (a set containing an optimal solution) enjoying properties such as compactness or convexity.In this note we address the problem of constructing dominating sets for problems whose objective is a componentwise nondecreasing function of (possibly an infinite number of) convex functions, and we show how

E. Carrizosa; J. B. G. Frenk

1996-01-01

380

Dominating Sets for Convex Functions with Some Applications  

Microsoft Academic Search

A number of optimization methods require as a first step the construction of a dominating set (a set containing an optimal solution) enjoying properties such as compactness or convexity. In this paper, we address the problem of constructing dominating sets for problems whose objective is a componentwise nondecreasing function of (possibly an infinite number of) convex functions, and we show

E. Carrizosa; J. B. G. Frenk

1998-01-01

381

Superresolution of passive millimeter-wave images using a combined maximum-likelihood optimization and projection-onto-convex-sets approach  

NASA Astrophysics Data System (ADS)

Imagery data acquired from Passive Millimeter-Wave (PMMW) radiometers have inherently poor resolution due to limited aperture dimensions and the consequent diffraction limits thus requiring processing by a sophisticated super- resolution algorithm before the images can be used for nay useful purposes such as surveillance, fusion, navigation and missile guidance. Recent research has produced a class of powerful algorithms that employ a Bayesian framework in order to iteratively optimize a likelihood function in the resolution enhancement process. These schemes, popularly called ML algorithms, enjoy several advantages such as simple digital implementation and robustness of performance to inaccurate estimation of sensor parameters. However, the convergence of iterations could in some cases become rather slow and practical implementations may require executing a large number of iterations before desired resolution levels can be achieved. The quality of restoration and the extent of achievable super-resolution depend on the accuracy and the amount of a prior information that could be utilized in processing the input imagery dat. Projection-based set- theoretic methods offer a considerable flexibility in incorporating available a priori information and hence provide an attractive framework for tailoring powerful restoration and super-resolution algorithms. The prior information, which is used as constraints during the processing, can be derived form a number of sources such as the phenomenology of the sensor employed, known conditions at the time of recording data, and scene-related information that could be extracted from the image. In this paper, we shall describe a POCS approach to image restoration and use it to enhance the super-resolution performance of ML algorithms. A new algorithm, termed POCS-assisted ML algorithm, that combines the strong points of ML and POCS approaches will be outlined. A quantitative evaluation of the performance of this algorithm for restoring and super- resolving PMMW image data will also be presented.

Sundareshan, Malur K.; Bhattacharjee, Supratik

2001-08-01

382

Multidisciplinary Design and Optimization of Multistage Ground-launched Boost Phase Interceptor Using Hybrid Search Algorithm  

Microsoft Academic Search

This article proposes a multidisciplinary design and optimization (MDO) strategy for the conceptual design of a multistage ground-based interceptor (GBI) using hybrid optimization algorithm, which associates genetic algorithm (GA) as a global optimizer with sequential quadratic programming (SQP) as a local optimizer. The interceptor is comprised of a three-stage solid propulsion system for an exoatmospheric boost phase intercept (BPI). The

Qasim Zeeshan; Dong Yunfeng; Khurram Nisar; Ali Kamran; Amer Rafique

2010-01-01

383

Library design using genetic algorithms for catalyst discovery and optimization  

NASA Astrophysics Data System (ADS)

This study reports a detailed investigation of catalyst library design by genetic algorithm (GA). A methodology for assessing GA configurations is described. Operators, which promote the optimization speed while being robust to noise and outliers, are revealed through statistical studies. The genetic algorithms were implemented in GA platform software called OptiCat, which enables the construction of custom-made workflows using a tool box of operators. Two separate studies were carried out (i) on a virtual benchmark and (ii) on real surface response which is derived from HT screening. Additionally, we report a methodology to model a complex surface response by binning the search space in small zones that are then independently modeled by linear regression. In contrast to artificial neural networks, this approach allows one to obtain an explicit model in an analogical form that can be further used in Excel or entered in OptiCat to perform simulations. While speeding the implementation of a hybrid algorithm combining a GA with a knowledge-based extraction engine is described, while speeding up the optimization process by means of virtual prescreening the hybrid GA enables one to open the "black-box" by providing knowledge as a set of association rules.

Clerc, Frederic; Lengliz, Mourad; Farrusseng, David; Mirodatos, Claude; Pereira, Sílvia R. M.; Rakotomalala, Ricco

2005-06-01

384

An optimized hybrid encode based compression algorithm for hyperspectral image  

NASA Astrophysics Data System (ADS)

Compression is a kernel procedure in hyperspectral image processing due to its massive data which will bring great difficulty in date storage and transmission. In this paper, a novel hyperspectral compression algorithm based on hybrid encoding which combines with the methods of the band optimized grouping and the wavelet transform is proposed. Given the characteristic of correlation coefficients between adjacent spectral bands, an optimized band grouping and reference frame selection method is first utilized to group bands adaptively. Then according to the band number of each group, the redundancy in the spatial and spectral domain is removed through the spatial domain entropy coding and the minimum residual based linear prediction method. Thus, embedded code streams are obtained by encoding the residual images using the improved embedded zerotree wavelet based SPIHT encode method. In the experments, hyperspectral images collected by the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) were used to validate the performance of the proposed algorithm. The results show that the proposed approach achieves a good performance in reconstructed image quality and computation complexity.The average peak signal to noise ratio (PSNR) is increased by 0.21~0.81dB compared with other off-the-shelf algorithms under the same compression ratio.

Wang, Cheng; Miao, Zhuang; Feng, Weiyi; He, Weiji; Chen, Qian; Gu, Guohua

2013-12-01

385

Using Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White  

E-print Network

-TSP algorithm as a Genetic Algorithm modification to ACS-TSP. The algorithm uses a GA to evolve a populationUsing Genetic Algorithms to optimize ACS-TSP Marcin L. Pilat and Tony White School of Computer,arpwhite}@scs.carleton.ca Abstract. We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve

White, Tony

386

Online and Offline Algorithmic Techniques for Communication Performance Optimization in Distributed Systems  

E-print Network

Online and Offline Algorithmic Techniques for Communication Performance Optimization in Distributed of communication optimization in distributed systems. Communication is a key issue in every distributed system and, since good communication performance is often difficult to achieve, optimization techniques are welcome

Paris-Sud XI, Université de

387

Swarm algorithms for single- and multi-objective optimization problems incorporating sensitivity analysis  

NASA Astrophysics Data System (ADS)

Swarm algorithms such as particle swarm optimization (PSO) are non-gradient probabilistic optimization algorithms that have been successfully applied for global searches in complex problems such as multi-peak problems. However, application of these algorithms to structural and mechanical optimization problems still remains a complex matter since local optimization capability is still inferior to general numerical optimization methods. This article discusses new swarm metaphors that incorporate design sensitivities concerning objective and constraint functions and are applicable to structural and mechanical design optimization problems. Single- and multi-objective optimization techniques using swarm algorithms are combined with a gradient-based method. In the proposed techniques, swarm optimization algorithms and a sequential linear programming (SLP) method are conducted simultaneously. Finally, truss structure design optimization problems are solved by the proposed hybrid method to verify the optimization efficiency.

Izui, K.; Nishiwaki, S.; Yoshimura, M.

2007-12-01

388

Optimized mean shift algorithm for color segmentation in image sequences  

NASA Astrophysics Data System (ADS)

The application of the mean shift algorithm to color image segmentation has been proposed in 1997 by Comaniciu and Meer. We apply the mean shift color segmentation to image sequences, as the first step of a moving object segmentation algorithm. Previous work has shown that it is well suited for this task, because it provides better temporal stability of the segmentation result than other approaches. The drawback is higher computational cost. For speed up of processing on image sequences we exploit the fact that subsequent frames are similar and use the cluster centers of previous frames as initial estimates, which also enhances spatial segmentation continuity. In contrast to other implementations we use the originally proposed CIE LUV color space to ensure high quality segmentation results. We show that moderate quantization of the input data before conversion to CIE LUV has little influence on the segmentation quality but results in significant speed up. We also propose changes in the post-processing step to increase the temporal stability of border pixels. We perform objective evaluation of the segmentation results to compare the original algorithm with our modified version. We show that our optimized algorithm reduces processing time and increases the temporal stability of the segmentation.

Bailer, Werner; Schallauer, Peter; Haraldsson, Harald B.; Rehatschek, Herwig

2005-03-01

389

Comparison of Particle Swarm Optimization and Genetic Algorithm in Rational Function Model Optimization  

NASA Astrophysics Data System (ADS)

Rational Function Models (RFM) are one of the most considerable approaches for spatial information extraction from satellite images especially where there is no access to the sensor parameters. As there is no physical meaning for the terms of RFM, in the conventional solution all the terms are involved in the computational process which causes over-parameterization errors. Thus in this paper, advanced optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are investigated to determine the optimal terms of RFM. As the optimization would reduce the number of required RFM terms, the possibility of using fewer numbers of Ground Control Points (GCPs) in the solution comparing to the conventional method is inspected. The results proved that both GA and PSO are able to determine the optimal terms of RFM to achieve rather the same accuracy. However, PSO shows to be more effective from computational time part of view. The other important achievement is that the algorithms are able to solve the RFM using less GCPs with higher accuracy in comparison to conventional RFM.

Yavari, S.; Zoej, M. J. V.; Mokhtarzade, M.; Mohammadzadeh, A.

2012-07-01

390

Optimizing Optical Quantum Logic Gates using Genetic Algorithms  

E-print Network

We introduce the method of using an annealing genetic algorithm to the numerically complex problem of looking for quantum logic gates which simultaneously have highest fidelity and highest success probability. We first use the linear optical quantum nonlinear sign (NS) gate as an example to illustrate the efficiency of this method. We show that by appropriately choosing the annealing parameters, we can reach the theoretical maximum success probability (1/4 for NS) for each attempt. We then examine the controlled-z (CZ) gate as the first new problem to be solved. Our goal is to use this method to find the maximum success probability for a CZ gate while maintaining a fidelity of 0.9997. Since the purpose of our algorithm is to optimize a unitary matrix for quantum transformations, it could easily be applied to other areas of interest such as quantum optics and quantum sensors.

Wu, Zhanghan; Uskov, Dmitry; Lee, Hwang; Dowling, Jonathan P

2007-01-01

391

Optimizing Optical Quantum Logic Gates using Genetic Algorithms  

E-print Network

We introduce the method of using an annealing genetic algorithm to the numerically complex problem of looking for quantum logic gates which simultaneously have highest fidelity and highest success probability. We first use the linear optical quantum nonlinear sign (NS) gate as an example to illustrate the efficiency of this method. We show that by appropriately choosing the annealing parameters, we can reach the theoretical maximum success probability (1/4 for NS) for each attempt. We then examine the controlled-z (CZ) gate as the first new problem to be solved. We show results that agree with the highest known maximum success probability for a CZ gate (2/27) while maintaining a fidelity of 0.9997. Since the purpose of our algorithm is to optimize a unitary matrix for quantum transformations, it could easily be applied to other areas of interest such as quantum optics and quantum sensors.

Zhanghan Wu; Sean D. Huver; Dmitry Uskov; Hwang Lee; Jonathan P. Dowling

2007-08-10

392

A parallel textured algorithm for optimal routing in data networks  

E-print Network

. Level Subnetioork ORP (a) A f( ? rP + Ei, rs = 1, 2, . . . , ~ W] subject to: (b) P ~ f & O?u g bi~ (3. 2) (c) f & 0, u g 9', where f] ?? [f&', . . . , f& I and fP denotes the flows over the branches u F fi' from OD pair ur; r& is a subvector.... (ii) go to Step 2. else if (max?eU, ~svr~ f?(j) ? f?(p ? 1)~ & s) then (i) /=1. (ii) f"(j+1) =f-(i), j =j+1 (iii) go to Step 2. else stop. ( the algorithm converges to a stationary point. ) end if end for Remark: Any optimization algorithm...

Hsieh, Wen-Lin

2012-06-07

393

An inflationary differential evolution algorithm for space trajectory optimization  

E-print Network

In this paper we define a discrete dynamical system that governs the evolution of a population of agents. From the dynamical system, a variant of Differential Evolution is derived. It is then demonstrated that, under some assumptions on the differential mutation strategy and on the local structure of the objective function, the proposed dynamical system has fixed points towards which it converges with probability one for an infinite number of generations. This property is used to derive an algorithm that performs better than standard Differential Evolution on some space trajectory optimization problems. The novel algorithm is then extended with a guided restart procedure that further increases the performance, reducing the probability of stagnation in deceptive local minima.

Massimiliano Vasile; Edmondo Minisci; Marco Locatelli

2011-04-25

394

Convex Bayes decision theory  

Microsoft Academic Search

The basic concepts of Levi's epistemic utility theory and credal convexity are presented. Epistemic utility, in addition to penalizing error as is done with traditional Bayesian decision methodology, permits a unit of informational value to be distributed among the hypotheses of a decision problem. Convex Bayes decision theory retains the conditioning structure of probability-based inference, but addresses many of the

W. C. Stirling; D. R. Morrell

1991-01-01

395

Gerrymandering and Convexity  

ERIC Educational Resources Information Center

Convexity-based measures of shape compactness provide an effective way to identify irregularities in congressional district boundaries. A low convexity coefficient may suggest that a district has been gerrymandered, or it may simply reflect irregularities in the corresponding state boundary. Furthermore, the distribution of population within a…

Hodge, Jonathan K.; Marshall, Emily; Patterson, Geoff

2010-01-01

396

Optimization of activated sludge designs using genetic algorithms.  

PubMed

We describe a framework in which a genetic algorithm (GA) and a static activated sludge (AS) treatment plant design model (WRC AS model) are used to identify low cost activated sludge designs that meet specified effluent limits (e.g. for BOD, N, and P). Once the user has chosen a particular process (Bardenpho, Biodenipho, UCT or SBR), this approach allows the parameterizations for each AS unit process to be optimized systematically and simultaneously. The approach is demonstrated for a wastewater treatment plant design problem and the GA-based performance is compared to that of a classical nonlinear optimization approach. The use of GAs for multiobjective problems such as AS design is demonstrated and their application for reliability-based design and alternative generation is discussed. PMID:12046573

Doby, T A; Loughlin, D H; de los Reyes, F L; Ducoste, J J

2002-01-01

397

Generalized Particle Swarm Algorithm for HCR Gearing Geometry Optimization  

NASA Astrophysics Data System (ADS)

Temperature scuffing evidenced by damage to teeth flanks of gears is one of the mostimportant problems needing to be solved in the process of gearing design and calculation. Accordingto current valid standards, such calculations can be resolved with a high level of reliability for all theusual gearing types. However, suitable calculations for HCR gears have not been adequatelyresearched to date. It has been identified that in HCR gears some different process of scuffingformation occurs during the gear`s operation. In this article, the authors describe a new method forfinding optimal solutions for * a1 h , * a 2 h and x1, using a Generalized Particle Swarm OptimizationAlgorithm.

Kuzmanovi?, Siniša; Vereš, Miroslav; Rackov, Milan

2012-12-01

398

Quadruped Robot Locomotion using a Global Optimization Stochastic Algorithm  

NASA Astrophysics Data System (ADS)

The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability. Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the GA to solve the proposed constrained optimization problem and make the search more efficient. The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with a high velocity and wide stability margin for a quadruped slow crawl gait.

Oliveira, Miguel; Santos, Cristina; Costa, Lino; Ferreira, Manuel

2011-09-01

399

Multi-population Binary ant Colony Algorithm with Concrete Behaviors for multi-objective optimization problem  

Microsoft Academic Search

Aiming at solving the drawbacks of the original binary ant colony algorithm on multi-objective optimization problems: easy to fall into the local optimization and difficult to get the Pareto optimal solutions, we proposed Multi-population Binary ant Colony Algorithm with Concrete Behaviors (MPBACB). The algorithm introduced multi-population method to ensure the globe optimization ability, and use environmental evaluation\\/reward model to improve

Ye Qing; Xiong Wei-Qing; Jiang Bao-chuan

2010-01-01

400

Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling  

NASA Astrophysics Data System (ADS)

This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and operational specialization. The standard approach to neural-evolution is at the network level such that a population of working solutions is manipulated until the fittest member is found. SANE [Symbiotic Adaptive Neuro-Evolution]1 source code offers an alternative method based on co-operative co-evolution in which a population of hidden neurons is evolved. The task of each hidden neuron is to establish appropriate connections that will provide: [i] a functional solution and [ii] performance improvements. Each member of the population attempts to optimize one particular aspect of the overall modelling process and evolution can lead to several different forms of specialization. This method of adaptive evolution also facilitates the creation of symbiotic relationships in which individual members must co-operate with others - who must be present - to permit survival. 1http://www.cs.utexas.edu/users/nn/pages/software/abstracts.html#sane-c

Abrahart, R. J.

2004-05-01

401

An optimized algorithm for detecting and annotating regional differential methylation  

PubMed Central

Background DNA methylation profiling reveals important differentially methylated regions (DMRs) of the genome that are altered during development or that are perturbed by disease. To date, few programs exist for regional analysis of enriched or whole-genome bisulfate conversion sequencing data, even though such data are increasingly common. Here, we describe an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequence data that is applicable to enriched whole-genome methylation profiling datasets, as well as other globally enriched epigenetic modification data. Results Here we show that our bimodal distribution model and weighted cost function for optimized regional methylation analysis provides accurate boundaries of regions harboring significant epigenetic modifications. Our algorithm takes the spatial distribution of CpGs into account for the enrichment assay, allowing for optimization of the definition of empirical regions for differential methylation. Combined with the dependent adjustment for regional p-value combination and DMR annotation, we provide a method that may be applied to a variety of datasets for rapid DMR analysis. Our method classifies both the directionality of DMRs and their genome-wide distribution, and we have observed that shows clinical relevance through correct stratification of two Acute Myeloid Leukemia (AML) tumor sub-types. Conclusions Our weighted optimization algorithm eDMR for calling DMRs extends an established DMR R pipeline (methylKit) and provides a needed resource in epigenomics. Our method enables an accurate and scalable way of finding DMRs in high-throughput methylation sequencing experiments. eDMR is available for download at http://code.google.com/p/edmr/. PMID:23735126

2013-01-01

402

Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems.  

PubMed

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations. PMID:17550112

Wang, Yong; Cai, Zixing; Guo, Guanqi; Zhou, Yuren

2007-06-01

403

Optimization of reconstruction algorithms using Monte Carlo simulation  

SciTech Connect

A method for optimizing reconstruction algorithms is presented that is based on how well a specified task can be performed using the reconstructed images. Task performance is numerically assessed by a Monte Carlo simulation of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, reconstruction, and performance of the stated task based on the final image. The use of this method is demonstrated through the optimization of the Algebraic Reconstruction Technique (ART), which reconstructs images from their projections by an iterative procedure. The optimization is accomplished by varying the relaxation factor employed in the updating procedure. In some of the imaging situations studied, it is found that the optimization of constrained ART, in which a nonnegativity constraint is invoked, can vastly increase the detectability of objects. There is little improvement attained for unconstrained ART. The general method presented may be applied to the problem of designing neutron-diffraction spectrometers. 11 refs., 6 figs., 2 tabs.

Hanson, K.M.

1989-01-01

404

Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails  

PubMed Central

Background Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications. PMID:23134742

2012-01-01

405

Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers  

NASA Astrophysics Data System (ADS)

Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our "matrix-free" approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image ?2 and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest ?2 is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.

Rogers, Adam; Fiege, Jason D.

2011-02-01

406

Classifying convex sets for vessel detection in retinal images  

Microsoft Academic Search

We present a method to detect vessels in images of the retina. Instead of relying on pixel classification, as many detection algorithms do, we propose a more natural representation for elongated structures, such as vessels. This new representation consists of primitives called affine convex sets. On these convex sets we apply the classification step. The reason for choosing this approach

Joes Staal; Stiliyan N. Kalitzin; Michael D. Abrcimofi; T. T. J. M. Berendschot; Bram Van Ginneken; Max A. Viergever

2002-01-01

407

Convex Delay Models for Transistor Sizing Mahesh Ketkar  

E-print Network

Convex Delay Models for Transistor Sizing Mahesh Ketkar Department of ECE, University of Minnesota for developing accurate con- vex delay models to be used for transistor sizing. A new rich class of convex for modern designs. The delay model is incorpo- rated into a transistor sizing algorithm based on TILOS

Sapatnekar, Sachin

408

An adaptive /N-body algorithm of optimal order  

NASA Astrophysics Data System (ADS)

Picard iteration is normally considered a theoretical tool whose primary utility is to establish the existence and uniqueness of solutions to first-order systems of ordinary differential equations (ODEs). However, in 1996, Parker and Sochacki [Neural, Parallel, Sci. Comput. 4 (1996)] published a practical numerical method for a certain class of ODEs, based upon modified Picard iteration, that generates the Maclaurin series of the solution to arbitrarily high order. The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns. The class is wider than might be expected. The method is ideally suited to the classical N-body problem, which is projectively polynomial. Here, we recast the N-body problem in polynomial form and develop a Picard-based algorithm for its solution. The algorithm is highly accurate, parameter-free, and simultaneously adaptive in time and order. Test cases for both benign and chaotic N-body systems reveal that optimal order is dynamic. That is, in addition to dependency upon N and the desired accuracy, optimal order depends upon the configuration of the bodies at any instant.

Pruett, C. David; Rudmin, Joseph W.; Lacy, Justin M.

2003-05-01

409

A Nonlinear GMRES Optimization Algorithm for Canonical Tensor Decomposition  

E-print Network

A new algorithm is presented for computing a canonical rank-R tensor approximation that has minimal distance to a given tensor in the Frobenius norm, where the canonical rank-R tensor consists of the sum of R rank-one components. Each iteration of the method consists of three steps. In the first step, a tentative new iterate is generated by a stand-alone one-step process, for which we use alternating least squares (ALS). In the second step, an accelerated iterate is generated by a nonlinear generalized minimal residual (GMRES) approach, recombining previous iterates in an optimal way, and essentially using the stand-alone one-step process as a preconditioner. In particular, the nonlinear extension of GMRES is used that was proposed by Washio and Oosterlee in [ETNA Vol. 15 (2003), pp. 165-185] for nonlinear partial differential equation problems. In the third step, a line search is performed for globalization. The resulting nonlinear GMRES (N-GMRES) optimization algorithm is applied to dense and sparse tensor ...

De Sterck, Hans

2011-01-01

410

In-Space Radiator Shape Optimization using Genetic Algorithms  

NASA Technical Reports Server (NTRS)

Future space exploration missions will require the development of more advanced in-space radiators. These radiators should be highly efficient and lightweight, deployable heat rejection systems. Typical radiators for in-space heat mitigation commonly comprise a substantial portion of the total vehicle mass. A small mass savings of even 5-10% can greatly improve vehicle performance. The objective of this paper is to present the development of detailed tools for the analysis and design of in-space radiators using evolutionary computation techniques. The optimality criterion is defined as a two-dimensional radiator with a shape demonstrating the smallest mass for the greatest overall heat transfer, thus the end result is a set of highly functional radiator designs. This cross-disciplinary work combines topology optimization and thermal analysis design by means of a genetic algorithm The proposed design tool consists of the following steps; design parameterization based on the exterior boundary of the radiator, objective function definition (mass minimization and heat loss maximization), objective function evaluation via finite element analysis (thermal radiation analysis) and optimization based on evolutionary algorithms. The radiator design problem is defined as follows: the input force is a driving temperature and the output reaction is heat loss. Appropriate modeling of the space environment is added to capture its effect on the radiator. The design parameters chosen for this radiator shape optimization problem fall into two classes, variable height along the width of the radiator and a spline curve defining the -material boundary of the radiator. The implementation of multiple design parameter schemes allows the user to have more confidence in the radiator optimization tool upon demonstration of convergence between the two design parameter schemes. This tool easily allows the user to manipulate the driving temperature regions thus permitting detailed design of in-space radiators for unique situations. Preliminary results indicate an optimized shape following that of the temperature distribution regions in the "cooler" portions of the radiator. The results closely follow the expected radiator shape.

Hull, Patrick V.; Kittredge, Ken; Tinker, Michael; SanSoucie, Michael

2006-01-01

411

A New Distributed Resource-Allocation Algorithm with Optimal Failure Locality  

Microsoft Academic Search

Failure locality measures an algorithm's robustness to process failures. We present a new algorithm for the dining philosophers problem | a classic problem in distributed resource allocation | that has optimal fail- ure locality. As a renement, the algorithm can be eas- ily parameterized by a simple failure model to achieve super-optimal failure locality in the average case.

Paolo A. G. Sivilotti; Scott M. Pike; Nigamanth Sridhar

2000-01-01

412

Genetic Algorithms: A Fundamental Component of an Optimization Toolkit for Improved  

E-print Network

Genetic Algorithms: A Fundamental Component of an Optimization Toolkit for Improved Engineering played by the toolkit's genetic algorithm in providing a robust, general purpose solution to nonlinear. This paper focuses on the role of the Genetic Algorithm in iSIGHT's Optimization Toolkit and its application

Coello, Carlos A. Coello

413

The Leap-Frog Algorithm and Optimal Control: Background and Demonstration  

E-print Network

The Leap-Frog Algorithm and Optimal Control: Background and Demonstration C. Yalc n Kaya School recently developed by J.L. Noakes, called the Leap-Frog Algorithm, can nd geodesics globally in a connected in optimal control. The motivation that led to the Leap-Frog Algorithm is also emphasized. Key words

Kaya, Yalcin

414

The PID prediction control system using particle swarm optimization and genetic algorithms  

Microsoft Academic Search

In this paper, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to optimize the parameters of PID algorithm in order to improve the performance of PID control system. Moreover, we propose the grey model based on grey system theory to combine with PID control to establish the PID prediction control system. The proposed control system can

Guo-Dong Li; Chen-Hong Wang; Shiro Masuda; Daisuke Yamaguchi; Masatake Nagai

2009-01-01

415

A Greedy Algorithm for Optimal Recombination Shiquan Wu and Xun Gu  

E-print Network

A Greedy Algorithm for Optimal Recombination Shiquan Wu and Xun Gu Center of Bioinformatics A from S by a series of recombinations in minimum number of steps. We present a greedy algorithm 2001 #12;A Greedy Algorithm for Optimal Recombination 87 s2 = b1b2 · · · bjbj+1 · · · bn n

Gu, Xun

416

Local Search Genetic Algorithm for Optimal Design of Reliable Networks Berna Dengiz and Fulya Altiparmak  

E-print Network

Corresponding author. #12;1 Local Search Genetic Algorithm for Optimal Design of Reliable Networks AbstractLocal Search Genetic Algorithm for Optimal Design of Reliable Networks Berna Dengiz and Fulya Pittsburgh, Pennsylvania 15261 USA aesmith@engrng.pitt.edu Abstract This paper presents a genetic algorithm

Smith, Alice E.

417

Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs  

Microsoft Academic Search

This paper presents a novel discrete population based stochastic optimization algorithm inspired from weed colonization. Its performance in a discrete benchmark, time-cost trade-off (TCT) problem, is evaluated and compared with five other evolutionary algorithms. Also we use our proposed discrete invasive weed optimization (DIWO) algorithm for cooperative multiple task assignment of unmanned aerial vehicles (UAVs) and compare the solutions with

Mohsen Ramezani Ghalenoei; Hossein Hajimirsadeghi; Caro Lucas

2009-01-01

418

On the Optimality of Allen and Kennedy's Algorithm for Parallelism Extraction in Nested Loops  

E-print Network

On the Optimality of Allen and Kennedy's Algorithm for Parallelism Extraction in Nested Loops Alain is that Allen and Kennedy's algorithm is optimal when dependences are approximated by dependence levels and Kennedy's algorithm, as long as dependence level is the only information available. 1 Introduction Many

Vivien, Frédéric

419

On the optimality of Allen and Kennedy's algorithm for parallelism extraction in nested loops  

E-print Network

On the optimality of Allen and Kennedy's algorithm for parallelism extraction in nested loops Alain. The result of this paper is that Allen and Kennedy's algorithm is optimal when dependences are approximated found by Allen and Kennedy's algorithm, as long as dependence level is the only information available

Vivien, Frédéric

420

PATTERN SYNTHESIS OF CYLINDRICAL CONFORMAL ARRAY BY THE MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM  

Microsoft Academic Search

In order to overcome drawbacks of standard particle swarm optimization (PSO) algorithm, such as prematurity and easily trapping in local optimum, a modified PSO algorithm which adopts a global best perturbation, is used to optimize the pattern of cylindrical conformal antenna array for sidelobe level (SLL) suppression and null control in certain directions.The convergence speed and accuracy of the algorithm

Zhan-Bo Lu; An Zhang; Xin-Yu Hou

2008-01-01

421

A Sparse Representation-Based Deployment Method for Optimizing the Observation Quality of Camera Networks  

PubMed Central

Deployment is a critical issue affecting the quality of service of camera networks. The deployment aims at adopting the least number of cameras to cover the whole scene, which may have obstacles to occlude the line of sight, with expected observation quality. This is generally formulated as a non-convex optimization problem, which is hard to solve in polynomial time. In this paper, we propose an efficient convex solution for deployment optimizing the observation quality based on a novel anisotropic sensing model of cameras, which provides a reliable measurement of the observation quality. The deployment is formulated as the selection of a subset of nodes from a redundant initial deployment with numerous cameras, which is an ?0 minimization problem. Then, we relax this non-convex optimization to a convex ?1 minimization employing the sparse representation. Therefore, the high quality deployment is efficiently obtained via convex optimization. Simulation results confirm the effectiveness of the proposed camera deployment algorithms. PMID:23989826

Wang, Chang; Qi, Fei; Shi, Guangming; Wang, Xiaotian

2013-01-01

422

Quantum-inspired immune clonal algorithm for global optimization.  

PubMed

Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum not gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result. PMID:18784009

Jiao, Licheng; Li, Yangyang; Gong, Maoguo; Zhang, Xiangrong

2008-10-01

423

Parallel global optimization with the particle swarm algorithm.  

PubMed

Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima-large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available. PMID:17891226

Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D

2004-12-01

424

The CrIMSS EDR Algorithm: Characterization, Optimization, and Validation  

NASA Astrophysics Data System (ADS)

Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) instruments aboard the Suomi National Polar-orbiting Partnership satellite provide high-quality hyperspectral infrared and microwave observations to retrieve atmospheric vertical temperature and moisture profiles (AVTP and AVMP) and many other environmental data records (EDRs). The official CrIS and ATMS EDR algorithm, together called the Cross-track Infrared and Microwave Sounding Suite (CrIMSS), produces EDR products on an operational basis through the interface data processing segment. The CrIMSS algorithm group is to assess and ensure that operational EDRs meet beta and provisional maturity requirements and are ready for stages 1-3 validations. This paper presents a summary of algorithm optimization efforts, as well as characterization and validation of the AVTP and AVMP products using the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis, the Atmospheric Infrared Sounder (AIRS) retrievals, and conventional and dedicated radiosonde observations. The global root-mean-square (RMS) differences between the CrIMSS products and the ECMWF show that the AVTP is meeting the requirements for layers 30-300 hPa (1.53 K versus 1.5 K) and 300-700 hPa (1.28 K versus 1.5 K). Slightly higher RMS difference for the 700 hPa-surface layer (1.78 K versus 1.6 K) is attributable to land and polar profiles. The AVMP product is within the requirements for 300-600 hPa (26.8% versus 35%) and is close in meeting the requirements for 600 hPa-surface (25.3% versus 20%). After just one year of maturity, the CrIMSS EDR products are quite comparable to the AIRS heritage algorithm products and show readiness for stages 1-3 validations.

Divakarla, Murty; Barnet, Christopher; Liu, Xu; Gu, Degui; Wilson, Michael; Kizer, Susan; Xiong, Xiaozhen; Maddy, Eric; Ferraro, Ralph; Knuteson, Robert; Hagan, Denise; Ma, Xia-lin; Tan, Changyi; Nalli, Nicholas; Reale, Anthony; Mollner, Andrew K.; Yang, Wenze; Gambacorta, Antonia; Feltz, Michelle; Iturbide-Sanchez, Flavio; Sun, Bomin; Goldberg, Mitch

2014-04-01

425

An investigation of two network flow optimization algorithms  

E-print Network

and with the linear programming Simplex algorithm. SUPERKIL is shown to be up to twice as fast as the Out- of-Kilter algorithm. The Simplex algorithm is at least ten times slower than either of the other algorithms. A principle which increases the computational.... THE ALGORITHMS 10 The Out-of -Kilter Algorithm The SUPERKIL Algorithm The Simplex Algorithm 10 23 31 III. PROCEDURES 34 Computer Investigation and Comparison . , 34 Development of Theorems and Conjectures . 51 IV. RESULTS 53 Computer Results Theorems...

Steelquist, John Anders

2012-06-07

426

Optimal reactive\\/voltage control by an improved harmony search algorithm  

Microsoft Academic Search

This paper presents an improved harmony search algorithm for the optimal reactive\\/voltage control problem. Optimal reactive\\/voltage control is a mixed integer, nonlinear optimization problem which includes both continuous and discrete control variables. The proposed algorithm is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation

A. H. Khazali; A. Parizad; M. Kalantar

2010-01-01

427

Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization  

Microsoft Academic Search

In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that

Boumediene ALLAOUA; Abdellah LAOUFI; Brahim GASBAOUI; Abdelfatah NASRI; Abdessalam ABDERRAHMANI

2008-01-01

428

Anatomy Based 3D Dose Optimization in Brachytherapy Using Multiobjective Genetic Algorithms  

E-print Network

Anatomy Based 3D Dose Optimization in Brachytherapy Using Multiobjective Genetic Algorithms M: Anatomy Based 3D Dose Optimization .... Page 2 of 2 Abstract In conventional dose optimization algorithms in terms of the COIN distribution and differential volume histograms, taking into account patient anatomy

Coello, Carlos A. Coello

429

Optimization of Training Samples with Affinity Propagation Algorithm for Multi-class SVM Classification  

Microsoft Academic Search

This paper presents a novel optimization method of training samples with Affinity Propagation (AP) clustering algorithm for multi-class Support Vector Machine (SVM) classification problem. The method of optimizing training samples is based on region clustering with affinity propagation algorithm. Then the multi-class support vector machines are trained for natural image classification with AP optimized samples. The feature space constructed in

Guangjun Lv; Qian Yin; Bingxin Xu; Ping Guo

2010-01-01

430

A new representation in evolutionary algorithms for the optimization of bioprocesses  

Microsoft Academic Search

Evolutionary Algorithms (EAs) have been used to achieve optimal feedforward control in a number of fed- batch fermentation processes. Typically, the optimiza- tion purpose is to set the optimal feeding trajectory, be- ing the feeding profile over time given by a piecewise lin- ear function, in order to reduce the number of parame- ters to the optimization algorithm. In this

Miguel Rocha; Isabel Rocha; Eugénio C. Ferreira

2005-01-01

431

Optimal Refueling Pattern Search for a CANDU Reactor Using a Genetic Algorithm  

Microsoft Academic Search

This paper presents the results from the application of genetic algorithms to a refueling optimization of a Canada deuterium uranium (CANDU) reactor. This work aims at making a mathematical model of the refueling optimization problem including the objective function and constraints and developing a method based on genetic algorithms to solve the problem. The model of the optimization problem and

DO Quang Binh; ROH Gyuhong; CHOI Hangbok

2006-01-01

432

Detecting rigid convexity of bivariate polynomials  

Microsoft Academic Search

Given a polynomial x ? Rn 7? p(x) in n = 2 variables, a symbolic-numerical algorithm is first described for detecting whether the connected component of the plane sublevel set P = {x : p(x) ? 0} containing the origin is rigidly convex, or equivalently, whether it has a linear matrix inequality (LMI) representation, or equivalently, if polynomial p(x) is

Didier Henriona

433

A Short Proof of Optimality for the MIN Cache Replacement Algorithm  

E-print Network

A Short Proof of Optimality for the MIN Cache Replacement Algorithm Benjamin Van Roy Stanford University December 2, 2010 Abstract The MIN algorithm is an offline strategy for deciding which item programming argument. Keywords: analysis of algorithms, on-line algorithms, caching, paging 1 The MIN

Van Roy, Ben

434

Modified Niched Pareto Multi-objective Genetic Algorithm for Construction Scheduling Optimization  

E-print Network

, a new data structure is proposed to define a solution to the problem and a general Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate optimization procedure. The main features of the proposed Multi-Objective Genetic Algorithm (MOGA...

Kim, Kyungki

2012-10-19

435

New knowledge-based genetic algorithm for excavator boom structural optimization  

NASA Astrophysics Data System (ADS)

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Hua, Haiyan; Lin, Shuwen

2014-03-01

436

Convex-Optimization-Based Enforcement of Robust BIBO Stability on the AIC Scheme Using a Modified RLS Algorithm  

Microsoft Academic Search

This paper addresses the issues relating to the enforcement of robust BIBO (linfin) stability when implementing the adaptive inverse control (AIC) scheme for noise cancellation. In this scheme, an adaptive FIR-form filter is added to a closed-loop system in order to reduce the output error caused by external disturbances. A small-gain-theorem-based sufficient stability condition, which accounts for the feedback interaction

N. O. P. Arancibia

2006-01-01

437

Computational and statistical tradeoffs via convex relaxation  

PubMed Central

Modern massive datasets create a fundamental problem at the intersection of the computational and statistical sciences: how to provide guarantees on the quality of statistical inference given bounds on computational resources, such as time or space. Our approach to this problem is to define a notion of “algorithmic weakening,” in which a hierarchy of algorithms is ordered by both computational efficiency and statistical efficiency, allowing the growing strength of the data at scale to be traded off against the need for sophisticated processing. We illustrate this approach in the setting of denoising problems, using convex relaxation as the core inferential tool. Hierarchies of convex relaxations have been widely used in theoretical computer science to yield tractable approximation algorithms to many computationally intractable tasks. In the current paper, we show how to endow such hierarchies with a statistical characterization and thereby obtain concrete tradeoffs relating algorithmic runtime to amount of data. PMID:23479655

Chandrasekaran, Venkat; Jordan, Michael I.

2013-01-01

438

Convex Graph Invariants  

E-print Network

The structural properties of graphs are usually characterized in terms of invariants, which are functions of graphs that do not depend on the labeling of the nodes. In this paper we study convex graph invariants, which are ...

Chandrasekaran, Venkat

439

Stereotype locally convex spaces  

NASA Astrophysics Data System (ADS)

We give complete proofs of some previously announced results in the theory of stereotype (that is, reflexive in the sense of Pontryagin duality) locally convex spaces. These spaces have important applications in topological algebra and functional analysis.

Akbarov, S. S.

2000-08-01

440

A hybrid optimization algorithm for the job-shop scheduling problem  

Microsoft Academic Search

The job-shop scheduling problem is a NP-hard combinational optimization and one of the best-known machine scheduling problems. Genetic algorithm is an effective search algorithm to solve this problem; however the quality of the best solution obtained by the algorithm has to improve due to its limitation. The paper proposes a novel hybrid optimization algorithm for the job-shop scheduling problem, which

Qiang Zhou; Xunxue Cui; Zhengshan Wang; Bin Yang

2009-01-01

441

Maximizing microbial perchlorate degradation using a genetic algorithm: consortia optimization.  

PubMed

Microorganisms in consortia perform many tasks more effectively than individual organisms and in addition grow more rapidly and in greater abundance. In this work, experimental datasets were assembled consisting of all possible selected combinations of perchlorate reducing strains of microorganisms and their perchlorate degradation rates were evaluated. A genetic algorithm (GA) methodology was successfully applied to define sets of microbial strains to achieve maximum rates of perchlorate degradation. Over the course of twenty generations of optimization using a GA, we saw a statistically significant 2.06 and 4.08-fold increase in average perchlorate degradation rates by consortia constructed using solely the perchlorate reducing bacteria (PRB) and by consortia consisting of PRB and accompanying organisms that did not degrade perchlorate, respectively. The comparison of kinetic rates constant in two types of microbial consortia additionally showed marked increases. PMID:23229741

Kucharzyk, Katarzyna H; Soule, Terence; Hess, Thomas F

2013-09-01

442

Stochastic optimal phase retrieval algorithm for high-contrast imaging  

NASA Astrophysics Data System (ADS)

The Princeton University Terrestrial Planet Finder (TPF) has been working on a novel method for direct imaging of extra solar planets using a shaped-pupil coronagraph. The entrance pupil of the coronagraph is optimized to have a point spread function (PSF) that provides the suppression level needed at the angular separation required for detection of extra solar planets. When integration time is to be minimized, the photon count at the planet location in the image plane is a Poisson distributed random process. The ultimate limitation of these high-dynamic-range imaging systems comes from scattering due to imperfections in the optical surfaces of the collecting system. The first step in correcting the wavefront errors is the estimation of the phase aberrations. The phase aberration caused by these imperfections is assumed to be a sum of two-dimensional sinusoidal functions. Its parameters are estimated using a global search with a genetic algorithm and a local optimization with the BFGS quasi-Newton method with a mixed quadratic and cubic line search procedure.

Give'on, Amir; Kasdin, N. Jeremy; Vanderbei, Robert J.; Spergel, David N.; Littman, Michael G.; Gurfil, Pini

2003-12-01

443

A homogeneous superconducting magnet design using a hybrid optimization algorithm  

NASA Astrophysics Data System (ADS)

This paper employs a hybrid optimization algorithm with a combination of linear programming (LP) and nonlinear programming (NLP) to design the highly homogeneous superconducting magnets for magnetic resonance imaging (MRI). The whole work is divided into two stages. The first LP stage provides a global optimal current map with several non-zero current clusters, and the mathematical model for the LP was updated by taking into account the maximum axial and radial magnetic field strength limitations. In the second NLP stage, the non-zero current clusters were discretized into practical solenoids. The superconducting conductor consumption was set as the objective function both in the LP and NLP stages to minimize the construction cost. In addition, the peak-peak homogeneity over the volume of imaging (VOI), the scope of 5 Gauss fringe field, and maximum magnetic field strength within superconducting coils were set as constraints. The detailed design process for a dedicated 3.0 T animal MRI scanner was presented. The homogeneous magnet produces a magnetic field quality of 6.0 ppm peak-peak homogeneity over a 16 cm by 18 cm elliptical VOI, and the 5 Gauss fringe field was limited within a 1.5 m by 2.0 m elliptical region.

Ni, Zhipeng; Wang, Qiuliang; Liu, Feng; Yan, Luguang

2013-12-01

444

A computer algorithm to optimize the scheduling of strategic sealift  

E-print Network

Operation Desert Shield. A mathematical model for the problem is proposed and an algorithm is developed and applied to solve the scheduling problem. Results of the algorithm are compared with randomly generated schedules to determine algorithm effectiveness....

Lambert, Garrett Randall

2012-06-07

445

Investigation of genetic algorithm design representation for multi-objective truss optimization  

E-print Network

and optimization methods a challenge even for smaller-scale structural systems. Modern researchers have been using Genetic Algorithm (GAs) (Holland 1975; Goldberg 1989) and other heuristic methods to search for near-optimal topology layouts... Algorithms (MOGA) (Goldberg 1989). In single-objective optimization, typically one near-optimal solution is desired and this solution is the best at meeting the stated objective. However, most realistic engineering problems have multiple, often conflicting...

Pathi, Soumya Sundar

2006-10-30

446

Experimental Study on a Hybrid Nature-Inspired Algorithm for Financial Portfolio Optimization  

Microsoft Academic Search

\\u000a Hybrid intelligent schemes have proven their efficiency in solving NP-hard optimization problems. Portfolio optimization refers\\u000a to the problem of finding the optimal combination of assets and their corresponding weights which satisfies a specific investment\\u000a goal and various constraints. In this study, a hybrid intelligent metaheuristic, which combines the Ant Colony Optimization\\u000a algorithm and the Firefly algorithm, is proposed in tackling

Giorgos Giannakouris; Vassilios Vassiliadis; Georgios Dounias

2010-01-01

447

A Combined D.C. Optimization—Ellipsoidal Branch-and-Bound Algorithm for Solving Nonconvex Quadratic Programming Problems  

Microsoft Academic Search

In this paper we propose a new branch-and-bound algorithm by using an ellipsoidal partition for minimizing an indefinite quadratic function over a bounded polyhedral convex set which is not necessarily given explicitly by a system of linear inequalities and\\/or equalities. It is required that for this set there exists an efficient algorithm to verify whether a point is feasible, and

Le Thi Hoai An; Pham Dinh Tao; Le Dung Muu

1998-01-01

448

Convex Formulations of Air Traffic Flow  

E-print Network

INVITED P A P E R Convex Formulations of Air Traffic Flow Optimization Problems A new technique using a Eulerian network model to describe air traffic flow. The evolution of traffic on each edge in the Oakland Air Route Traffic Control Center. Several computational aspects of the method are assessed

449

On Convex Relaxations for Quadratically Constrained Quadratic ...  

E-print Network

Jul 28, 2010 ... In the case that Qi ? 0 for each i, QCQP is a convex programming problem that can be solved ... QCQP is a fundamental problem that has been extensively studied in the global optimization literature; ..... In Table 1 we report the results of applying several increasingly tight ..... SIAM Review, 38:49–95,. 1996.

2010-07-28

450

NETWORK OPTIMIZATION FOR STEADY FLOW AND WATER HAMMER USING GENETIC ALGORITHMS  

Microsoft Academic Search

The paper presents the water network optimization by selecting the optimal pipe diameters for steady state flow and water hammer. The optimization method used is the Genetic Algorithm (GA). The GA's have been used in solving the water network optimization for steady state conditions. The GA is integrated with the Newton- Raphson program and a transient analysis program to improve

Berge Djebedjian; Mohamed S. Mohamed; Abdel-Gawad Mondy

2005-01-01

451

Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains  

NASA Astrophysics Data System (ADS)

Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures.

Dutta, Rajdeep; Ganguli, Ranjan; Mani, V.

2011-10-01

452

A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model.  

PubMed

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. PMID:24613939

Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

2014-09-01

453

Ultra-fast fluence optimization for beam angle selection algorithms  

NASA Astrophysics Data System (ADS)

Beam angle selection (BAS) including fluence optimization (FO) is among the most extensive computational tasks in radiotherapy. Precomputed dose influence data (DID) of all considered beam orientations (up to 100 GB for complex cases) has to be handled in the main memory and repeated FOs are required for different beam ensembles. In this paper, the authors describe concepts accelerating FO for BAS algorithms using off-the-shelf multiprocessor workstations. The FO runtime is not dominated by the arithmetic load of the CPUs but by the transportation of DID from the RAM to the CPUs. On multiprocessor workstations, however, the speed of data transportation from the main memory to the CPUs is non-uniform across the RAM; every CPU has a dedicated memory location (node) with minimum access time. We apply a thread node binding strategy to ensure that CPUs only access DID from their preferred node. Ideal load balancing for arbitrary beam ensembles is guaranteed by distributing the DID of every candidate beam equally to all nodes. Furthermore we use a custom sorting scheme of the DID to minimize the overall data transportation. The framework is implemented on an AMD Opteron workstation. One FO iteration comprising dose, objective function, and gradient calculation takes between 0.010 s (9 beams, skull, 0.23 GB DID) and 0.070 s (9 beams, abdomen, 1.50 GB DID). Our overall FO time is < 1 s for small cases, larger cases take ~ 4 s. BAS runs including FOs for 1000 different beam ensembles take ~ 15-70 min, depending on the treatment site. This enables an efficient clinical evaluation of different BAS algorithms.

Bangert, M.; Ziegenhein, P.; Oelfke, U.

2014-03-01

454

Optimization of the double dosimetry algorithm for interventional cardiologists  

NASA Astrophysics Data System (ADS)

A double dosimetry method is recommended in interventional cardiology (IC) to assess occupational exposure; yet currently there is no common and universal algorithm for effective dose estimation. In this work, flexible and adaptive algorithm building methodology was developed and some specific algorithm applicable for typical irradiation conditions of IC procedures was obtained. It was shown that the obtained algorithm agrees well with experimental measurements and is less conservative compared to other known algorithms.

Chumak, Vadim; Morgun, Artem; Bakhanova, Elena; Voloskiy, Vitalii; Borodynchik, Elena

2014-11-01

455

An adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization.  

PubMed

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail. PMID:24688370

Yu, Xiaobing; Cao, Jie; Shan, Haiyan; Zhu, Li; Guo, Jun

2014-01-01

456

Planning and Optimization Algorithms for Image-Guided Medical Procedures Ron Alterovitz  

E-print Network

. In this dissertation, we combine ideas from robotics, physically-based modeling, and operations research to develop newPlanning and Optimization Algorithms for Image-Guided Medical Procedures by Ron Alterovitz B-Guided Medical Procedures Copyright c 2006 by Ron Alterovitz #12;Abstract Planning and Optimization Algorithms

Alterovitz, Ron

457

Optimal fuzzy control of the spindle motor in a CD-ROM drive using genetic algorithms  

Microsoft Academic Search

An optimal controller of the spindle motor in a CD-ROM drive is designed using fuzzy logic with genetic algorithms. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are searched by means of genetic algorithms. Computer simulations demonstrate that the fuzzy controller

Gwo-Ruey Yu; Rey-Chue Hwang; Chi-Pei Lin

2004-01-01

458

Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks  

Microsoft Academic Search

Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the

Venu G. Gudise; Ganesh K. Venayagamoorthy

2003-01-01

459

A quickly convergent continuous ant colony optimization algorithm with Scout Ants  

Microsoft Academic Search

Many studies on ants behavior have demonstrated that their food searching process starts with Scout Ants’ scouting all around for food. In this paper, we propose a novel Scout Ant Continuous Optimization (SACO) algorithm which can simulate the food searching process of the Scout Ants. In this algorithm, the solution space of an optimization problem is divided into m subspaces.

Qingbao Zhu; Zhijun Yang; Wei Ma

2011-01-01

460

A Metropolis Algorithm applied to a Nuclear Power Plant Auxiliary Feedwater System surveillance tests policy optimization  

Microsoft Academic Search

The Particle Collision Algorithm (PCA) is a Metropolis-based algorithm loosely inspired by the physics of nuclear particle collision reactions, particularly scattering and absorption. This metaheuristic is applied to a Nuclear Power Plant Auxiliary Feedwater System surveillance tests policy optimization, and its performance is compared to previous results. The optimization problem consists in maximizing the system's average availability for a given

Wagner F. Sacco; Celso M. F. Lapa; Cláudio M. N. A. Pereira; Hermes Alves Filho

2008-01-01

461

Optimal design of SSSC damping controller to improve power system dynamic stability using modified intelligent algorithms  

Microsoft Academic Search

In this paper, A modified intelligent Particle Swarm Optimization (PSO) and continuous Genetic Algorithms (GA) have been used for optimal selection of the static synchronous series compensator (SSSC) damping controller parameters in order to improve power system dynamic response and its stability. Then the performance of these methods on system stability has been compared. First intelligent PSO and genetic algorithms

S. Khani; M. Sadeghi; S. H. Hosseini

2010-01-01

462

Comparison among evolutionary algorithms and classical optimization methods for circuit design problems  

Microsoft Academic Search

This work concerns the comparison of evolu- tionary algorithms and standard optimization methods on two circuit design problems: the parameter extrac- tion of device circuit model and the multi-objective op- timization of an Operational Transconductance Ampli- fier. We compare standard optimization techniques and evolutionary algorithms in terms of quality of the solu- tions and computational effort, that is, objective func-

Angelo Marcello Anile; Vincenzo Cutello; Giuseppe Nicosia; Rosario Rascunŕ; Salvatore Spinella

2005-01-01

463

An effective ant colony optimization-based algorithm for flow shop scheduling  

Microsoft Academic Search

This article presents a modified scheme named local search ant colony optimization algorithm on the basis of alternative ant colony optimization algorithm for solving flow shop scheduling problems. The flow shop problem (FSP) is confirmed to be an NP-hard sequencing scheduling problem, which has been studied by many researchers and applied to plenty of applications. Restated, the flow shop problem

Ruey-Maw Chen; Shih-Tang Lo; Chung-Lun Wu; Tsung-Hung Lin

2008-01-01

464

A new hybrid optimization algorithm for the job-shop scheduling problem  

Microsoft Academic Search

A new hybrid optimization algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling environment. The new algorithm is based on the principle of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs

Xia Weijun; Wu Zhiming; Zhang Wei; Yang Genke

2004-01-01

465

A Particle Swarm Optimization and Differential Evolution Algorithms for Job Shop Scheduling Problem  

Microsoft Academic Search

In this paper, we present particle swarm optimization (PSO) and differential evolution (DE) algorithms for the job shop scheduling problem with the makespan criterion. The applications of PSO and DE on combinatorial optimization problems are still considered limited, but the advantages of PSO and DE algorithms such as structural simplicity, accessibility to practical applications, ease of implementatio n, speed to

M. Fatih Tasgetiren; Mehmet Sevkli; Yun-Chia Liang; M. Mutlu Yenisey

2006-01-01

466

An Algorithm to Select the Optimal Program Based on Rough Sets and Fuzzy Soft Sets  

PubMed Central

Combining rough sets and fuzzy soft sets, we propose an algorithm to obtain the optimal decision program. In this algorithm, firstly, according to fuzzy soft sets, we build up information systems; secondly, we compute the significance of each parameter according to rough set theory; thirdly, combining subjective bias, we give an algorithm to obtain the comprehensive weight of each parameter; at last, we put forward a method to choose the optimal program. Example shows that the optimal algorithm is effective and rational. PMID:25243212

Wenjun, Liu; Qingguo, Li

2014-01-01

467

arXiv:1302.2349v1[math.OC]10Feb2013 Dual subgradient algorithms for large-scale nonsmooth learning  

E-print Network

(FO) algorithms of convex optimization, such as Mirror Descent algorithm or Nesterov's optimal is a nonempty closed and bounded subset of Euclidean space Ex, and f is concave and Lipschitz continuous Grenoble Cedex 9, France, Anatoli.Juditsky@imag.fr Georgia Institute of Technology, Atlanta, Georgia 30332

Nemirovski, Arkadi

468

Biogeography-Based Optimization for Different Economic Load Dispatch Problems  

Microsoft Academic Search

This paper presents a biogeography-based optimization (BBO) algorithm to solve both convex and non-convex economic load dispatch (ELD) problems of thermal plants. The proposed methodology can take care of economic dispatch problems involving constraints such as transmission losses, ramp rate limits, valve point loading, multi-fuel options and prohibited operating zones. Biogeography deals with the geographical distribution of biological species. Mathematical

Aniruddha Bhattacharya; Pranab Kumar Chattopadhyay

2010-01-01

469

New evolutionary genetic algorithms for NP-complete combinatorial optimization problems  

Microsoft Academic Search

Evolutionary genetic algorithms have been proposed to solve NP-complete combinatorial optimization problems. A new crossover operator based on group theory has been created. Computational processes motivated by proposed evolutionary genetic algorithms were described as stochastic processes, using population dynamics and interactive markovian chains. The proposed algorithms were used in solving flowshop problems and an asymmetric traveling salesman problem. The experimental

Fam Quang Bac; V. L. Perov

1993-01-01

470

Greedy Algorithms In dynamic programming, the optimal solution is described in a recursive manner,  

E-print Network

CHAPTER 16 Greedy Algorithms · In dynamic programming, the optimal solution is described an alternative design technique, called greedy algorithms. · This method typically leads to simpler and faster algorithms, but it is not as powerful or as widely applicable as dynamic programming. · The greedy concept

Dragan, Feodor F.

471

Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement Philipp Schuetz and Amedeo Caflisch  

E-print Network

Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement Philipp.g., the modularity, have been proposed. We present here a multistep extension of the greedy algorithm MSG that allows not alter the scaling of computational cost of the greedy algorithm. DOI: 10.1103/PhysRevE.77.046112 PACS

Caflisch, Amedeo

472

Synergy of evolutionary algorithm and socio-political process for global optimization  

Microsoft Academic Search

This paper proposes a hybrid approach by combining the evolutionary optimization based genetic algorithm (GA) and socio-political process based colonial competitive algorithm (CCA). The performance of hybrid algorithm is illustrated using standard test functions in comparison to basic CCA method. Since the CCA method is newly developed, very little research work has been undertaken to deal with curse of dimensionality

Tushar Jain; M. J. Nigam

2010-01-01

473

DIAGNOSIS OF WIRING NETWORKS: AN OPTIMAL RANDOMIZED ALGORITHM FOR FINDING CONNECTED  

E-print Network

DIAGNOSIS OF WIRING NETWORKS: AN OPTIMAL RANDOMIZED ALGORITHM FOR FINDING CONNECTED COMPONENTS as interconnect diagnosis of wiring networks in VLSI. We present a deterministic algorithm using O(minfk; lg ng algorithm, lower bound, fault diagnosis, graph, component, connection class. AMS subject classifications. 68

West, Douglas B.

474

Optimal water-filling algorithms for a Gaussian multiaccess channel with intersymbol interference  

Microsoft Academic Search

This paper presents two novel and efficient water-filling algorithms for a two-user Gaussian multiaccess channel with intersymbol interference. These algorithms efficiently compute the optimal transmit power spectral density (PSD) for each user and obtain the capacity region of the channel. One algorithm is developed for the special case where two users have the same priorities and is more efficient than

Chaohuang Zeng; L. M. C. Hoo; J. M. Cioffi

2001-01-01

475

Genetic\\/quadratic search algorithm for plant economic optimizations using a process simulator  

Microsoft Academic Search

The genetic\\/quadratic search algorithm (GQSA) is a hybrid genetic algorithms (GA) for optimizing plant economics when a process simulator models the plant. By coupling a regular GA with an algorithm based upon a quadratic search, the required number of objective function evaluations for obtaining an acceptable solution decreases significantly in most cases. The GQSA combines advantages of GA and quadratic

Won-hyouk Jang; Juergen Hahn; Kenneth R. Hall

2005-01-01

476

A hybrid of genetic algorithm and particle swarm optimization for recurrent network design  

Microsoft Academic Search

An evolutionary recurrent network which automates the design of recurrent neural\\/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover

Chia-feng Juang

2004-01-01

477

Discrete bat algorithm for optimal problem of permutation flow shop scheduling.  

PubMed

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem. PMID:25243220

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

478

Convex Formulations of Learning from Crowds  

NASA Astrophysics Data System (ADS)

It has attracted considerable attention to use crowdsourcing services to collect a large amount of labeled data for machine learning, since crowdsourcing services allow one to ask the general public to label data at very low cost through the Internet. The use of crowdsourcing has introduced a new challenge in machine learning, that is, coping with low quality of crowd-generated data. There have been many recent attempts to address the quality problem of multiple labelers, however, there are two serious drawbacks in the existing approaches, that are, (i) non-convexity and (ii) task homogeneity. Most of the existing methods consider true labels as latent variables, which results in non-convex optimization problems. Also, the existing models assume only single homogeneous tasks, while in realistic situations, clients can offer multiple tasks to crowds and crowd workers can work on different tasks in parallel. In this paper, we propose a convex optimization formulation of learning from crowds by introducing personal models of individual crowds without estimating true labels. We further extend the proposed model to multi-task learning based on the resemblance between the proposed formulation and that for an existing multi-task learning model. We also devise efficient iterative methods for solving the convex optimization problems by exploiting conditional independence structures in multiple classifiers.

Kajino, Hiroshi; Kashima, Hisashi

479

Optimum design of antennas using metamaterials with the efficient global optimization (EGO) algorithm  

NASA Astrophysics Data System (ADS)

EGO is an evolutionary, data-adaptive algorithm which can be useful for optimization problems with expensive cost functions. Many antenna design problems qualify since complex computational electromagnetics (CEM) simulations can take significant resources. This makes evolutionary algorithms such as genetic algorithms (GA) or particle swarm optimization (PSO) problematic since iterations of large populations are required. In this paper we discuss multiparameter optimization of a wideband, single-element antenna over a metamaterial ground plane and the interfacing of EGO (optimization) with a full-wave CEM simulation (cost function evaluation).

Southall, Hugh L.; O'Donnell, Teresa H.; Derov, John S.

2010-04-01

480

Multiple shooting algorithms for jump-discontinuous problems in optimal control and estimation  

NASA Technical Reports Server (NTRS)

Multiple shooting algorithms are developed for jump-discontinuous two-point boundary value problems arising in optimal control and optimal estimation. Examples illustrating the origin of such problems are given to motivate the development of the solution algorithms. The algorithms convert the necessary conditions, consisting of differential equations and transversality conditions, into algebraic equations. The solution of the algebraic equations provides exact solutions for linear problems. The existence and uniqueness of the solution are proved.

Mook, D. J.; Lew, Jiann-Shiun

1991-01-01

481

Convex Quantum Logic  

E-print Network

In this work we study the convex set of quantum states from a quantum logical point of view. We consider an algebraic structure based on the convex subsets of this set. The relationship of this algebraic structure with the lattice of propositions of quantum logic is shown. This new structure is suitable for the study of compound systems and shows new differences between quantum and classical mechanics. This differences are linked to the nontrivial correlations which appear when quantum systems interact. They are reflected in the new propositional structure, and do not have a classical analogue. This approach is also suitable for an algebraic characterization of entanglement.

F. Holik; C. Massri; N. Ciancaglini

2010-08-24

482

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.  

PubMed

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

483

An inexact ?1 penalty SQP algorithm for PDE-constrained optimization with an application to shape optimization in linear elasticity  

Microsoft Academic Search

We develop an optimization algorithm which is able to deal with inexact evaluations of the objective function. The proposed algorithm employs sequential quadratic programming with a line search that uses the ?1 penalty function for an Armijo-like condition. Both the objective gradient computations for the quadratic subproblems and the objective function computations for the line search admit some inexactness which

Wolfgang Hess; Stefan Ulbrich

2012-01-01

484

A Novel Multi-objective Optimization Algorithm Based on Artificial Immune System  

Microsoft Academic Search

The traditional evolutionary algorithm (EA) for solving the multi-objective optimization problem (MOP) is difficult to accelerate convergence and keep the diversity of the achieved Pareto optimal solutions. A novel EA, i.e., immune multi-objective optimization algorithm (IMOA), is proposed to solve the MOP in this paper. The special evolutional mechanism of the artificial immune system (AIS) prevents the prematurity and quickens

Chun-hua Li; Xin-jan Zhu; Wan-qi Hu; Guang-yi Cao

2009-01-01

485

Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine  

PubMed Central

By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency. PMID:24294135

Yu, Zhang; Yang, Xiaomei

2013-01-01

486

The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances  

Microsoft Academic Search

The field of ACO algorithms is very lively, as testified, for example, by the successful biannual workshop (ANTS—From Ant Colonies to Artificial Ants: A Series of International Workshops on Ant Algorithms; http:\\/\\/iridia.ulb.ac.be\\/~ants\\/) where researchers meet to discuss the properties of ACO and other ant algorithms, both theoretically and experimentally.

Marco Dorigo; Thomas Stützle

487

Seizure warning algorithm based on optimization and nonlinear dynamics  

Microsoft Academic Search

There is growing evidence that temporal lobe seizures are preceded by a preictal transition, characterized by a gradual dynamical change from asymptomatic interictal state to seizure. We herein report the first prospective analysis of the online automated algorithm for detecting the preictal transition in ongoing EEG signals. Such, the algorithm constitutes a seizure warning system. The algorithm estimates STL max,

Panos M. Pardalos; Wanpracha Art Chaovalitwongse; Leonidas D. Iasemidis; J. Chris Sackellares; Deng-shan Shiau; Paul R. Carney; Oleg A. Prokopyev; Vitaliy A. Yatsenko

2004-01-01

488

Algorithm Optimization and Mask Data Generating for Dammann Gratings in Laser Medical Applications  

Microsoft Academic Search

We report our researching work on algorithm design and software development for Dammann gratings design and fabrication for CO2 laser medical applications. The algorithm consists of two parts: elements surface-relief optimization and mask data generating. In the first part, Simulated Annealing Algorithm is adopted and some acceleration codes are imbedded into the algorithm for fast-calculation consideration, and the detailed design

Guoxing Zheng; Song Li; Ping'an He; Hui Zhou; Jinling Yang; Junling Gao

2010-01-01

489

Design of application specific long period waveguide grating filters using adaptive particle swarm optimization algorithms  

NASA Astrophysics Data System (ADS)

We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra.

Semwal, Girish; Rastogi, Vipul

2014-01-01

490

Non-algorithmic stress optimization using simulation for DRAMs  

Microsoft Academic Search

Stress optimization for memory devices is a complex process due to the continuous space of possible optimization values for relevant parameters. This paper uses a method based on electrical Spice simulation to perform this optimization process for DRAM devices. The paper presents a case-study performed in Qimonda to optimize the timing and temperature stresses for the strap problem in defective

Z. Al-Ars; S. Hamdioui

2009-01-01

491

Behavioral analysis of genetic algorithm for function optimization  

Microsoft Academic Search

Function optimization is the process of finding absolutely best values of the variables so that value of an objective function becomes optimal. Many optimization techniques are available but if they perform well on one class of problems then they may not work at all on other classes of problems. Moreover function optimization problems are a class of NP-complete problems so

T. P. Patalia; G. R. Kulkarni

2010-01-01

492

A Multistrategy Optimization Improved Artificial Bee Colony Algorithm  

PubMed Central

Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster. PMID:24982924

Liu, Wen

2014-01-01

493

A multistrategy optimization improved artificial bee colony algorithm.  

PubMed

Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster. PMID:24982924

Liu, Wen

2014-01-01

494

New Optimization Heuristics The Great Deluge Algorithm and the Record-to-Record Travel  

Microsoft Academic Search

In a former paper we introduced a very effective new general purpose optimization principle. We compared this method, which we called threshold accepting (TA), with the well-known simulated annealing (SA) method for discrete optimization. The empirical results demonstrated the superiority of the TA algorithm. In further experiments with the TA principle we discovered two new powerful optimization heuristics: The great

Gunter Dueck

1993-01-01

495