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1

An Optimal Algorithm for Roundness Determination on Convex Polygons  

Microsoft Academic Search

In tolerancing, the Out-Of-Roundness factor determines the relativecircularity of planar shapes. The measurement of concern in this workis the Minimum Radial Separation, as recommended by the AmericanNational Standards Institute (ANSI). Here we show that the algorithmgiven in Le and Lee [4] runs in \\\\Theta(n2) time even for convex polygons.Furthermore, we present an optimal O(n) time algorithm to computethe Minimum Radial

Kurt Swanson; D. T. Lee; Vanban L. Wu

1995-01-01

2

An optimal algorithm for computing the minimum vertex distance between two crossing convex polygons  

Microsoft Academic Search

LetP={p1,p2, ...,pm} andQ={q1,q2, ...,qn} be two intersecting convex polygons whose vertices are specified by their cartesian coordinates in order. An optimalO(m+n) algorithm is presented for computing the minimum euclidean distance betweena vertexpi inP and a vertexqj inQ.

G. T. Toussaint

1984-01-01

3

An optimal algorithm for solving collision distance between convex polygons in plane  

Microsoft Academic Search

In this paper, we study the problem of calculating the minimum collision distance between two planar convex polygons when\\u000a one of them moves to another along a given direction. First, several novel concepts and properties are explored, then an optimal\\u000a algorithm OPFIV with time complexityO (log (n+m)) is developed and its correctness and optimization are proved rigorously.

Yong Yan

1993-01-01

4

An Optimal Algorithm for Computing the Minimum Vertex Distance Between Two Crossing Convex Polygons  

Microsoft Academic Search

Let P = {p1, p2,..., pm} and Q = {q1, q2,..., qn} be two intersecting polygons whosevertices are specified by their cartesian coordinates in order. An optimal O(m+n) algorithmis presented for computing the minimum euclidean distance between a vertexpiin P and a vertex qjin Q.Key words: Algorithms, complexity, computational geometry, convex polygons, minimumdistance, Voronoi diagrams.1. IntroductionLet P = {p 1

Godfried Toussaint

1984-01-01

5

An Optimal Algorithm for Roundness Determination on Convex Polygons  

Microsoft Academic Search

In tolerancing, the Out-Of-Roundness factor determines the relative circularity of planar shapes. The measurement of concern in this work is the Minimum Radial Separation, as recommended by the American National Standards Institute (ANSI). Here presented is a further clarification of the complexity of a previously presented algorithm of Van-Ban Le and D. T. Lee to determine the Minimum Radial Separation

Kurt Swanson

1993-01-01

6

Convex Hull Algorithms  

NSDL National Science Digital Library

An applet that demonstrates some algorithms for computing the convex hull of points in three dimensions. See the points from different viewpoints; see how the Incremental algorithm constructs the hull, face by face; while it's playing, look at it from different directions; see how the gift-wrapping or divide-and-conquer algorithms construct the hull; look at animations of Delaunay triangulation algorithms.

Lambert, Tim

2007-08-09

7

Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms With Directed Gossip Communication  

NASA Astrophysics Data System (ADS)

We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x=x^\\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l_1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.

Jakovetic, Dusan; Xavier, João; Moura, José M. F.

2011-08-01

8

Optimal Algorithm for Solving Collision Distance between Convex Polygons in Plane.  

National Technical Information Service (NTIS)

In this paper, we study the problem of calculating the minimum collision distance between two planar convex polygons when one of them moves to another along a given direction. First, several novel concepts and properties are explored, then an optimal algo...

Y. Yan

1994-01-01

9

Solving minimum distance problems with convex or concave bodies using combinatorial global optimization algorithms  

Microsoft Academic Search

Determining the minimum distance between convex objects is a problem that has been solved using many difierent approaches. On the other hand, computing the minimum distance between combinations of convex and con- cave objects is known to be a more complicated problem. Most methods propose to partition the concave object into convex sub-objects and then solve the convex problem be-

Juan A. Carretero; Meyer A. Nahon

2005-01-01

10

An algorithm for linearizing convex extremal problems  

NASA Astrophysics Data System (ADS)

This paper suggests a method of approximating the solution of minimization problems for convex functions of several variables under convex constraints is suggested. The main idea of this approach is the approximation of a convex function by a piecewise linear function, which results in replacing the problem of convex programming by a linear programming problem. To carry out such an approximation, the epigraph of a convex function is approximated by the projection of a polytope of greater dimension. In the first part of the paper, the problem is considered for functions of one variable. In this case, an algorithm for approximating the epigraph of a convex function by a polygon is presented, it is shown that this algorithm is optimal with respect to the number of vertices of the polygon, and exact bounds for this number are obtained. After this, using an induction procedure, the algorithm is generalized to certain classes of functions of several variables. Applying the suggested method, polynomial algorithms for an approximate calculation of the L_p-norm of a matrix and of the minimum of the entropy function on a polytope are obtained. Bibliography: 19 titles.

Gorskaya, Elena S.

2010-06-01

11

An algorithm for linearizing convex extremal problems  

SciTech Connect

This paper suggests a method of approximating the solution of minimization problems for convex functions of several variables under convex constraints is suggested. The main idea of this approach is the approximation of a convex function by a piecewise linear function, which results in replacing the problem of convex programming by a linear programming problem. To carry out such an approximation, the epigraph of a convex function is approximated by the projection of a polytope of greater dimension. In the first part of the paper, the problem is considered for functions of one variable. In this case, an algorithm for approximating the epigraph of a convex function by a polygon is presented, it is shown that this algorithm is optimal with respect to the number of vertices of the polygon, and exact bounds for this number are obtained. After this, using an induction procedure, the algorithm is generalized to certain classes of functions of several variables. Applying the suggested method, polynomial algorithms for an approximate calculation of the L{sub p}-norm of a matrix and of the minimum of the entropy function on a polytope are obtained. Bibliography: 19 titles.

Gorskaya, Elena S [M. V. Lomonosov Moscow State University, Faculty of Mechanics and Mathematics, Moscow (Russian Federation)

2010-06-09

12

Phase retrieval, error reduction algorithm, and Fienup variants: a view from convex optimization  

Microsoft Academic Search

The phase retrieval problem is of paramount importance in various areas of applied physics and engineering. The state of the art for solving this problem in two dimensions relies heav- ily on the pioneering work of Gerchberg, Saxton, and Fienup. Despite the widespread use of the algorithms proposed by these three researchers, current mathematical theory cannot ex- plain their remarkable

Heinz H. Bauschke; Patrick L. Combettes; D. Russell Luke

2002-01-01

13

Sequential optimality conditions for composed convex optimization problems  

Microsoft Academic Search

Using a general approach which provides sequential optimality conditions for a general convex optimization problem, we derive necessary and sufficient optimality conditions for composed convex optimization problems. Further, we give sequential characterizations for a subgradient of the precomposition of a K-increasing lower semicontinuous convex function with a K-convex and K-epi-closed (continuous) function, where K is a nonempty convex cone. We

Radu Ioan Bo?; Ernö Robert Csetnek; Gert Wanka

2008-01-01

14

An extension of Karmarkar's projective algorithm for convex quadratic programming  

Microsoft Academic Search

We present an extension of Karmarkar's linear programming algorithm for solving a more general group of optimization problems: convex quadratic programs. This extension is based on the iterated application of the objective augmentation and the projective transformation, followed by optimization over an inscribing ellipsoid centered at the current solution. It creates a sequence of interior feasible points that converge to

Yinyu Ye; Edison Tse

1989-01-01

15

Second-order global optimality conditions for convex composite optimization  

Microsoft Academic Search

In recent years second-order sufficient conditions of an isolated local minimizer for convex composite optimization problems have been established. In this paper, second-order optimality conditions are obtained of aglobal minimizer for convex composite problems with a non-finite valued convex function and a twice strictly differentiable function by introducing a generalized representation condition. This result is applied to a minimization problem

Xiaoqi Yang

1998-01-01

16

Solution of non-convex economic dispatch problem considering valve loading effect by a new Modified Differential Evolution algorithm  

Microsoft Academic Search

This paper presents Economic Dispatch (ED) solution considering valve loading effect by a new Modified Differential Evolution (MDE) algorithm. Considering valve loading effect changes ED into a non-convex optimization problem. This non-convexity challenges analytical and heuristic methods in finding optimal solution in reasonable time. Differential Evolution (DE) is one of evolutionary algorithms, which has been used in many optimization problems

Nima Amjady; Hossein Sharifzadeh

2010-01-01

17

On perturbed steepest descent methods with inexact line search for bilevel convex optimization  

Microsoft Academic Search

We use a general framework for solving convex constrained optimization problems introduced in an earlier work to obtain algorithms for problems with a constraint set defined as the set of minimizers of a given function. Also, the algorithms allow the objective function to be decomposed as a sum of other convex functions that can be treated separately. We prove that

Elias Salomão Helou Neto; Álvaro Rodolfo De Pierro

2010-01-01

18

On perturbed steepest descent methods with inexact line search for bilevel convex optimization  

Microsoft Academic Search

We use a general framework for solving convex constrained optimization problems introduced in an earlier work to obtain algorithms for problems with a constraint set defined as the set of minimizers of a given function. Also, the algorithms allow the objective function to be decomposed as a sum of other convex functions that can be treated separately. We prove that

Elias Salomão Helou Neto; Álvaro Rodolfo De Pierro

2011-01-01

19

MIMO control system design for aircraft via convex optimization  

Microsoft Academic Search

This paper shows how convex optimization may be used to solve control system design problems for multip~e-input multip]e.output( ~~~~) linear time invariant(~~l) finite di. mensional plants. The Youla parameterization is used to convex) convex optimization ideas presented in 171. In this work, we are primarily concerned with 'H\\

Oguzhan Cifdaloz; Marco Shayeb; R. Metzger; Yu-Lung Yi; Armando A. Rodriguez

2003-01-01

20

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

21

Qhull: Quickhull algorithm for computing the convex hull  

NASA Astrophysics Data System (ADS)

Qhull computes the convex hull, Delaunay triangulation, Voronoi diagram, halfspace intersection about a point, furthest-site Delaunay triangulation, and furthest-site Voronoi diagram. The source code runs in 2-d, 3-d, 4-d, and higher dimensions. Qhull implements the Quickhull algorithm for computing the convex hull. It handles roundoff errors from floating point arithmetic. It computes volumes, surface areas, and approximations to the convex hull.

Barber, C. Bradford; Dobkin, David P.; Huhdanpaa, Hannu

2013-04-01

22

Parallel algorithms for separation of two sets of points and recognition of digital convex polygons  

SciTech Connect

Given two finite sets of points in a plane, the polygon separation problem is to construct a separating convex k-gon with smallest k. In this paper, we present a parallel algorithm for the polygon separation problem. The algorithm runs in O(log n) time on a CREW PRAM with n processors, where n is the number of points in the two given sets. The algorithm is cost-optimal, since [Omega](n log n) is a lower-bound for the first time needed by any sequential algorithm. We apply this algorithm to the problem of finding a convex region is its digital image. The algorithm in this paper constructs one such polygon with possibly two more edges than the minimal one.

Sarkar, D. (Univ. of Miami, Coral Gables, FL (United States)); Stojmenovic, I. (Univ. of Ottawa, Ontario (Canada))

1992-04-01

23

Approximation Algorithms for the Minimum Convex Partition Problem  

Microsoft Academic Search

We present two algorithms that compute constant factor approximations of a minimum convex partition of a set P of n points in the plane. The first algorithm is very simple and computes a 3-approximation in O(n logn) time. The second algorithm improves the approximation factor to \\u000a\\u000a\\u000a\\u000a\\u000a\\u000a\\\\frac3011 \\\\frac{30}{11} but it is more complex and a straight forward implementation will run

Christian Knauer; Andreas Spillner

2006-01-01

24

An Algorithm on Collision Detection by Computing the Minimum Distance between Two Convex Polyhedra  

Microsoft Academic Search

The problem of tracking the distance between two convex polyhedra is finding applications in many areas of robotics, including intersection detection, collision detection, and path planning. An algorithm for computing the minimum distance between two convex polyhedra is presented. The algorithm applies to polyhedral objects that can be represented as convex hulls of its vertex value in three-dimensional space. Nonlinear

Hanjun Jin; Yanlin Wang; Xiaorong Wang; Jia Fu

2006-01-01

25

Block Clustering Based on Difference of Convex Functions (DC) Programming and DC Algorithms.  

PubMed

We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM. PMID:23777526

Le, Hoai Minh; Le Thi, Hoai An; Dinh, Tao Pham; Huynh, Van Ngai

2013-06-18

26

The utility of convex optimization for weight selection in arrays  

Microsoft Academic Search

In this paper, the versatility and usefulness of convex optimization is illustrated. A sidelobe-minimizing weighting method will be presented that returns optimal weights given a set of specifications, including: scan direction, beamwidth, array geometry, set of null constraints and smoothness criteria. The method also takes into account the radiation patterns of the elements, which do not have to be identical.

Peter J. Bevelacqua

2009-01-01

27

Application of convex optimization to acoustical array signal processing  

NASA Astrophysics Data System (ADS)

This paper demonstrates that optimum weighting coefficients and inverse filters for microphone arrays can be accomplished, with the aid of a systematic methodology of mathematical programming. Both far-field and near-field array problems are formulated in terms of convex optimization formalism. Three application examples, including data-independent far-field array design, nearfield array design, and pressure field interpolation, are presented. In far-field array design, array coefficients are optimized to tradeoff Directivity Index for White Noise Gain or the coefficient norm, while in nearfield array convex optimization is applied to design Equivalent Source Method-based Nearfield Acoustical Holography. Numerical examples are given for designing a far-field two-dimensional random array comprised of thirty microphones. For far-field arrays, five design approaches, including a Delay-And-Sum beamformer, a Super Directivity Array, three optimal arrays designed using ?1,?2,and??-norms, are compared. Numerical and experimental results have shown that sufficiently high White Noise Gain was crucial to robust performance of array against sensor mismatch and noise. For nearfield arrays, inverse filters were designed in light of Equivalent Source Method and convex optimization to reconstruct the velocity field on a baffled spherical piston source. The proposed nearfield design is benchmarked by those designed using Truncated Singular Value Decomposition and Tikhonov Regularization. Compressive Sampling and convex optimization is applied to pressure field reconstruction, source separation and modal analysis with satisfactory performance in both near-field and far-field microphone arrays.

Bai, Mingsian R.; Chen, Ching-Cheng

2013-12-01

28

Optimal placement of convex polygons to maximize point containment  

Microsoft Academic Search

Given a convex polygon P with m vertices and a set S ofn points in the plane, we consider the problem of findinga placement of P that contains the maximum number ofpoints in S. We allow both translation and rotation.Our algorithm is self-contained and utilizes the geometricproperties of the containing regions in the parameter space oftransformations. The algorithm requires O(nk2m2log(mk))time

Matthew Dickerson; Daniel Scharstein

1996-01-01

29

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

30

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

31

Convex Optimization for Nonrigid Stereo Reconstruction  

Microsoft Academic Search

We present a method for recovering 3-D nonrigid structure from an image pair taken with a stereo rig. More specifically, we dedicate to recover shapes of nearly inextensible deformable surfaces. In our approach, we represent the surface as a 3-D triangulated mesh and formulate the reconstruction problem as an optimization problem consisting of data terms and shape terms. The data

Shuhan Shen; Wenjuan Ma; Wenhuan Shi; Yuncai Liu

2010-01-01

32

A complementarity constraint formulation of convex multiobjective optimization problems.  

SciTech Connect

We propose a new approach to convex nonlinear multiobjective optimization that captures the geometry of the Pareto set by generating a discrete set of Pareto points optimally. We show that the problem of finding a maximally uniform representation of the Pareto surface can be formulated as a mathematical program with complementarity constraints. The complementarity constraints arise from modeling the set of Pareto points, and the objective maximizes some quality measure of this discrete set. We present encouraging numerical experience on a range of test problems collected from the literature.

Leyffer, S.; Mathematics and Computer Science

2009-04-01

33

Rank-Constrained Schur-Convex Optimization With Multiple Trace/Log-Det Constraints  

NASA Astrophysics Data System (ADS)

Rank-constrained optimization problems have received an increasing intensity of interest recently, because many optimization problems in communications and signal processing applications can be cast into a rank-constrained optimization problem. However, due to the non-convex nature of rank constraints, a systematic solution to general rank-constrained problems has remained open for a long time. In this paper, we focus on a rank-constrained optimization problem with a Schur-convex/concave objective function and multiple trace/logdeterminant constraints. We first derive a structural result on the optimal solution of the rank-constrained problem using majorization theory. Based on the solution structure, we transform the rank-constrained problem into an equivalent problem with a unitary constraint. After that, we derive an iterative projected steepest descent algorithm which converges to a local optimal solution. Furthermore, we shall show that under some special cases, we can derive a closed-form global optimal solution. The numerical results show the superior performance of our proposed technique over the baseline schemes.

Yu, Hao; Lau, Vincent K. N.

2011-01-01

34

A compaction algorithm for non-convex polygons and its application  

Microsoft Academic Search

Given a two dimensional, non-overlapping layout of convex and non-convex polygons, compaction can be thought of as simulating the motion of the polygons as a result of applied “forces.” Compaction can be modeled as a motion of the polygons that reduces the value of some linear functional on their positions. Optimal compaction, planning a motion that finds the global minimum

Zhenyu Li; Victor Milenkovicf

1993-01-01

35

A recurrent neural network for nonlinear continuously differentiable optimization over a compact convex subset.  

PubMed

We propose a general recurrent neural-network (RNN) model for nonlinear optimization over a nonempty compact convex subset which includes the bound subset and spheroid subset as special cases. It is shown that the compact convex subset is a positive invariant and attractive set of the RNN system and that all the network trajectories starting from the compact convex subset converge to the equilibrium set of the RNN system. The above equilibrium set of the RNN system coincides with the optimum set of the minimization problem over the compact convex subset when the objective function is convex. The analysis of these qualitative properties for the RNN model is conducted by employing the properties of the projection operator of Euclidean space onto the general nonempty closed convex subset. A numerical simulation example is also given to illustrate the qualitative properties of the proposed general RNN model for solving an optimization problem over various compact convex subsets. PMID:18249977

Liang, X B

2001-01-01

36

The DC (Difference of Convex Functions) Programming and DCA Revisited with DC Models of Real World Nonconvex Optimization Problems  

Microsoft Academic Search

The DC programming and its DC algorithm (DCA) address the problem of minimizing a function f=g?h (with g,h being lower semicontinuous proper convex functions on R\\u000a \\u000a n\\u000a ) on the whole space. Based on local optimality conditions and DC duality, DCA was successfully applied to a lot of different\\u000a and various nondifferentiable nonconvex optimization problems to which it quite often

Le Thi Hoai An; Pham Dinh Tao

2005-01-01

37

Analysis of the Criteria of Activation-Based Inverse Electrocardiography using Convex Optimization  

PubMed Central

In inverse electrocardiography (ECG), the problem of finding activation times on the heart noninvasively from body surface potentials is typically formulated as a nonlinear least squares optimization problem. Current solutions rely on iterative algorithms which are sensitive to the presence of local minima. As a result, improved initialization approaches for this problem have been of considerable interest. However, in experiments conducted on a subject with Wolff-Parkinson-White syndrome, we have observed that there may be a mismatch between favorable solutions of the optimization problem and solutions with the desired physiological characteristics. In this work, we use a method based on a convex optimization framework to explore the solution space and analyze whether the optimization criteria target their intended objective.

Erem, Burak; van Dam, Peter M.; Brooks, Dana H.

2012-01-01

38

An Approximation Algorithm for MINIMUM CONVEX COVER with Logarithmic Performance Guarantee  

Microsoft Academic Search

The problem Minimum Convex Cover of covering a given polygon with a minimum number of (possibly overlapping) convex poly- gons is known to be NP-hard, even for polygons without holes (3). We propose a polynomial-time approximation algorithm for this problem for polygons with or without holes that achieves an approximation ratio of O(log n), where n is the number of

Stephan Eidenbenz; Peter Widmayer

2001-01-01

39

A practical algorithm for decomposing polygonal domains into convex polygons by diagonals  

Microsoft Academic Search

We present algorithms for decomposing a polygon (with holes) into convex polygons by diagonals. The methods are computationally\\u000a quick, and although the partitions that they produce may not have minimal cardinality they usually have a low number of convex\\u000a pieces. Thus, the methods are suitable for being used when achieving a modest load on the CPU time is more important

José Fernández; Boglárka Tóth; Lázaro Cánovas; Blas Pelegrín

2008-01-01

40

Fireworks Algorithm for Optimization  

Microsoft Academic Search

\\u000a Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed\\u000a for global optimization of complex functions. In the proposed FA, two types of explosion (search) processes are employed,\\u000a and the mechanisms for keeping diversity of sparks are also well designed. In order to demonstrate the validation of the FA,\\u000a a number of experiments were

Ying Tan; Yuanchun Zhu

2010-01-01

41

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

42

Mind the Duality Gap: Logarithmic regret algorithms for online optimization  

Microsoft Academic Search

We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest known logarithmic regret bounds for Follow-The-Leader and for the gradient de- scent algorithm proposed in Hazan et al. (2006). We then show that one can inter- polate between these two extreme cases. In particular, we derive a new algorithm

Shai Shalev-shwartz; Sham M. Kakade

2008-01-01

43

On the principle of smooth fit for some convex optimal control problems  

Microsoft Academic Search

Proceedings of the 8th International Symposium on Mechatronics and its Applications (ISMA12)1, Sharjah, UAE. The purpose of this work is to show that the principle of smooth fit can fail even for convex problems. Using the dynamic programming approach which involves using differential equation methods, we get the value function for a convex optimal control problem. Then this value function

Jesus Pascal

2012-01-01

44

Algorithms for computing the center of area of a convex polygon  

Microsoft Academic Search

The center of area of a convex polygonP is the unique pointp* that maximizes the minimum area overlap betweenP and any halfplane that includesp*. We show thatp* is unique and present two algorithms for its computation. The first is a combinatorial algorithm that runs in timeO (n6 log2n). The second is a “numerical” algorithm that runs in timeO(GK(n+K)) whereK represents

Matthew Díaz; Joseph O'rourke

1994-01-01

45

A new fast algorithm for computing the distance between two disjoint convex polygons based on Voronoi diagram  

Microsoft Academic Search

Computing the distance between two convex polygons is often a basic step to the algorithms of collision detection and path planning. Now, the lowest time complexity algorithm takes O(logm+logn) time to compute the minimum distance between two disjoint convex polygons P and Q, where n and m are the number of the polygons' edges respectively. This paper discusses the location

YANG Cheng-lei; QI Meng; MENG Xiang-xu; LI Xue-qing; WANG Jia-ye

2006-01-01

46

A class of rank-two ellipsoid algorithms for convex programming  

Microsoft Academic Search

In this paper, we develop a method for constructing minimum volume ellipsoids containing a wedge-shaped subset of a given\\u000a ellipsoid. This construction yields a class of ellipsoid algorithms for convex programming that use rank-two update formulae.

A. Ech-Cherif; J. G. Ecker

1984-01-01

47

A fast convex optimization approach to segmenting 3D scar tissue from delayed-enhancement cardiac MR images.  

PubMed

We propose a novel multi-region segmentation approach through a partially-ordered ports (POP) model to segment myocardial scar tissue solely from 3D cardiac delayed-enhancement MR images (DE-MRI). The algorithm makes use of prior knowledge of anatomical spatial consistency and employs customized label ordering to constrain the segmentation without prior knowledge of geometric representation. The proposed method eliminates the need for regional constraint segmentations, thus reduces processing time and potential sources of error. We solve the proposed optimization problem by means of convex relaxation and introduce its duality: the hierarchical continuous max-flow (HMF) model, which amounts to an efficient numerical solver to the resulting convex optimization problem. Experiments are performed over ten DE-MRI data sets. The results are compared to a FWHM (full-width at half-maximum) method and the inter- and intra-operator variabilities assessed. PMID:23285608

Rajchl, Martin; Yuan, Jing; White, James A; Nambakhsh, Cyrus; Ukwatta, Eranga; Li, Feng; Stirrat, John; Peters, Terry M

2012-01-01

48

ON CONVEX STABILITY DOMAIN AND OPTIMIZATION OF IIR FILTERS  

Microsoft Academic Search

We discuss descriptions of convex domains containing Schur polynomials, built around a given Schur polynomial. We show that the domain described by a positive realness constraint always contains the domain characterized by Rouche's theorem. We also show how to handle computa- tionally the positive realness condition, using semidefinite programming, in the context of designing stable IIR filters. Two recent methods

Bogdan Dumitrescu

49

Duality gap of the conic convex constrained optimization problems in normed spaces  

Microsoft Academic Search

In this paper, motivated by a result due to Champion [Math. Program.99, 2004], we introduce a property $${\\\\mathcal{D}(y)}$$ for a conic quasi-convex vector-valued function in a general normed space. We prove that this property $${\\\\mathcal{D}(y)}$$ characterizes the zero duality gap for a class of the conic convex constrained optimization problem in the sense that if\\u000a this property is satisfied and

Liqun Ban; Wen Song

2009-01-01

50

Measuring the profile of a convex aspherical surface by solving a bi-objective optimization problem  

NASA Astrophysics Data System (ADS)

This paper describes a method based on bi-objective evolutionary algorithms to obtain the profile of a convex aspherical surface, which is defined by a set of synthetic points placed on an xyz coordinate system. The set of points to be analyzed is constructed considering the sources of measurement error in a coordinate measuring machine (CMM), such as machine, probe, and positioning errors. The proposed method is applied to solve a bi-objective optimization problem by minimizing two objective functions. By minimizing the first objective function the positioning error is removed from the coordinates of each affected point. Once the first goal is achieved, the second objective function is minimized to determine from the resulting data all parameters related to the test surface, such as paraxial radius of curvature, the conic constant and the deformation constants. Hence, this method can obtain the correct surface profile even when the positioning error tends to increase the CMM measurement error in the set of analyzed points. The bi-objective evolutionary algorithm (BEA) was tested against a single-objective evolutionary algorithm, and illustrative numerical examples demonstrate that the BEA performs better.

Jaime Sánchez Escobar, Juan

2012-07-01

51

A recurrent neural network for solving a class of generalized convex optimization problems.  

PubMed

In this paper, we propose a penalty-based recurrent neural network for solving a class of constrained optimization problems with generalized convex objective functions. The model has a simple structure described by using a differential inclusion. It is also applicable for any nonsmooth optimization problem with affine equality and convex inequality constraints, provided that the objective function is regular and pseudoconvex on feasible region of the problem. It is proven herein that the state vector of the proposed neural network globally converges to and stays thereafter in the feasible region in finite time, and converges to the optimal solution set of the problem. PMID:23584134

Hosseini, Alireza; Wang, Jun; Hosseini, S Mohammad

2013-03-25

52

FIR Filter Design via Spectral Factorization and Convex Optimization  

Microsoft Academic Search

udio, spectrum shaping, ... ) upper bounds are convex in h; lower bounds are notMagnitude filter design problem involves magnitude specsClassical example: lowpass filter designlowpass filter with maximum stopband attenuation:521\\/51IS()l variables: h C R (filter coefficients),52 G R (stopband attenuation) parameters: 51 ( R (logarithmic passband ripple), n (order),Op (passband frequency), Os (stopband frequency)magnitude filter design problems are nonconvex can

Lieven Vandenberghe; Shao-po Wu; Stephen Boyd

1997-01-01

53

A new fast algorithm for computing the distance between two disjoint convex polygons based on Voronoi diagram  

Microsoft Academic Search

Computing the distance between two convex polygons is often a basic step to the algorithms of collision detection and path\\u000a planning. Now, the lowest time complexity algorithm takes O(logm+logn) time to compute the minimum distance between two disjoint convex polygons P and Q, where n and m are the number of the polygons’ edges respectively. This paper discusses the location

Cheng-lei Yang; Meng Qi; Xiang-xu Meng; Xue-qing Li; Jia-ye Wang

2006-01-01

54

Calculating and controlling the error of discrete representations of Pareto surfaces in convex multi-criteria optimization  

PubMed Central

A discrete set of points and their convex combinations can serve as a sparse representation of the Pareto surface in multiple objective convex optimization. We develop a method to evaluate the quality of such a representation, and show by example that in multiple objective radiotherapy planning, the number of Pareto optimal solutions needed to represent Pareto surfaces of up to five dimensions grows at most linearly with the number of objectives. The method described is also applicable to the representation of convex sets.

Craft, David

2009-01-01

55

Genetic algorithms for numerical optimization  

Microsoft Academic Search

Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known. However, such applications can encounter problems that sometimes delay,

Zbigniew Michalewicz; Cezary Z. Janikow

1991-01-01

56

Approximation algorithms for homogeneous polynomial optimization with quadratic constraints  

Microsoft Academic Search

In this paper, we consider approximation algorithms for optimizing a generic multi-variate homogeneous polynomial function, subject to homogeneous quadratic constraints. Such optimiza- tion models have wide applications, e.g., in signal processing, magnetic resonance imaging (MRI), data training, approximation theory, and portfolio selection. Since polynomial functions are non- convex in general, the problems under consideration are all NP-hard. In this paper

Simai He; Zhening Li; Shuzhong Zhang

2010-01-01

57

EVOLUTIONARY ALGORITHMS FOR MULTIOBJECTIVE OPTIMIZATION  

Microsoft Academic Search

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate sub- discipline combining the elds of evolutionary computation

Eckart Zitzler

2002-01-01

58

Reduction of shock induced noise in imperfectly expanded supersonic jets using convex optimization  

NASA Astrophysics Data System (ADS)

Imperfectly expanded jets generate screech noise. The imbalance between the backpressure and the exit pressure of the imperfectly expanded jets produce shock cells and expansion or compression waves from the nozzle. The instability waves and the shock cells interact to generate the screech sound. The mathematical model consists of cylindrical coordinate based full Navier-Stokes equations and large-eddy-simulation turbulence modeling. Analytical and computational analysis of the three-dimensional helical effects provide a model that relates several parameters with shock cell patterns, screech frequency and distribution of shock generation locations. Convex optimization techniques minimize the shock cell patterns and the instability waves. The objective functions are (convex) quadratic and the constraint functions are affine. In the quadratic optimization programs, minimization of the quadratic functions over a set of polyhedrons provides the optimal result. Various industry standard methods like regression analysis, distance between polyhedra, bounding variance, Markowitz optimization, and second order cone programming is used for Quadratic Optimization.

Adhikari, Sam

2007-11-01

59

Convex optimization theory applied to joint beamforming design in multicarrier MIMO channels  

Microsoft Academic Search

This paper addresses the joint design of transmit and receive beamvectors for a multicarrier MIMO channel within the general and powerful framework of convex optimization theory. From this perspective, a great span of design criteria can be easily accommodated and efficiently solved even though closed-form expressions may not be available. Among other criteria, we consider the minimization of the average

Daniel Perez Palomar; John M. Cioffi; Miguel Angel Lagunas; Antonio Pascual Iserte

2003-01-01

60

Algorithm for calculating the area of overlap of an ellipse and a convex polygon. Research paper, October-November 1987  

SciTech Connect

This paper describes a new and improved algorithm for estimating in computer simulations the area of overlap of an ellipse and a convex polygon. The need for such algorithms arises frequently in military operations analysis, in particular in estimating the portion of a rectangular target overlapped by a disk-shaped nuclear coverage area. A detailed description of the algorithm and illustrations of its application are included.

Helmbold, R.L.

1987-11-01

61

Mixed H2\\/H? control: a convex optimization approach  

Microsoft Academic Search

The problem of finding an internally stabilizing controller that minimizes a mixed H2\\/H? performance measure subject to an inequality constraint on the H? norm of another closed-loop transfer function is considered. This problem can be interpreted and motivated as a problem of optimal nominal performance subject to a robust stability constraint. Both the state-feedback and output-feedback problems are considered. It

Pramod P. Khargonekar; Mario A. Rotea

1991-01-01

62

Convex duality in constrained mean-variance portfolio optimization  

Microsoft Academic Search

We apply conjugate duality to establish the existence\\u000aof optimal portfolios in an asset-allocation\\u000aproblem, with the goal of minimizing\\u000athe variance of the final wealth which results from\\u000atrading over a fixed, finite horizon in a continuous-time, complete market,\\u000asubject to the constraints that the expected final wealth equal a specified\\u000atarget value and the portfolio of the investor

Chantal Labbé; Andrew J. Heunis

2007-01-01

63

About the non-convex optimization problem induced by non-positive semidefinite kernel learning  

Microsoft Academic Search

During the last years, kernel based methods proved to be very successful for many real-world learning problems. One of the\\u000a main reasons for this success is the efficiency on large data sets which is a result of the fact that kernel methods like\\u000a support vector machines (SVM) are based on a convex optimization problem. Solving a new learning problem can

Ingo Mierswa; Katharina Morik

2008-01-01

64

Particle swarm and ant colony algorithms hybridized for improved continuous optimization  

Microsoft Academic Search

This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we explore a

P. S. Shelokar; Patrick Siarry; Valadi K. Jayaraman; Bhaskar D. Kulkarni

2007-01-01

65

An Implementation of a linear time algorithm for computing the minimum perimeter triangle enclosing a convex polygon  

Microsoft Academic Search

In this paper, we discuss an efficient and robust im- plementation of a linear time algorithm due to (1) for computing the minimum perimeter triangle that cir- cumscribes a convex n-gon. Our implementation is in C++, and utilizes the OpenGL graphics library for visu- alization and animation. The proposed implementation is efficient in the sense that it complies with the

Anna Medvedeva; Asish Mukhopadhyay

2003-01-01

66

Gradient vs. approximation design optimization techniques in low-dimensional convex problems  

NASA Astrophysics Data System (ADS)

Design Optimization methods' application in structural designing represents a suitable manner for efficient designs of practical problems. The optimization techniques' implementation into multi-physical softwares permits designers to utilize them in a wide range of engineering problems. These methods are usually based on modified mathematical programming techniques and/or their combinations to improve universality and robustness for various human and technical problems. The presented paper deals with the analysis of optimization methods and tools within the frame of one to three-dimensional strictly convex optimization problems, which represent a component of the Design Optimization module in the Ansys program. The First Order method, based on combination of steepest descent and conjugate gradient method, and Supbproblem Approximation method, which uses approximation of dependent variables' functions, accompanying with facilitation of Random, Sweep, Factorial and Gradient Tools, are analyzed, where in different characteristics of the methods are observed.

Fedorik, Filip

2013-10-01

67

Covering a simple orthogonal polygon with a minimum number of orthogonally convex polygons  

Microsoft Academic Search

The problem of covering a polygon with convex polygons has proven to be very difficult, even when restricted to the class of orthogonal polygons using orthogonally convex covers. We develop a method of analysis based on dent diagrams for orthogonal polygons, and are able to show that Keil's &Ogr;(n2) algorithm for covering horizontally convex polygons is optimal, but can be

Robert A. Reckhow; Joseph C. Culberson

1987-01-01

68

QoS and Fairness Constrained Convex Optimization of Resource Allocation for Wireless Cellular and Ad Hoc Networks  

Microsoft Academic Search

For wireless cellular and ad hoc networks with QoS constraints, we propose a suite of problem formulations that allocate network resources to optimize SIR, maximize throughput and minimize de- lay. The distinguishing characteristics of these resource allocation formulations is that, by using convex optimization, they accommo- date a variety of realistic QoS and fairness constraints. Their glob- ally optimal solutions

David Julian; Mung Chiang; Daniel O'neill; Stephen P. Boyd

2002-01-01

69

Algorithms for optimal redundancy allocation  

SciTech Connect

Heuristic and exact methods for solving the redundancy allocation problem are compared to an approach based on genetic algorithms. The various methods are applied to the bridge problem, which has been used as a benchmark in earlier work on optimization methods. Comparisons are presented in terms of the best configuration found by each method, and the computation effort which was necessary in order to find it.

Vandenkieboom, J.; Youngblood, R.

1993-01-01

70

On the optimality of the neighbor-joining algorithm  

PubMed Central

The popular neighbor-joining (NJ) algorithm used in phylogenetics is a greedy algorithm for finding the balanced minimum evolution (BME) tree associated to a dissimilarity map. From this point of view, NJ is "optimal" when the algorithm outputs the tree which minimizes the balanced minimum evolution criterion. We use the fact that the NJ tree topology and the BME tree topology are determined by polyhedral subdivisions of the spaces of dissimilarity maps R+(n2) to study the optimality of the neighbor-joining algorithm. In particular, we investigate and compare the polyhedral subdivisions for n ? 8. This requires the measurement of volumes of spherical polytopes in high dimension, which we obtain using a combination of Monte Carlo methods and polyhedral algorithms. Our results include a demonstration that highly unrelated trees can be co-optimal in BME reconstruction, and that NJ regions are not convex. We obtain the l2 radius for neighbor-joining for n = 5 and we conjecture that the ability of the neighbor-joining algorithm to recover the BME tree depends on the diameter of the BME tree.

Eickmeyer, Kord; Huggins, Peter; Pachter, Lior; Yoshida, Ruriko

2008-01-01

71

Minimum strictly convex quadrangulations of convex polygons  

Microsoft Academic Search

We present a linear-time algorithm that decomposes a convex poly- gon conformally into a minimum number of strictly convex quadri- laterals. Moreover, we characterize the polygons that can b e de- composed without additional vertices inside the polygon, and we present a linear-time algorithm for such decompositions, t oo. As an application, we consider the problem of constructing a minimum

Matthias Miiller-Hannemannl; Karsten Weihe

1997-01-01

72

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

73

Evolutionary optimization algorithm by entropic sampling  

NASA Astrophysics Data System (ADS)

A combinatorial optimization algorithm, genetic-entropic algorithm, is proposed. This optimization algorithm is based on the genetic algorithms and the natural selection via entropic sampling. With the entropic sampling, this algorithm helps to escape local optima in the complex optimization problems. To test the performance of the algorithm, we adopt the NK model (N is the number of bits in the string and K is the degree of epistasis) and compare the performances of the proposed algorithm with those of the canonical genetic algorithm. It is found that the higher the K value, the better this algorithm can escape local optima and search near global optimum. The characteristics of this algorithm in terms of the power spectrum analysis together with the difference between two algorithms are discussed.

Lee, Chang-Yong; Han, Seung Kee

1998-03-01

74

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

75

Stochastic search for signal processing algorithm optimization  

Microsoft Academic Search

This paper presents an evolutionary algorithm for searching for the optimal implementations of signal transforms and compares this approach against other search techniques. A single signal processing algorithm can be represented by a very large number of different but mathematically equivalent formulas. When these formulas are implemented in actual code, unfortunately their running times differ significantly. Signal processing algorithm optimization

Bryan Singer; Manuela M. Veloso

2001-01-01

76

Evolutionary programming based optimal power flow algorithm  

Microsoft Academic Search

This paper develops an efficient and reliable evolutionary programming algorithm for solving the optimal power flow (OPF) problem. The class of curves used to describe generator performance does not limit the algorithm and the algorithm is also less sensitive to starting points. To improve the speed of convergence of the algorithm as well as its ability to handle larger systems,

Jason Yuryevich; Kit Po Wong

1999-01-01

77

Optimizing RLV ascent trajectory using PSO algorithms  

Microsoft Academic Search

RLV (reusable launch vehicle) ascent-trajectory optimization is a difficult problem due to the complexity of many physical constraints and optimization cost surfaces. In this paper, a heuristic technique based on a PSO (particle swarm optimization) algorithm is used. The highly constrained nonlinear trajectory planning problem is decomposed into control parameters search problems, The algorithm is able to generate a complete

He Chenglong; Chen Xin; Wu Leni

2008-01-01

78

Compaction and separation algorithms for non-convex polygons and their applications  

Microsoft Academic Search

Given a two-dimensional, non-overlapping layout of convex and non-convex polygons, compaction can be thought of as simulating the motion of the polygons resulting from applied ‘forces’. By moving many polygons simultaneously, compaction can improve the material utilization of even tightly packed layouts. Compaction is hard: finding the tightest layout that a valid motion can reach is PSPACE-hard, and even compacting

Zhenyu Li; Victor Milenkovic

1995-01-01

79

Parameter optimization in FCM clustering algorithms  

Microsoft Academic Search

Weighting exponent m is an important parameter in fuzzy c-means (FCM) clustering algorithm, which directly affects the performance of the algorithm and the validity of fuzzy cluster analysis. However, so far the optimal choice of m is still an open problem. A method of selecting the optimal m is proposed in this paper, which is based on the fuzzy decision

Gao Xinbo; Li Jie; Xie Weixin

2000-01-01

80

Development of particle swarm optimization algorithm  

Microsoft Academic Search

Particle swarm optimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm is summarized in several fields, such as parameter selection and design, population topology, hybrid PSO algorithm etc. Finally, some vital applications and aspects that may be conducted in

Yu Chen; Fan Yang; Quan Zou; Chen Lin

2011-01-01

81

An optimal algorithm for finding segments intersections  

Microsoft Academic Search

This paper deals with a new deterministic algorithm for finding intersecting pairs from a given set of N segments in the plane. The algorithm is asymptotically optimal and has time and space complexity O(AJ log N+ K) and 0( IV ) respectively, where K is the number of intersecting pairs. The algorithm may be used for finding intersections not only

Ivan J. Balaban

1995-01-01

82

Sublinear geometric algorithms  

Microsoft Academic Search

We initiate an investigation of sublinear algorithms for geometric problems in two and three dimensions. We give optimal algorithms for intersection detection of convex polygons and polyhedra, point location in two-dimensional Delaunay triangulations and Voronoi diagrams, and ray shooting in convex polyhedra, all of which run in time O(?n), where n is the size of the input. We also provide

Bernard Chazelle; Ding Liu; Avner Magen

2003-01-01

83

A Genetic Algorithm for calculating minimum distance between convex and concave bodies  

Microsoft Academic Search

Distance determination, i.e. obtaining the distance between a pair of objects, is used in dierent appli- cations such as the simulation of physical systems and robot path planning. Most of the existing al- gorithms focus on obtaining the separation distance and are limited to deal only with convex objects. In this work, a novel method for solving the mini- mum

Juan A. Carretero; Meyer A. Nahon

2001-01-01

84

Security-constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators  

Microsoft Academic Search

This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems with considering transmission security and bus voltage constraints for practical application. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic crossover and mutation schemes are proposed to deal

Zwe-Lee Gaing; Rung-Fang Chang

2006-01-01

85

Real-coded mixed-integer genetic algorithm for constrained optimal power flow  

Microsoft Academic Search

This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic mutation schemes are proposed to deaf with continuous\\/discrete control variables, respectively. Simultaneously, because the length of the individual is

Zwe-Lee Gaing; Hou-Sheng Huang

2004-01-01

86

Evaluation of relative entropy of entanglement and derivation of optimal Lewenstein–Sanpera decomposition of bell decomposable states via convex optimization  

Microsoft Academic Search

We provide an analytical expression for optimal Lewenstein–Sanpera decomposition of Bell decomposable states by using semi-definite programming. Also using the Karush–Kuhn–Tucker optimization method, the minimum relative entropy of entanglement of Bell decomposable states has been evaluated and it is shown that the same separable Bell decomposable state lying at the boundary of convex set of separable Bell decomposable states, optimizes

M. A. Jafarizadeh; M. Mirzaee; M. Rezaee

2005-01-01

87

Evaluation of relative entropy of entanglement and derivation of optimal Lewenstein-Sanpera decomposition of Bell decomposable states via convex optimization  

Microsoft Academic Search

We provide an analytical expression for optimal Lewenstein-Sanpera decomposition of Bell decomposable states by using semi-definite programming. Also using the Karush-Kuhn-Tucker optimization method, the minimum relative entropy of entanglement of Bell decomposable states has been evaluated and it is shown that the same separable Bell decomposable state lying at the boundary of convex set of separable Bell decomposable states, optimizes

M. A. Jafarizadeh; M. Mirzaee; M. Rezaee

2006-01-01

88

Probabilistic reliability optimization using hybrid genetic algorithms  

Microsoft Academic Search

In this paper transmission power system structure optimization is performed via a minimal spanning tree based encoded fuzzy logic self-controlled hybrid genetic algorithm (GA). During the redundancy optimization of the power system network a binary encoded GA is used for a modified transmission network expansion problem, finding the optimal power line type with respect to the net present value (NPV)

A. Gaun; G. Rechberger; H. Renner

2010-01-01

89

Dikin-type algorithms for dextrous grasping force optimization  

SciTech Connect

One of the central issues in dextrous robotic hand grasping is to balance external forces acting on the object and at the same time achieve grasp stability and minimum grasping effort. A companion paper shows that the nonlinear friction-force limit constraints on grasping forces are equivalent to the positive definiteness of a certain matrix subject to linear constraints. Further, compensation of the external object force is also a linear constraint on this matrix. Consequently, the task of grasping force optimization can be formulated as a problem with semidefinite constraints. In this paper, two versions of strictly convex cost functions, one of them self-concordant, are considered. These are twice-continuously differentiable functions that tend to infinity at the boundary of possible definiteness. For the general class of such cost functions, Dikin-type algorithms are presented. It is shown that the proposed algorithms guarantee convergence to the unique solution of the semidefinite programming problem associated with dextrous grasping force optimization. Numerical examples demonstrate the simplicity of implementation, the good numerical properties, and the optimality of the approach.

Buss, M. [Technical Univ. of Munich (Germany). Inst. of Automatic Control Engineering; Faybusovich, L. [Univ. of Notre Dame, IN (United States). Dept. of Mathematics; Moore, J.B. [Australian National Univ., Canberra, Australia Capital Territory (Australia)

1998-08-01

90

A Polynomial Time Algorithm for Optimizing Join Queries  

Microsoft Academic Search

The dynamic programming algorithm for query optimization has exponential complexity. An alternative polynomial time algorithm, the IK-KBZ algorithm, is severely limited in the queries it can optimize. Other algorithms have been proposed, including the greedy algorithm, iterative improvement, and simulated annealing. The AB algorithm, which combines randomization and neighborhood search with the IK-KBZ algorithm, is presented. The AB algorithm is

Arun N. Swami; Balakrishna R. Iyer

1993-01-01

91

Efficient Algorithms for Two-Center Problems for a Convex Polygon  

Microsoft Academic Search

Let P be a convex polygon with n vertices. We want to find two congruent disks whose union covers P and whose radius is minimized. We also consider its discrete version with centers restricted to be at vertices of P. Standard and discrete two-center problems are respectively solved in O(n log3\\u000a n log log n) and O(n log2\\u000a n) time.

Sung Kwon Kim; Chan-su Shin

2000-01-01

92

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

93

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

94

Algorithms for Optimizing Hydropower System Operation  

NASA Astrophysics Data System (ADS)

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 period. The LP-DP algorithm is clearly dominated: it takes longer to find a solution and produces significantly less hydropower than the other two procedures. Successive linear programming (SLP) appears to find the global maximum and is easily implemented. For simple systems the optimal control algorithm finds the optimum in about one fifth the time required by SLP but is harder to implement. Computing costs for a two-reservoir, 12-month deterministic problem averaged about seven cents per run using optimal control and 37 cents using successive linear programming.

Grygier, Jan C.; Stedinger, Jery R.

1985-01-01

95

Optimization algorithm for reverse osmosis desalination economics  

Microsoft Academic Search

In this work an optimization algorithm for the calculation of water unit cost from various RO candidate schemes was developed. Such an algorithm may be used for evaluation purposes when many RO candidate schemes are taken into account. The applicability of the method is demonstrated on an example in which six RO candidate schemes are examined.

Andreas Poullikkas

2001-01-01

96

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

97

MULTIOBJECTIVE DESIGN OPTIMIZATION BY AN EVOLUTIONARY ALGORITHM  

Microsoft Academic Search

This paper presents an evolutionary algorithm for generic multiobjective design optimization problems. The algorithm is based on nondominance of solutions in the objective and constraint space and uses effective mating strategies to improve solutions that are weak in either. Since the methodology is based on nondominance, scaling and aggregation affecting conventional penalty function methods for constraint handling does not arise.

TAPABRATA RAY; KANG TAI; KIN CHYE SEOW

2001-01-01

98

A Cellular Genetic Algorithm for Multiobjective Optimization  

Microsoft Academic Search

This paper introduces a new cellular genetic algorithm for solving multiobjective contin- uous optimization problems. Our approach is characterized by using an external archive to store non-dominated solutions and a feedback mechanism in which solutions from this archive randomly replaces existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal

A. J. Nebro; J. J. Durillo; F. Luna; B. Dorronsoro; E. Alba

99

An Organizational Evolutionary Algorithm for Numerical Optimization  

Microsoft Academic Search

Taking inspiration from the interacting process among organizations in human societies, this correspondence designs a kind of structured population and corresponding evolutionary operators to form a novel algorithm, organizational evolutionary algorithm (OEA), for solving both unconstrained and constrained optimization problems. In OEA, a population consists of organizations, and an organization consists of individuals. All evolutionary operators are designed to simulate

Jing Liu; Weicai Zhong; Licheng Jiao

2007-01-01

100

A bacterial swarming algorithm for global optimization  

Microsoft Academic Search

This paper presents a novel bacterial swarming algorithm (BSA) for global optimization. This algorithm is inspired by swarming behaviors of bacteria, in particular, focusing on the study of tumble and run actions which are the major part of the chemotactic process. Adaptive tumble and run operators are developed to improve the global and local search capability of the BSA, based

W. J. Tang; Q. H. Wu; J. R. Saunders

2007-01-01

101

A Simplified Adaptive Particle Swarm Optimization Algorithm  

Microsoft Academic Search

A new particle swarm optimization (PSO) algorithm is presented based on three methods of improvement in original PSO. First, the iteration formula of PSO is changed and simplified by removal of velocity parameter that is unnecessary during the course of evolution. Second, the dynamically decreasing inertia weight is employed to enhance the balance of global and local search of algorithm.

Zhigang Zhao; Cheng Chang

2010-01-01

102

Minimum Strictly Convex Quadrangulations of Convex Polygons  

Microsoft Academic Search

We present a linear--time algorithm that decomposesa convex polygon conformally into a minimumnumber of strictly convex quadrilaterals.Moreover, we characterize the polygons that canbe decomposed without additional vertices insidethe polygon, and we present a linear--time algorithmfor such decompositions, too. As an application,we consider the problem of constructinga minimum conformal refinement of a mesh inthe three--dimensional space, which approximatesthe surface of a

Karsten Weihe; Matthias M Uller-hannemann; Matthias Muller-hannemann

1996-01-01

103

An algorithm for online optimization of accelerators  

NASA Astrophysics Data System (ADS)

A general algorithm is developed for online optimization of accelerator performance, i.e., online tuning, using the performance measure as the objective function. This method, named robust conjugate direction search (RCDS), combines the conjugate direction set approach of Powell's method with a robust line optimizer which considers the random noise in bracketing the minimum and uses parabolic fit of data points that uniformly sample the bracketed zone. It is much more robust against noise than traditional algorithms and is therefore suitable for online application. Simulation and experimental studies have been carried out to demonstrate the strength of the new algorithm.

Huang, Xiaobiao; Corbett, Jeff; Safranek, James; Wu, Juhao

2013-10-01

104

Multiobjective optimization of trusses using genetic algorithms  

Microsoft Academic Search

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical

Carlos A. Coello Coello; Alan D. Christiansen

2000-01-01

105

Optimizing connected component labeling algorithms  

SciTech Connect

This paper presents two new strategies that can be used to greatly improve the speed of connected component labeling algorithms. To assign a label to a new object, most connected component labeling algorithms use a scanning step that examines some of its neighbors. The first strategy exploits the dependencies among them to reduce the number of neighbors examined. When considering 8-connected components in a 2D image, this can reduce the number of neighbors examined from four to one in many cases. The second strategy uses an array to store the equivalence information among the labels. This replaces the pointer based rooted trees used to store the same equivalence information. It reduces the memory required and also produces consecutive final labels. Using an array instead of the pointer based rooted trees speeds up the connected component labeling algorithms by a factor of 5 {approx} 100 in our tests on random binary images.

Wu, Kesheng; Otoo, Ekow; Shoshani, Arie

2005-01-16

106

An Immunological Algorithm for Global Numerical Optimization  

Microsoft Academic Search

\\u000a Numerical optimization of given objective functions is a crucial task in many real-life problems. The present article introduces\\u000a an immunological algorithm for continuous global optimization problems, called opt-IA. Several biologically inspired algorithms have been designed during the last few years and have shown to have very good performance\\u000a on standard test bed for numerical optimization.\\u000a \\u000a \\u000a In this paper we assess

Vincenzo Cutello; Giuseppe Narzisi; Giuseppe Nicosia; Mario Pavone

2005-01-01

107

Optimal algorithms for approximate clustering  

Microsoft Academic Search

In a clustering problem, the aim is to partition a given set of n points in d-dimensional space into k groups, called clusters, so that points within each cluster are near each other. Two objective functions frequently used to measure the performance of a clustering algorithm are, for any L4 metric, (a) the maximum distance between pairs of points in

Tomás Feder; Daniel H. Greene

1988-01-01

108

A forward backward splitting algorithm for the minimization of non-smooth convex functionals in Banach space  

NASA Astrophysics Data System (ADS)

We consider the task of computing an approximate minimizer of the sum of a smooth and a non-smooth convex functional, respectively, in Banach space. Motivated by the classical forward-backward splitting method for the subgradients in Hilbert space, we propose a generalization which involves the iterative solution of simpler subproblems. Descent and convergence properties of this new algorithm are studied. Furthermore, the results are applied to the minimization of Tikhonov-functionals associated with linear inverse problems and semi-norm penalization in Banach spaces. With the help of Bregman-Taylor-distance estimates, rates of convergence for the forward-backward splitting procedure are obtained. Examples which demonstrate the applicability are given, in particular, a generalization of the iterative soft-thresholding method by Daubechies, Defrise and De Mol to Banach spaces as well as total-variation-based image restoration in higher dimensions are presented.

Bredies, Kristian

2009-01-01

109

Two stochastic optimization algorithms applied to nuclear reactor core design  

Microsoft Academic Search

Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck's Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and

Wagner F. Sacco; Cassiano R. E. de oliveira; Cláudio M. N. A. Pereira

2006-01-01

110

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

111

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

112

Improved algorithms for optimal embeddings  

Microsoft Academic Search

In the last decade, the notion of metric embeddings with small distortion received wide attention in the literature, with applications in combinatorial optimization, discrete mathematics and bio-informatics. The notion of embedding is, given two metric spaces on the same number of points, to flnd a bijection that minimizes maximum Lipschitz and bi-Lipschitz constants. One reason for the popularity of the

Nishanth Chandran; Ryan Moriarty; Rafail Ostrovsky; Omkant Pandey; Mohammad Ali Safari; Amit Sahai

2008-01-01

113

The effects of initial conditions and control time on optimal actuator placement via a max-min Genetic Algorithm  

SciTech Connect

This paper examines the role of the control objective and the control time in determining fuel-optimal actuator placement for structural vibration suppression. A general theory is developed that can be easily extended to include alternative performance metrics such as energy and time-optimal control. The performance metric defines a convex admissible control set which leads to a max-min optimization problem expressing optimal location as a function of initial conditions and control time. A solution procedure based on a nested Genetic Algorithm is presented and applied to an example problem. Results indicate that the optimal locations vary widely as a function of control time and initial conditions.

Redmond, J. [Sandia National Labs., Albuquerque, NM (United States); Parker, G. [State Univ. of New York, Buffalo, NY (United States)

1993-07-01

114

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

115

Decomposition and Nondifferentiable Optimization with the Projective Algorithm  

Microsoft Academic Search

This paper deals with an application of a variant of Karmarkar's projective algorithm for linear programming to the solution of a generic nondifferentiable minimization problem. This problem is closely related to the Dantzig-Wolfe decomposition technique used in large-scale convex programming. The proposed method is based on a column generation technique defining a sequence of primal linear programming maximization problems. Associated

J. L. Goffin; A. Haurie; J. P. Vial

1992-01-01

116

An Improved Algorithm for Hydropower Optimization  

NASA Astrophysics Data System (ADS)

A new algorithm named energy management by successive linear programming (EMSLP) was developed to solve the optimization problem of the hydropower system operation. The EMSLP algorithm has two iteration levels: at the first level a stable solution is sought, and at the second the interior of the feasible region is searched to improve the objective function whenever its value decreases. The EMSLP algorithm has been tested using the Manitoba Hydro system data applied to a single reservoir system. To evaluate the performance of the algorithm the comparison has been made with the results obtained by the energy management and maintenance analysis (EMMA) program used in the Manitoba Hydro practice. The paper describes the EMSLP algorithm and presents the results of the comparison with EMMA.

Reznicek, K. K.; Simonovic, S. P.

1990-02-01

117

Optimal power flow using differential evolution algorithm  

Microsoft Academic Search

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The\\u000a proposed approach employs differential evolution algorithm for optimal settings of OPF problem control variables. The proposed\\u000a approach is examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization,\\u000a voltage profile improvement, and voltage stability

A. A. Abou El Ela; M. A. Abido; S. R. Spea

2009-01-01

118

An optimal extraction algorithm for CCD spectroscopy  

NASA Astrophysics Data System (ADS)

An optimal spectrum extraction procedure is described, and examples of its performance with CCD data are presented. The algorithm delivers the maximum possible signal-to-noise ratio while preserving spectrophotometric accuracy. The effects of moderate geometric distortion and of cosmic-ray hits on the spectrum are automatically accounted for. In tests with background-noise limited CCD spectra, optimal extraction offers a 70-percent gain in effective exposure time in comparison with conventional extraction procedures.

Horne, K.

1986-06-01

119

An optimal algorithm for counting network motifs  

NASA Astrophysics Data System (ADS)

Network motifs are small connected sub-graphs occurring at significantly higher frequencies in a given graph compared with random graphs of similar degree distribution. Recently, network motifs have attracted attention as a tool to study networks microscopic details. The commonly used algorithm for counting small-scale motifs is the one developed by Milo et al. This algorithm is extremely costly in CPU time and actually cannot work on large networks, consisting of more than 100,000 edges on current CPUs. We here present a new optimal algorithm, based on network decomposition for counting K-size network motifs with constant memory costs and a CPU cost linear with the number of counted motifs. Our algorithm performs better than previous full enumeration algorithms in terms of running time. Moreover, it uses a constant amount of memory. It also outperforms sampling algorithms. Our algorithm permits the counting of three and four motif for large networks that consists of more than 500,000 nodes and 5,000,000 links. For large networks, it performs more than a thousand times faster than current algorithms.

Itzhack, Royi; Mogilevski, Yelena; Louzoun, Yoram

2007-07-01

120

Genetic algorithms in truss topological optimization  

Microsoft Academic Search

The present paper describes the use of a stochastic search procedure that is the basis of genetic algorithms, in developing near-optimal topologies of load-bearing truss structures. The problem addressed is one wherein the structural geometry is created from a specification of load conditions and available support points in the design space. The development of this geometry must satisfy kinematic stability

P. Hajela; E. Lee

1995-01-01

121

Optimization of a chemical identification algorithm  

NASA Astrophysics Data System (ADS)

A procedure to evaluate and optimize the performance of a chemical identification algorithm is presented. The Joint Contaminated Surface Detector (JCSD) employs Raman spectroscopy to detect and identify surface chemical contamination. JCSD measurements of chemical warfare agents, simulants, toxic industrial chemicals, interferents and bare surface backgrounds were made in the laboratory and under realistic field conditions. A test data suite, developed from these measurements, is used to benchmark algorithm performance throughout the improvement process. In any one measurement, one of many possible targets can be present along with interferents and surfaces. The detection results are expressed as a 2-category classification problem so that Receiver Operating Characteristic (ROC) techniques can be applied. The limitations of applying this framework to chemical detection problems are discussed along with means to mitigate them. Algorithmic performance is optimized globally using robust Design of Experiments and Taguchi techniques. These methods require figures of merit to trade off between false alarms and detection probability. Several figures of merit, including the Matthews Correlation Coefficient and the Taguchi Signal-to-Noise Ratio are compared. Following the optimization of global parameters which govern the algorithm behavior across all target chemicals, ROC techniques are employed to optimize chemical-specific parameters to further improve performance.

Chyba, Thomas H.; Fisk, Brian; Gunning, Christin; Farley, Kevin; Polizzi, Amber; Baughman, David; Simpson, Steven; Slamani, Mohamed-Adel; Almassy, Robert; da Re, Ryan; Li, Eunice; MacDonald, Steve; Slamani, Ahmed; Mitchell, Scott A.; Pendell-Jones, Jay; Reed, Timothy L.; Emge, Darren

2010-04-01

122

Genetic algorithm for optimization of optical systems  

Microsoft Academic Search

In this paper we described a blind optimization technique with an in-expensive electronics for optical systems to maximize the output signal. Deformable mirror is the main optical element used in the system to correct the wavefront and increase the output signal. The mirror is controlled by genetic algorithms through the computer microphone port and two PCI-Express cards.

Mohammad R. N. Avanaki; S. A. Hojjatoleslami; R. Ebrahimpour; H. Sarmadi; A. G. H. Podoleanu

2010-01-01

123

Optimal synthesis of mechanisms with genetic algorithms  

Microsoft Academic Search

This paper deals with solution methods of optimal synthesis of planar mechanisms. A searching procedure is defined which applies genetic algorithms based on evolutionary techniques and the type of goal function. Problems of synthesis of four-bar planar mechanisms are used to test the method, showing that solutions are accurate and valid for all cases. The possibility of extending the method

J. A. Cabrera; A. Simon; M. Prado

2002-01-01

124

Optimal Power Flow by Enhanced Genetic Algorithm  

Microsoft Academic Search

This paper presents an enhanced genetic algorithm for the solution of the optimal power flow with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus

A. G. Bakirtzis; P. N. Biskas; C. E. Zoumas; V. Petridis

2002-01-01

125

Optimal power flow by enhanced genetic algorithm  

Microsoft Academic Search

This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits,

Anastasios G. Bakirtzis; Pandel N. Biskas; Christoforos E. Zoumas; Vasilios Petridis

2002-01-01

126

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

127

A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing  

Microsoft Academic Search

Hyperspectral unmixing aims at identifying the hid- den spectral signatures (or endmembers) and their corresponding proportions (or abundances) from an observed hyperspectral scene. Many existing hyperspectral unmixing algorithms were developed under a commonly used assumption that pure pixels exist. However, the pure-pixel assumption may be seriously violated for highly mixed data. Based on intuitive grounds, Craig reported an unmixing criterion

Tsung-Han Chan; Chong-Yung Chi; Yu-Min Huang; Wing-Kin Ma

2009-01-01

128

What Is Optimized in Convex Relaxations for Multi-Label Problems: Connecting Discrete and Continuously-Inspired MAP Inference.  

PubMed

In this work we present a unified view on Markov random fields and recently proposed continuous tight convex relaxations for multi-label assignment in the image plane. These relaxations are far less biased towards the grid geometry than Markov random fields (MRFs) on grids. It turns out that the continuous methods are non-linear extensions of the well-established local polytope MRF relaxation. In view of this result a better understanding of these tight convex relaxations in the discrete setting is obtained. Further, a wider range of optimization methods is now applicable to find a minimizer of the tight formulation. We propose two methods to improve the efficiency of minimization. One uses a weaker, but more efficient continuously inspired approach as initialization and gradually refines the energy where it is necessary. The other one reformulates the dual energy enabling smooth approximations to be used for efficient optimization. We demonstrate the utility of our proposed minimization schemes in numerical experiments. Finally, we generalize the underlying energy formulation from isotropic metric smoothness costs to arbitrary non-metric and orientation dependent smoothness terms. PMID:23751286

Zach, Christopher; Hane, Christian; Pollefeys, Marc

2013-06-01

129

Decomposing a polygon into its convex parts  

Microsoft Academic Search

A common operation in geometric computing is the decomposition of complex structures into more basic structures. Since it is easier to apply most algorithms to triangles or arbitrary convex polygons, there is considerable interest in finding fast algorithms for such decompositions. We consider the problem of decomposing a simple (non-convex) polygon into the union of a minimal number of convex

Bernard Chazelle; David P. Dobkin

1979-01-01

130

Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations.  

National Technical Information Service (NTIS)

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tie...

G. Venter J. Sobieszczanski-Sobieski

2005-01-01

131

Optimization of Low Thrust Spacecraft Trajectories Using a Genetic Algorithm.  

National Technical Information Service (NTIS)

This thesis concerns the use of genetic algorithms in the optimization of the trajectories of low thrust spacecraft. Genetic algorithms are programming tools which use the principles of biological evolution and adaptation to optimize processes. These algo...

J. C. Eisenreich

1998-01-01

132

Genetic algorithm optimization of atomic clusters  

SciTech Connect

The authors have been using genetic algorithms to study the structures of atomic clusters and related problems. This is a problem where local minima are easy to locate, but barriers between the many minima are large, and the number of minima prohibit a systematic search. They use a novel mating algorithm that preserves some of the geometrical relationship between atoms, in order to ensure that the resultant structures are likely to inherit the best features of the parent clusters. Using this approach, they have been able to find lower energy structures than had been previously obtained. Most recently, they have been able to turn around the building block idea, using optimized structures from the GA to learn about systematic structural trends. They believe that an effective GA can help provide such heuristic information, and (conversely) that such information can be introduced back into the algorithm to assist in the search process.

Morris, J.R.; Deaven, D.M.; Ho, K.M.; Wang, C.Z.; Pan, B.C.; Wacker, J.G.; Turner, D.E. [Ames Lab., IA (United States)]|[Iowa State Univ., Ames, IA (United States). Dept. of Physics

1996-12-31

133

Sequential unconstrained minimization algorithms for constrained optimization  

NASA Astrophysics Data System (ADS)

The problem of minimizing a function f(x):RJ ? R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G_k(x)=f(x)+g_k(x), to obtain xk. The auxiliary functions gk(x):D ? RJ ? R+ are nonnegative on the set D, each xk is assumed to lie within D, and the objective is to minimize the continuous function f:RJ ? R over x in the set C=\\overline D , the closure of D. We assume that such minimizers exist, and denote one such by \\hat x . We assume that the functions gk(x) satisfy the inequalities 0\\leq g_k(x)\\leq G_{k-1}(x)-G_{k-1}(x^{k-1}), for k = 2, 3, .... Using this assumption, we show that the sequence {f(xk)} is decreasing and converges to f({\\hat x}) . If the restriction of f(x) to D has bounded level sets, which happens if \\hat x is unique and f(x) is closed, proper and convex, then the sequence {xk} is bounded, and f(x^*)=f({\\hat x}) , for any cluster point x*. Therefore, if \\hat x is unique, x^*={\\hat x} and \\{x^k\\}\\rightarrow {\\hat x} . When \\hat x is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton-Raphson method. The proof techniques used for SUMMA can be extended to obtain related results for the induced proximal distance method of Auslander and Teboulle.

Byrne, Charles

2008-02-01

134

Non-convex algorithm for sparse and low-rank recovery: application to dynamic MRI reconstruction.  

PubMed

In this work we exploit two assumed properties of dynamic MRI in order to reconstruct the images from under-sampled K-space samples. The first property assumes the signal is sparse in the x-f space and the second property assumes the signal is rank-deficient in the x-t space. These assumptions lead to an optimization problem that requires minimizing a combined lp-norm and Schatten-p norm. We propose a novel FOCUSS based approach to solve the optimization problem. Our proposed method is compared with state-of-the-art techniques in dynamic MRI reconstruction. Experimental evaluation carried out on three real datasets shows that for all these datasets, our method yields better reconstruction both in quantitative and qualitative evaluation. PMID:23102947

Majumdar, Angshul; Ward, Rabab K; Aboulnasr, Tyseer

2012-10-25

135

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

2011-05-01

136

Multidisciplinary design optimization using genetic algorithms  

NASA Astrophysics Data System (ADS)

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. optimization is studied.

Unal, Resit

1994-12-01

137

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

138

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

139

Machining fixture layout optimization using particle swarm optimization algorithm  

NASA Astrophysics Data System (ADS)

Optimization of fixture layout (locator and clamp locations) is critical to reduce geometric error of the workpiece during machining process. In this paper, the application of particle swarm optimization (PSO) algorithm is presented to minimize the workpiece deformation in the machining region. A PSO based approach is developed to optimize fixture layout through integrating ANSYS parametric design language (APDL) of finite element analysis to compute the objective function for a given fixture layout. Particle library approach is used to decrease the total computation time. The computational experiment of 2D case shows that the numbers of function evaluations are decreased about 96%. Case study illustrates the effectiveness and efficiency of the PSO based optimization approach.

Dou, Jianping; Wang, Xingsong; Wang, Lei

2010-12-01

140

Genetic Algorithms Compared to Other Techniques for Pipe Optimization  

Microsoft Academic Search

The genetic algorithm technique is a relatively new optimization tech- nique. In this paper we present a methodology for optimizing pipe networks using genetic algorithms. Unknown decision variables are coded as binary strings. We investigate a three-operator genetic algorithm comprising reproduction, crossover, and mutation. Results are compared with the techniques of complete enumeration and nonlinear programming. We apply the optimization

Angus R. Simpson; Graeme C. Dandy; Laurence J. Murphy

1994-01-01

141

Curvature-Constrained Shortest Paths in a Convex Polygon  

Microsoft Academic Search

Let be a point robot moving in the plane, whose path is con- strained to have curvature at most , and let be a convex polygon with vertices. We study the collision-free, optimal path-plann ing problem for moving between two configurations inside (a con- figuration specifies both a location and a direction of travel ). We present an time algorithm

Pankaj K. Agarwal; Therese C. Biedl; Sylvain Lazard; Steve Robbins; Subhash Suri; Sue Whitesides

2002-01-01

142

Optimal hydrogenerator governor tuning with a genetic algorithm  

SciTech Connect

Many techniques exist for developing optimal controllers. This paper investigates genetic algorithms as a means of finding optimal solutions over a parameter space. In particular, the genetic algorithm is 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 provides advantages in speed over digital plant simulation. This speed advantage makes application of the genetic algorithm in an actual plant environment feasible. Furthermore, the genetic algorithm is shown to possess the ability to reject plant noise and other system anomalies in its search for optimizing solutions.

Lansberry, J.E. (Illinois Univ., Urbana, IL (United States). Dept. of Electrical and Computer Engineering); Wozniak, L.; Goldberg, D.E. (Illinois Univ., Urbana, IL (United States). Dept. of General Engineering)

1992-12-01

143

Genetic algorithm optimization for aerospace electromagnetic design and analysis  

Microsoft Academic Search

This paper provides a tutorial overview of a new approach to optimization for aerospace electromagnetics known as the Genetic Algorithm. Genetic Algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and GA is discussed and the details of GA optimization implementation are explored. The tutorial overview

J. Michael Johnson; Yahya Rahmat-Samii

1996-01-01

144

Evolutionary Based Intelligent Algorithm for Topology Optimization of Structure  

Microsoft Academic Search

This paper gives a new evolutionary algorithm for structure topology optimization. It combines the merits of level set method (LSM) and evolutionary structure optimization (ESO) for structure topology optimization. The traditional LSM algorithm has some drawbacks, for instance, its optimal topology configuration is largely dependent on the initial configuration of the structure. Additionally, new holes cannot be inserted into the

Chundong Jiang; Haipeng Jia

2006-01-01

145

Urban drain layout optimization using PBIL algorithm  

NASA Astrophysics Data System (ADS)

Strengthen the environmental protection is one of the basic national policies in China. The optimization of urban drain layout plays an important role to the protection of water ecosystem and urban environment. The paper puts forward a method to properly locate urban drain using population based incremental learning (PBIL) algorithm. The main factors such as regional containing sewage capacity, sewage disposal capacity quantity limit of drains within specific area are considered as constraint conditions. Analytic hierarchy process is used to obtain weight of each factor, and spatial analysis of environmental influencing factors is carried on Based on GIS. Penalty function method is put forward to model the problem and object function is to guarantee economy benefit. The algorithm is applied to the drain layout engineering of Nansha District, Guangzhou City, China. The drain layout obtained though PBIL algorithm excels traditional method and it can protect the urban environment more efficiently and ensure the healthy development of water ecosystem more successfully. The result has also proved that PBIL algorithm is a good method in solving this question because of its robust performance and stability which supplied strong technologic support to the sustainable development of environment.

Wan, Shanshan; Hao, Ying; Qiu, Dongwei; Zhao, Xu

2008-10-01

146

Intervals in evolutionary algorithms for global optimization  

SciTech Connect

Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.

Patil, R.B.

1995-05-01

147

Multiobjective evolutionary algorithms for complex portfolio optimization problems  

Microsoft Academic Search

This paper investigates the ability of Multiobjective Evolutionary Algorithms (MOEAs), namely the Non-dominated Sorting Genetic\\u000a Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm (PESA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2),\\u000a for solving complex portfolio optimization problems. The portfolio optimization problem is a typical bi-objective optimization\\u000a problem with objectives the reward that should be maximized and the risk that should be

Konstantinos P. Anagnostopoulos; Georgios Mamanis

2011-01-01

148

Convexity, complexity, and high dimensions  

Microsoft Academic Search

We discuss metric, algorithmic and geometric issues related to broadly understood complexity of high dimensional convex sets. The specific topics we bring up include metric entropy and its duality, derandomization of constructions of normed spaces or of convex bodies, and different fundamental questions related to geometric diversity of such bodies, as measured by various isomorphic (as opposed to isometric) invariants.

Stanislaw J. Szarek

149

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

150

Stochastic Convex Programming: Kuhn-Tucker Conditions.  

National Technical Information Service (NTIS)

Optimality criteria are derived for stochastics programs with convex objective and convex constraints. The (basic) Kuhn-Tucker conditions are obtained in terms of conditions on the existence of saddle points of a Lagrangian associated with the stochastic ...

R. T. Rockafellar R. J. B. Wets

1974-01-01

151

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.

Celik, Yuksel; Ulker, Erkan

2013-01-01

152

An ant colony algorithm aimed at dynamic continuous optimization  

Microsoft Academic Search

The introduction of the concept of swarm intelligence into ant colony optimization (ACO) algorithms has shown the rich possibilities of self-organization when dealing with difficult optimization. Indeed, the inherent flexibility and efficiency of ACO algorithms proved to be advantageous for difficult dynamic discrete problems, e.g. routing in telecommunication networks. Moreover, we believe that ant colony algorithms can be efficient for

J. Dréo; P. Siarry

2006-01-01

153

Design of the Optimal Path Algorithm for Line Track Robots  

Microsoft Academic Search

According to the path information collected,combined with the characteristics of the car body, the data of the path has been analyzed, the path optimizing algorithm for line track robots has been established, and the correctness of the algorithm has been verified in experiments in this paper. The algorithm design has some certain significance for the line track optimization of robots

Shixuan Yao; Baoliang Li; Yu Yuan; Jiyou Fei

2009-01-01

154

An Estimation of Distribution Particle Swarm Optimization Algorithm  

Microsoft Academic Search

In this paper we present an estimation of distribution par- ticle swarm optimization algorithm that borrows ideas from recent de- velopments in ant colony optimization which can be considered an es- timation of distribution algorithm. In the classical particle swarm opti- mization algorithm, particles exploit their individual memory to explore the search space. However, the swarm as a whole has

Mudassar Iqbal; Marco Antonio Montes De Oca

2006-01-01

155

Instrument design and optimization using genetic algorithms  

NASA Astrophysics Data System (ADS)

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.

Hölzel, Robert; Bentley, Phillip M.; Fouquet, Peter

2006-10-01

156

Optimization of algorithms for ion mobility calculations.  

PubMed

Ion mobility spectrometry (IMS) is increasingly employed to probe the structures of gas-phase ions, particularly those of proteins and other biological macromolecules. This process involves comparing measured mobilities to those computed for potential geometries, which requires evaluation of orientationally averaged cross sections using some approximate treatment of ion-buffer gas collisions. Two common models are the projection approximation (PA) and exact hard-spheres scattering (EHSS) that represent ions as collections of hard spheres. Though calculations for large ions and/or conformer ensembles take significant time, no algorithmic optimization had been explored. Previous EHSS programs were dominated by ion rotation operations that allow orientational averaging. We have developed two new algorithms for PA and EHSS calculations: one simplifies those operations and greatly reduces their number, and the other disposes of them altogether by propagating trajectories from a random origin. The new algorithms were tested for a representative set of seven ion geometries including diverse sizes and shapes. While the best choice depends on the geometry in a nonobvious way, the difference between the two codes is generally modest. Both are much more efficient than the existing software, for example faster than the widely used Mobcal (implementing EHSS) approximately 10-30-fold. PMID:17300182

Shvartsburg, Alexandre A; Mashkevich, Stefan V; Baker, Erin Shammel; Smith, Richard D

2007-02-15

157

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

158

An Analytical Approach to Optimize NC Tool Path Planning for Face Milling Flat Convex Polygonal Surfaces  

Microsoft Academic Search

CAD\\/CAM systems currently generate the tool cutter path for many NC operations. However no mathematical model is available for computing an optimal tool cutter path for face milling. By utilizing such a model, the minimum length of cut can be identified for face milling flat surfaces. In this paper, the authors present an analytical procedure from which the optimal cutting

HSU-PIN WANG; HENG CHANG; RICHARD A. WYSK

1988-01-01

159

A novel subgradient-based optimization algorithm for blockmodel functional module identification  

PubMed Central

Functional module identification in biological networks may provide new insights into the complex interactions among biomolecules for a better understanding of cellular functional organization. Most of existing functional module identification methods are based on the optimization of network modularity and cluster networks into groups of nodes within which there are a higher-than-expectation number of edges. However, module identification simply based on this topological criterion may not discover certain kinds of biologically meaningful modules within which nodes are sparsely connected but have similar interaction patterns with the rest of the network. In order to unearth more biologically meaningful functional modules, we propose a novel efficient convex programming algorithm based on the subgradient method with heuristic path generation to solve the problem in a recently proposed framework of blockmodel module identification. We have implemented our algorithm for large-scale protein-protein interaction (PPI) networks, including Saccharomyces cerevisia and Homo sapien PPI networks collected from the Database of Interaction Proteins (DIP) and Human Protein Reference Database (HPRD). Our experimental results have shown that our algorithm achieves comparable network clustering performance in comparison to the more time-consuming simulated annealing (SA) optimization. Furthermore, preliminary results for identifying fine-grained functional modules in both biological networks and the comparison with the commonly adopted Markov Clustering (MCL) algorithm have demonstrated the potential of our algorithm to discover new types of modules, within which proteins are sparsely connected but with significantly enriched biological functionalities.

2013-01-01

160

Optimality of the neighbor joining algorithm and faces of the balanced minimum evolution polytope.  

PubMed

Balanced minimum evolution (BME) is a statistically consistent distance-based method to reconstruct a phylogenetic tree from an alignment of molecular data. In 2000, Pauplin showed that the BME method is equivalent to optimizing a linear functional over the BME polytope, the convex hull of the BME vectors obtained from Pauplin's formula applied to all binary trees. The BME method is related to the Neighbor Joining (NJ) Algorithm, now known to be a greedy optimization of the BME principle. Further, the NJ and BME algorithms have been studied previously to understand when the NJ Algorithm returns a BME tree for small numbers of taxa. In this paper we aim to elucidate the structure of the BME polytope and strengthen knowledge of the connection between the BME method and NJ Algorithm. We first prove that any subtree-prune-regraft move from a binary tree to another binary tree corresponds to an edge of the BME polytope. Moreover, we describe an entire family of faces parameterized by disjoint clades. We show that these clade-faces are smaller dimensional BME polytopes themselves. Finally, we show that for any order of joining nodes to form a tree, there exists an associated distance matrix (i.e., dissimilarity map) for which the NJ Algorithm returns the BME tree. More strongly, we show that the BME cone and every NJ cone associated to a tree T have an intersection of positive measure. PMID:21373975

Haws, David C; Hodge, Terrell L; Yoshida, Ruriko

2011-03-04

161

An improved harmony search algorithm for solving optimization problems  

Microsoft Academic Search

This paper develops an Improved harmony search (IHS) algorithm for solving optimization problems. IHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented. The IHS algorithm

M. Mahdavi; M. Fesanghary; E. Damangir

2007-01-01

162

Two improved harmony search algorithms for solving engineering optimization problems  

NASA Astrophysics Data System (ADS)

This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. First algorithm, proposed harmony search (PHS), introduces a new definition of bandwidth (bw). Second algorithm, improving proposed harmony search (IPHS) employs to enhance accuracy and convergence rate of PHS algorithm. In IPHS, non-uniform mutation operation is introduced which is combination of Yang bandwidth and PHS bandwidth. Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using IPHS are in agreement or better than those obtained from other methods.

Jaberipour, Majid; Khorram, Esmaile

2010-11-01

163

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

NASA Astrophysics Data System (ADS)

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.

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

2008-11-01

164

Genetic algorithm and particle swarm optimization combined with Powell method  

NASA Astrophysics Data System (ADS)

In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui

2013-10-01

165

Optimal path planning for mobile robots based on intensified ant colony optimization algorithm  

Microsoft Academic Search

Optimal path planning for mobile robots plays an important role in the field of robotics. At present, there are many advanced algorithms used to solve this optimal problem. However, for those algorithms, it is very difficult to solve some path planning problems containing certain constraint conditions due to the complex background environment. Based on the intensified ant colony optimization algorithm,

Xiaoping Fan; Xiong Luo; Sheng Yi; Shengyue Yang; Heng Zhang

2003-01-01

166

Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm  

Microsoft Academic Search

This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller

Hai-bin Duan; Dao-bo Wang; Xiu-fen Yu

2006-01-01

167

Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior  

Microsoft Academic Search

Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members

Shan He; Q. Henry Wu; J. R. Saunders

2009-01-01

168

An efficient envelope-based Branch and Bound algorithm for non-convex combined heat and power production planning  

Microsoft Academic Search

Combined heat and power (CHP) production is universally accepted as one of the most energy-efficient technologies to produce energy with lower fuel consumption and fewer emissions. In CHP technology, heat and power generation follow a joint characteristic. Traditional CHP production is usually applied in backpressure plants, where the joint characteristic can often be represented by a convex model. Advanced CHP

Aiying Rong; Risto Lahdelma

2007-01-01

169

Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization.  

PubMed

Reducing scanning time is significantly important for MRI. Compressed sensing has shown promising results by undersampling the k-space data to speed up imaging. Sparsity of an image plays an important role in compressed sensing MRI to reduce the image artifacts. Recently, the method of patch-based directional wavelets (PBDW) which trains geometric directions from undersampled data has been proposed. It has better performance in preserving image edges than conventional sparsifying transforms. However, obvious artifacts are presented in the smooth region when the data are highly undersampled. In addition, the original PBDW-based method does not hold obvious improvement for radial and fully 2D random sampling patterns. In this paper, the PBDW-based MRI reconstruction is improved from two aspects: 1) An efficient non-convex minimization algorithm is modified to enhance image quality; 2) PBDW are extended into shift-invariant discrete wavelet domain to enhance the ability of transform on sparsifying piecewise smooth image features. Numerical simulation results on vivo magnetic resonance images demonstrate that the proposed method outperforms the original PBDW in terms of removing artifacts and preserving edges. PMID:23992629

Ning, Bende; Qu, Xiaobo; Guo, Di; Hu, Changwei; Chen, Zhong

2013-08-29

170

Optimizing the pre-decoding algorithm  

NASA Astrophysics Data System (ADS)

Considering the coding-excitation technology applied in ultrasonic systems, pre-decoding by multi-center before beam-synthesis is recognized as the best method for decoding. Compared with the method of decoding after synthesizing, the former avoids the inferior quality of side-lobe performance invited by beam-synthesis (the attenuation is more than 15dB). However, it is restricted by its great requirement to hardware cost resources so that pre-decoding method couldn't be made the most of in practice. In order to resolve the practical issue, this paper advances a set of project to retrench hardware cost by optimizing the decoding algorithm in theory. The resulting data based on Golay code with Quartus II validates the validity and feasibility of this project.

He, Jun; Han, Song; Han, Ziqiang

2007-03-01

171

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

NASA Astrophysics Data System (ADS)

Digital breast tomosynthesis (DBT) is a rapidly developing imaging modality that gives some tomographic information for breast cancer screening. The effectiveness of standard mammography can be limited by the presence of overlapping structures in the breast. A DBT scan, consisting of a limited number of views covering a limited arc projecting the breast onto a fixed flat-panel detector, involves only a small modification of digital mammography, yet DBT yields breast image slices with reduced interference from overlapping breast tissues. We have recently developed an iterative image reconstruction algorithm for DBT based on image total variation (TV) minimization that improves on EM in that the resulting images have fewer artifacts and there is no need for additional regularization. In this abstract, we present the total p-norm variation (TpV) image reconstruction algorithm. TpV has the advantages of our previous TV algorithm, while improving substantially on the efficiency. Results for the TpV on clinical data are shown and compared with EM.

Sidky, Emil Y.; Reiser, Ingrid; Nishikawa, Robert M.; Pan, Xiaochuan; Chartrand, Rick; Kopans, Daniel B.; Moore, Richard H.

2008-04-01

172

BMI optimization based on unimodal normal distribution crossover GA with relaxed LMI convex estimation  

Microsoft Academic Search

This paper deals with the global optimization of the BMIEP (bilinear matrix inequalities eigenvalue problem) based on the UNDX (unimodal normal distribution crossover) GA. First, analyzing the structure of the BMIEP, the existence of the typical difficult structures is confirmed. Then, based on the results of the problem structures analysis and the consideration of the BMIEP characteristic properties, an efficient

Michihiro Kawanishi; Yuu Ikuyama

2005-01-01

173

SamACO: Variable Sampling Ant Colony Optimization Algorithm for Continuous Optimization  

Microsoft Academic Search

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of

Xiao-Min Hu; Jun Zhang; Henry Shu-Hung Chung; Yun Li; Ou Liu

2010-01-01

174

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

175

Optimization of methanol synthesis reactor using genetic algorithms  

Microsoft Academic Search

This paper presents a study on optimization of methanol synthesis reactor to enhance overall production. A mathematical heterogeneous model of the reactor was used for optimization of reactor performance, both at steady state and dynamic conditions. Here, genetic algorithms were used as powerful methods for optimization of complex problems. Initially, optimal temperature profile along the reactor was studied. Then, a

H. Kordabadi; A. Jahanmiri

2005-01-01

176

A Scheduling Algorithm to Optimize Parallel Processes  

Microsoft Academic Search

In this paper we present a scheduling algorithm that assigns tasks of medium size grain. The behavior of the proposed algorithm, called extended latency time (ELT), is compared with the dominant sequence clustering (DSC) algorithm. One of the inputs values required by the ELT algorithm is the maximum number of processors available in the architecture. This value corresponds to the

Mauricio Solar

2008-01-01

177

Integrated discrete and configuration optimization of trusses using genetic algorithms  

Microsoft Academic Search

The application of genetic algorithms to integrated discrete and configuration optimization of trusses is presented. It is mathematically formulated as a constrained nonlinear optimization problem with a mix of discrete sizing and continuous configuration variables. The components of genetic algorithms are described. The discrete sizing variables are treated by constructing mapping relationships between binary digit strings and discrete values by

Shyue-Jian Wu; Pei-Tse Chow

1995-01-01

178

New algorithms for mixed-integer dynamic optimization  

Microsoft Academic Search

Mixed-integer dynamic optimization (MIDO) problems arise in chemical engineering whenever discrete and continuous decisions are to be made for a system described by a transient model. Areas of application include integrated design and control, synthesis of reactor networks, reduction of kinetic mechanisms and optimization of hybrid systems. This article presents new formulations and algorithms for solving MIDO problems. The algorithms

Vikrant Bansal; Vassilis Sakizlis; Roderick Ross; John D. Perkins; Efstratios N. Pistikopoulos

2003-01-01

179

An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach  

Microsoft Academic Search

Evolutionary algorithms (EA) have proved to be well suited for optimization problemswith multiple objectives. Due to their inherent parallelism they are able tocapture a number of solutions concurrently in a single run. In this report, wepropose a new evolutionary approach to multicriteria optimization, the StrengthPareto Evolutionary Algorithm (SPEA). It combines various features of previousmultiobjective EAs in a unique manner and

Eckart Zitzler; Lothar Thiele

1998-01-01

180

Optimal coordination of directional overcurrent relays using harmony search algorithm  

Microsoft Academic Search

Directional over current relays (DOR) are used to protection of interconnected networks and looped distribution systems. Several techniques and formulations have been proposed to solve the optimal coordination of DOR problem. In this paper, harmony search algorithm (HSA) is proposed for optimal coordination of DOR in a looped distribution system. Then this algorithm is developed to a new Improvised harmony

Mostafa Barzegari; S. M. T Bathaee; Mohsen Alizadeh

2010-01-01

181

An Island Based Hybrid Evolutionary Algorithm for Optimization  

Microsoft Academic Search

Evolutionary computation has become an important prob- lem solving methodology among the set of search and optimization tech- niques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for op- timization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation

Changhe Li; Shengxiang Yang

2008-01-01

182

Human-Inspired Algorithms for continuous function optimization  

Microsoft Academic Search

The Human-Inspired Algorithm (HIA) is a new algorithm that uses a given population (a group of candidate solutions) to improve the search for optimal solutions to continuous functions in different optimization applications such as non-linear programming. HIA imitates the intelligent search strategies of mountain climbers who use modern techniques (such as binoculars and cell phones) to effectively find the highest

Luna Mingyi Zhang; Cheyenne Dahlmann; Yanqing Zhang

2009-01-01

183

Discrete Optimization Algorithms in Real-Time Visual Tracking  

Microsoft Academic Search

In this work we introduce a novel formulation of the association problem in visual tracking systems as a discrete optimization problem. The full data association problem is formulated as a search for the best tracking configuration to match hypothesis. We have implemented three local search algorithms: Hill Climbing, Simulated Annealing, and Tabu Search algorithms. These algorithms are guided by heuristic

Miguel A. Patricio; Iván Dotú; Jesús García; Antonio Berlanga; José M. Molina

2009-01-01

184

A Micro-Genetic Algorithm for Multiobjective Optimization  

Microsoft Academic Search

In this paper, a new evolutionary multiobjective optimization algorithm is proposed. The approach is based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to generate the initial population of the micro-GA. Our

Carlos Coello Coello Coello; Gregorio Toscano Pulido

2001-01-01

185

Two improved harmony search algorithms for solving engineering optimization problems  

Microsoft Academic Search

This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of

Majid Jaberipour; Esmaile Khorram

2010-01-01

186

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 ...

187

A distributed Cooperative coevolutionary algorithm for multiobjective optimization  

Microsoft Academic Search

Recent advances in evolutionary algorithms show that coevolutionary architectures are effective ways to broaden the use of traditional evolutionary algorithms. This paper presents a cooperative coevolutionary algorithm (CCEA) for multiobjective optimization, which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subpopulations. Incorporated with various features like archiving, dynamic

Kay Chen Tan; Y. J. Yang; Chi Keong Goh

2006-01-01

188

Applying fuzzy clustering optimization algorithm to extracting traffic spatial pattern  

NASA Astrophysics Data System (ADS)

Traditional analytical methods for traffic information can't meet to need of intelligent traffic system. Mining value-add information can deal with more traffic problems. The paper exploits a new clustering optimization algorithm to extract useful spatial clustered pattern for predicting long-term traffic flow from macroscopic view. Considering the sensitivity of initial parameters and easy falling into local extreme in FCM algorithm, the new algorithm applies Particle Swarm Optimization method, which can discovery the globe optimal result, to the FCM algorithm. And the algorithm exploits the union of the clustering validity index and objective function of the FCM algorithm as the fitness function of the PSO algorithm. The experimental result indicates that it is effective and efficient. For fuzzy clustering of road traffic data, it can produce useful spatial clustered pattern. And the clustered centers represent the locations which have heavy traffic flow. Moreover, the parameters of the patterns can provide intelligent traffic system with assistant decision support.

Hu, Chunchun; Shi, Wenzhong; Meng, Lingkui; Liu, Min

2009-10-01

189

Hybrid Evolutionary Algorithm for Solving Global Optimization Problems  

Microsoft Academic Search

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid

Radha Thangaraj; Millie Pant; Ajith Abraham; Youakim Badr

2009-01-01

190

Harmony search algorithm: application to the redundancy optimization problem  

Microsoft Academic Search

The redundancy optimization problem is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system performance, given different system-level constraints. This article presents an efficient algorithm based on the harmony search algorithm (HSA) to solve this optimization problem. The HSA is a new nature-inspired algorithm which mimics the improvization process of music players.

Nabil Nahas; Dao Thien-My

2010-01-01

191

Parallel Mixed Bayesian Optimization Algorithm: A Scaleup Analysis  

Microsoft Academic Search

Estimation of Distribution Algorithms have been proposed as a new paradigm\\u000afor evolutionary optimization. This paper focuses on the parallelization of\\u000aEstimation of Distribution Algorithms. More specifically, the paper discusses\\u000ahow to predict performance of parallel Mixed Bayesian Optimization Algorithm\\u000a(MBOA) that is based on parallel construction of Bayesian networks with\\u000adecision trees. We determine the time complexity of parallel

Jiri Ocenasek; Martin Pelikan

2004-01-01

192

Optimal and nearly optimal algorithms for approximating polynomial zeros  

Microsoft Academic Search

We substantially improve the known algorithms for approximating all the complex zeros of an nth degree polynomial p(x). Our new algorithms save both Boolean and arithmetic sequential time, versus the previous best algorithms of Schönhage [1], Pan [2], and Neff and Reif [3]. In parallel (NC) implementation, we dramatically decrease the number of processors, versus the parallel algorithm of Neff

V. Y. Pan

1996-01-01

193

Optimal Discrete Recombination: Hybridising Evolution Strategies with the A* Algorithm  

Microsoft Academic Search

This work studies a hybrid model in which an optimal search algorithm intended for discrete optimisation (A*) is combined with a heuristic algorithm for continuous optimisation (an evolution strategy). The resulting algorithm is successfully\\u000a evaluated on a set of functions exhibiting different features such as multimodality, noise or epistasis. The scalability of\\u000a the algorithm in the presence of epistasis is

Carlos Cotta; José M. Troya

1999-01-01

194

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

2013-07-14

195

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

196

A Danger-Theory-Based Immune Network Optimization Algorithm  

PubMed Central

Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.

Li, Tao; Xiao, Xin; Shi, Yuanquan

2013-01-01

197

Study on MCM interconnect test generation using ant algorithm and particle swarm optimization algorithm  

Microsoft Academic Search

A new approach based on ant algorithm (AA) and particle swarm optimization (PSO) algorithm is proposed for Multi-chip Module (MCM) interconnect test generation in this paper. Using the pheromone-updating rule and state transition rule, AA generates the initial candidate test vectors. PSO is employed to evolve the candidates generated by AA. The optimized search is guided by the swarm intelligent

Chen Lei

2008-01-01

198

Optimal barreling of steel shells via simulated annealing algorithm  

Microsoft Academic Search

The load carrying capacity, of externally pressurised and optimally shaped metallic shell, has been increased by 40% over the performance of an equivalent cylinder. The optimal geometry has been sought within a class of generalised ellipses by the application of simulated annealing algorithm.The optimal solution has been verified experimentally by collapsing two, nominally identical, CNC-machined, mild steel shells at about

J. B?achut

2003-01-01

199

OPTIMIZATION FOR INDUCTION MOTOR DESIGN BY IMPROVED GENETIC ALGORITHM  

Microsoft Academic Search

Optimization for induction motor design is one of the interested subjects by electrical engineers. This paper proposes an Improved Genetic Algorithm (IGA) for optimization of 3-phase induction motor design. The proposed IGA possesses the characteristics of real number encoding, stochastic crossover operator, self-adaptable mutation operator and annealing penalty function, and multi- turns evolution strategy for solving nonlinear constrained multivariable optimization

Li HAN; Hui LI; Jingcan LI; Jianguo ZHU

2004-01-01

200

Nonholonomic motion planning based on Newton algorithm with energy optimization  

Microsoft Academic Search

Discusses a modification of the Newton algorithm applied to nonholonomic motion planning with energy optimization. The energy optimization is performed either by optimizing motion in the space of the Jacobian matrix derived from the nonholonomic system or coupling this motion with movement toward the goal. Resulting controls are smooth and easily generated by motors or thrusters. The two methods can

Ignacy Duleba; Jurek Z. Sasiadek

2003-01-01

201

Wave Algorithms: Optimal Database Search and Catalysis  

NASA Astrophysics Data System (ADS)

Grover's database search algorithm, although discovered in the context of quantum computation, can be implemented using any physical system that allows superposition of states. A physical realization of this algorithm is described using coupled simple harmonic oscillators, which can be exactly solved in both classical and quantum domains. Classical wave algorithms are far more stable against decoherence compared to their quantum counterparts. In addition to providing convenient demonstration models, they may have a role in practical situations, such as catalysis.

Patel, Apoorva D.

2006-11-01

202

Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems  

Microsoft Academic Search

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained\\u000a optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that\\u000a it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained\\u000a optimization problems and applied to

Dervis Karaboga; Bahriye Basturk

2007-01-01

203

Locally optimal decomposition for autonomous obstacle avoidance with the Tunnel-MILP algorithm  

Microsoft Academic Search

The Tunnel-MILP algorithm is a three stage path planning method for 2-D environments that relies on the iden- tification of a sequence of convex polygons to form an obstacle free tunnel through which to plan a dynamically feasible path. This work investigates two aspects of the algorithm. First, a greedy cut method is proposed for improved decomposition of the environment,

Michael P. Vitus; Steven L. Waslander; Claire J. Tomlin

2008-01-01

204

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.

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

2012-09-01

205

Selective voting in convex-hull ensembles improves classification accuracy  

PubMed Central

Objective Classification algorithms can be used to predict risks and responses of patients based on genomic and other high-dimensional data. While there is optimism for using these algorithms to improve the treatment of diseases, they have yet to demonstrate sufficient predictive ability for routine clinical practice. They generally classify all patients according to the same criteria, under an implicit assumption of population homogeneity. The objective here is to allow for population heterogeneity, possibly unrecognized, in order to increase classification accuracy and further the goal of tailoring therapies on an individualized basis. Methods and materials Anew selective-voting algorithm is developed in the context of a classifier ensemble of two-dimensional convex hulls of positive and negative training samples. Individual classifiers in the ensemble are allowed to vote on test samples only if those samples are located within or behind pruned convex hulls of training samples that define the classifiers. Results Validation of the new algorithm’s increased accuracy is carried out using two publicly available datasets having cancer as the outcome variable and expression levels of thousands of genes as predictors. Selective voting leads to statistically significant increases in accuracy from 86.0% to 89.8% (p < 0.001) and 63.2% to 67.8% (p < 0.003) compared to the original algorithm. Conclusion Selective voting by members of convex-hull classifier ensembles significantly increases classification accuracy compared to one-size-fits-all approaches.

Kodell, Ralph L.; Zhang, Chuanlei; Siegel, Eric R.; Nagarajan, Radhakrishnan

2011-01-01

206

A fast algorithm for assortment optimization problems  

Microsoft Academic Search

Assortment optimization problems intend to seek the best way of placing a given set of rectangles within a minimum-area rectangle. Such problems are often formulated as a quadratic mixed 0–1 program. Many current methods for assortment problems are either unable to find an optimal solution or being computationally inefficient for reaching an optimal solution. This paper proposes a new method

Han-lin Li; Jung-fa Tsai

2001-01-01

207

On the cellular convexity of complexes.  

PubMed

In this paper we discuss cellular convexity of complexes. A new definition of cellular convexity is given in terms of a geometric property. Then it is proven that a regular complex is celiularly convex if and only if there is a convex plane figure of which it is the cellular image. Hence, the definition of cellular convexity by Sklansky [7] is equivalent to the new definition for the case of regular complexes. The definition of Minsky and Papert [4] is shown to be equivalent to our definition. Therefore, aU definitions are virtually equivalent. It is shown that a regular complex is cellularly convex if and only if its minimum-perimeter polygon does not meet the boundary of the complex. A 0(n) time algorithm is presented to determine the cellular convexity of a complex when it resides in n × m cells and is represented by the run length code. PMID:21868981

Kim, C E

1981-06-01

208

Shape Optimization of Cochlear Implant Electrode Array Using Genetic Algorithms.  

National Technical Information Service (NTIS)

Finite element analysis is used to compute the current distribution of the human cochlea during cochlear implant electrical stimulation. Genetic algorithms are then applied in conjunction with the finite element analysis to optimize the shape of cochlear ...

C. T. Choi

2001-01-01

209

Industrial applications of the ant colony optimization algorithm  

Microsoft Academic Search

The ant colony optimization (ACO) algorithm is a fast suboptimal meta-heuristic based on the behavior of a set of ants that\\u000a communicate through the deposit of pheromone. It involves a node choice probability which is a function of pheromone strength\\u000a and inter-node distance to construct a path through a node-arc graph. The algorithm allows fast near optimal solutions to\\u000a be

Bud Fox; Wei Xiang; Heow Pueh Lee

2007-01-01

210

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

211

Algorithm for computer aided optimal control system design  

Microsoft Academic Search

The paper describes a new algorithm of optimal control system design for a linear, time-invariant, single-input single-output plant. The proposed algorithm has low computational complexity, high stability and possesses the clear physical sense of the existence conditions of the design problem solution. The reference (command) signal and disturbances can include random and regular components. The algorithm is based on the

A. R. Gaiduk; Y. A. Vershinin; A. Jawaid

2002-01-01

212

Optimization and improvement of Genetic Algorithms solving Traveling Salesman Problem  

Microsoft Academic Search

Traveling salesman problem (TSP) is a typical NP-complete problem, of which the search space increases with the number of cities. Genetic algorithm (GA) is an efficient optimization algorithm characterized with explicit parallelism and robustness, applicable to TSP. In this paper, we compare the performance of the existing GAs in searching the solution for TSP and find a superior combination of

Liping Zhang; Min Yao; Nenggan Zheng

2009-01-01

213

Multiobjective Optimization using a Micro-Genetic Algorithm  

Microsoft Academic Search

In this paper, we propose a micro genetic algorithm with three forms of elitism for multiobjective optimization. We show how this relatively simple algorithm coupled with an external file and a diversity approach based on geographical distribution can generate efficiently the Pareto fronts of several difficult test functions (both constrained and unconstrained). A metric based on the average distance to

Carlos A. Coello Coello

214

A Computationally Efficient Evolutionary Algorithm for Real-Parameter Optimization  

Microsoft Academic Search

Due to increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have recently developed a number of real-parameter genetic algorithms (GAs). In these studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create an offspring. Some operators emphasize solutions at the center

Kalyanmoy Deb; Ashish Anand; Dhiraj Joshi

2002-01-01

215

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

216

Algorithms for Noisy Problems in Gas Transmission Pipeline Optimization  

Microsoft Academic Search

In this paper we describe some algorithms for noisy optimization in the context of problems from the gas transmission industry. The algorithms are implicit filtering, DIRECT, and a new hybrid of these methods, which uses DIRECT to find an intitial iterate for implicit filtering. We report on numerical results that illustrate the performance of the methods.

R. G. Carter; J. M. Gablonsky; A. Patrick; C. T. Kelley; O. J. Eslinger

2001-01-01

217

The Development of Information Guided Evolution Algorithm for Global Optimization  

Microsoft Academic Search

Evolutionary algorithm (EA) has become popular in global optimization with applications widely used in many industrial areas. However, there exists probable premature convergence problem when rugged contour situation is encountered. As to the original genetic algorithm (GA), no matter single population or multi-population cases, the ways to prevent the problem of probable premature convergence are to implement various selection methods,

Chen-Wei Yeh; Shi-Shang Jang

2006-01-01

218

Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm  

Microsoft Academic Search

During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but not in the other. In the first part of the paper, we present the results and insights obtained from a detailed empirical

Marco Antonio Montes de Oca; Thomas Stützle; Mauro Birattari; Marco Dorigo

2009-01-01

219

Recurrent learning algorithms for designing optimal controllers of continuous systems  

Microsoft Academic Search

Proposes a recurrent learning algorithm for designing the controllers of continuous dynamical systems in optimal control problems. The controllers are in the form of unfolded recurrent neural nets embedded with physical laws from classical control techniques. The learning algorithm is characterized by a double forward-recurrent-loops structure for solving both temporal recurrent and structure recurrent problems. The first problem results from

Yi-jen Wang; Chin-teng Lin

2000-01-01

220

Optimizing the construction relations of chiral slab via genetic algorithm  

Microsoft Academic Search

A novel method of optimizing the continuous variable constitutive relation of multi-layered chiral slab based on genetic algorithm was discussed in this paper. This method derived continuous variable constitutive relations automatically, then calculated the reflection coefficient by wave-splitting method. Furthermore the genetic algorithm was used to search the maximum absorbability of the chiral slab with certain thickness

Y. C. Gao; Jian Hua Xu; De Shuang Zhao; Shu Zhang Liu

2000-01-01

221

A polynomial time algorithm for optimal routing around a rectangle  

Microsoft Academic Search

In this paper we present an algorithm for a special case of wire routing. Given a rectangular circuit component on a planar surface with terminals around its boundary, the algorithm finds an optimal set of paths in the plane connecting specified pairs of terminals. The paths are restricted to lie on the outside of the component and must consist of

Andrea S. LaPaugh

1980-01-01

222

Solving an optimization packing problem of circles and non-convex polygons with rotations into a multiply connected region  

Microsoft Academic Search

This paper deals with the packing problem of circles and non-convex polygons, which can be both translated and rotated into a strip with prohibited regions. Using the ?-function technique, a mathematical model of the problem is constructed and its characteristics are investigated. Based on the characteristics, a solution approach to the problem is offered. The approach includes the following methods:

Y G Stoyan; M V Zlotnik; A M Chugay

223

Solving an optimization packing problem of circles and non-convex polygons with rotations into a multiply connected region  

Microsoft Academic Search

This paper deals with the packing problem of circles and non-convex polygons, which can be both translated and rotated into a strip with prohibited regions. Using the ?-function technique, a mathematical model of the problem is constructed and its characteristics are investigated. Based on the characteristics, a solution approach to the problem is offered. The approach includes the following methods:

Y G Stoyan; M V Zlotnik; A M Chugay

2012-01-01

224

An optimization algorithm based on chaotic behavior and fractal nature  

NASA Astrophysics Data System (ADS)

In this paper, we propose a new optimization technique by modifying a chaos optimization algorithm (COA) based on the fractal theory. We first implement the weighted gradient direction-based chaos optimization in which the chaotic property is used to determine the initial choice of the optimization parameters both in the starting step and in the mutations applied when a convergence to local minima occurred. The algorithm is then improved by introducing a method to determine the optimal step size. This method is based on the fact that the sensitive dependence on the initial condition of a root finding technique (such as the Newton-Raphson search technique) has a fractal nature. From all roots (step sizes) found by the implemented technique, the one that most minimizes the cost function is employed in each iteration. Numerical simulation results are presented to evaluate the performance of the proposed algorithm.

Tavazoei, Mohammad Saleh; Haeri, Mohammad

2007-09-01

225

Applying new optimization algorithms to more predictive control  

SciTech Connect

The connections between optimization and control theory have been explored by many researchers and optimization algorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point methods, have produced a new set of algorithms that may be especially helpful in this context. In this paper, we reexamine the relatively simple problem of control of linear processes subject to quadratic objectives and general linear constraints. We show how new algorithms for quadratic programming can be applied efficiently to this problem. The approach extends to several more general problems in straightforward ways.

Wright, S.J.

1996-03-01

226

The Vector Model of Artificial Physics Optimization Algorithm for Global Optimization Problems  

Microsoft Academic Search

To solve complex global optimization problems, Artificial Physics Optimization (APO) algorithm is presented based on Physicomimetics framework, which is a population-based stochastic algorithm inspired by physical force. The solutions (particles) sampled from the feasible region of the problems are treated as physical individuals. Each individual has a mass, position and velocity. The mass of each individual corresponds to a user-defined

Liping Xie; Jianchao Zeng; Zhuihua Cui

2009-01-01

227

Coordination and Optimization Of HVDC Modulations Using Chaotic Optimization Algorithm in DC\\/AC Power System  

Microsoft Academic Search

A Chaotic Optimization Algorithm (COA) is applied to integrated AC\\/DC hybrid power system in which the multi-infeed DC power transmission system is included, and the research on coordination controls among the HVDC modulations has been investigated in this paper. Chaotic Optimization Algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from local minima,

Zheng Xi-yun; Li Xing-yuan; Wang Yu-hong; Xu Mei-mei; Mu Zi-long; Liu Jian; Wei Wei

2010-01-01

228

Optimal Distillation Sequencing Using a Genetic Algorithm  

Microsoft Academic Search

Optimal distillation sequencing is a method for obtaining the best structure of multicomponent separation processes. Due to the significant contribution of the distillation sequences to the capital and operating costs for the whole chemical process, the development of a systematic framework that will select the optimal distillation sequences becomes an important research issue. Since distillation sequencing is a combinatorial problem,

P. Piumsomboon

2001-01-01

229

Imperialist competitive algorithm combined with chaos for global optimization  

NASA Astrophysics Data System (ADS)

A novel chaotic improved imperialist competitive algorithm (CICA) is presented for global optimization. The ICA is a new meta-heuristic optimization developed based on a socio-politically motivated strategy and contains two main steps: the movement of the colonies and the imperialistic competition. Here different chaotic maps are utilized to improve the movement step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the other ICA-based methods demonstrates the superiority of the CICA for the benchmark functions.

Talatahari, S.; Farahmand Azar, B.; Sheikholeslami, R.; Gandomi, A. H.

2012-03-01

230

Bacterial Foraging Algorithm for Optimal Power Flow in Dynamic Environments  

Microsoft Academic Search

Optimal power flow (OPF) problem has already been attempted as a static optimization problem, by adopting conventional gradient-based methods and more recently, nonconventional ones, such as evolutionary algorithms. However, as the loads, generation capacities and network connections in a power system are always in a changing status, these static-oriented methods are of limited use for this issue. This paper presents

W. J. Tang; M. S. Li; Q. H. Wu; J. R. Saunders

2008-01-01

231

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

232

Algorithmic funnel-and-gate system design optimization  

Microsoft Academic Search

Funnel-and-gate systems (FGSs), which constitute a common variant of permeable reactive barriers used for in situ treatment of groundwater, pose particular challenges to the task of design optimization. Because of the complex interplay of funnels and gates, the evolutionary algorithms applied have to cope with multimodality, nonseparability, and nonlinearity of the optimization task. We analyze these features in a test

Claudius M. Bürger; Peter Bayer; Michael Finkel

2007-01-01

233

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

234

An Investigation on the Optimization Procedures of Intelligent Genetic Algorithm  

Microsoft Academic Search

Although the genetic algorithm (GA) is powerful and not limited by restrictive assumptions about search space of optimization problems, it has a convergency problem when it is used to find the optimum point with high accuracy. The object of developing an intelligent GA is to improve this weakness. In this paper, the optimization procedures of an intelligent GA are investigated,

Yan-Gang Zhao

2008-01-01

235

Mesh Adaptive Direct Search Algorithms for Constrained Optimization  

Microsoft Academic Search

This paper introduces the Mesh Adaptive Direct Search (MADS) class of algorithms for nonlinear optimization. MADS extends the Generalized Pattern Search (GPS) class by allowing local exploration, called polling, in an asymptotically dense set of directions in the space of optimization variables. This means that under certain hypotheses, including a weak constraint qualification due to Rockafellar, MADS can treat constraints

Charles Audet; J. E. Dennis Jr.

2006-01-01

236

A solution to the optimal power flow using genetic algorithm  

Microsoft Academic Search

Optimal power flow (OPF) is one of the main functions of power generation operation and control. It determines the optimal setting of generating units. It is therefore of great importance to solve this problem as quickly and accurately as possible. This paper presents the solution of the OPF using genetic algorithm technique. This paper proposes a new methodology for solving

M. S. Osman; Mahmoud A. Abo-sinna; A. A. Mousa

2004-01-01

237

Machining fixture layout optimization using the genetic algorithm  

Microsoft Academic Search

Dimensional and form accuracy of a workpiece are influenced by the fixture layout selected for the machining operation. Hence, optimization of fixture layout is a critical aspect of machining fixture design. This paper presents a fixture layout optimization technique that uses the genetic algorithm (GA) to find the fixture layout that minimizes the deformation of the machined surface due to

Kulankara Krishnakumar; Shreyes N. Melkote

2000-01-01

238

On Portfolio Investment Model Using Ant Colony Optimization Algorithm  

Microsoft Academic Search

Based on Markowitz' theory of asset portfolio, a multiple-goal optimization model of portfolio investment was set up considering both risk and return. Then applying ant colony optimization algorithm to solve the model, we got a better result than that of using Lingo.

Zhou Jianguo; Zhang Hui; Tian Jiming

2007-01-01

239

A Dynamic Near-Optimal Algorithm for Online Linear Programming  

Microsoft Academic Search

A natural optimization model that formulates many online resource allocation and revenue management problems is the online linear program (LP) where the constraint matrix is revealed column by column along with the objective function. We provide a near-optimal algorithm for this surprisingly general class of online problems under the assumption of random order of arrival and some mild conditions on

Shipra Agrawal; Zizhuo Wang; Yinyu Ye

2009-01-01

240

Genetic algorithm for neural networks optimization  

NASA Astrophysics Data System (ADS)

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB®.

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

241

Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization  

NASA Astrophysics Data System (ADS)

This chapter introduces two Perl programs that implement graphical tools for exploring the performance of stochastic local search algorithms for biobjective optimization problems. These tools are based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. In particular, we consider the visualization of attainment surfaces and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice.

López-Ibáñez, Manuel; Paquete, Luís; Stützle, Thomas

242

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

243

Analysis and optimization of randomized gossip algorithms  

Microsoft Academic Search

We study the distributed averaging problem on an arbitrary network with a gossip constraint, which means that no node communicates with more than one neighbour in every time slot. We consider algorithms which are linear iterations, where each iteration is described by a random matrix picked i.i.d. from some distribution. We derive conditions that this distribution must satisfy so that

Stephen Boyd; Arpita Ghosh; Balaji Prabhakar; Shah Devavrat

2004-01-01

244

Optimal Speedup of Las Vegas Algorithms  

Microsoft Academic Search

Let A be a Las Vegas algorithm, i.e., A is a randomized algorithmthat always produces the correct answer when it stops but whose runningtime is a random variable. We consider the problem of minimizingthe expected time required to obtain an answer from A using strategieswhich simulate A as follows: run A for a fixed amount of timet 1 , then

Michael Luby; Alistair Sinclair; David Zuckerman

1993-01-01

245

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

246

A Bacterial foraging PSO-DE algorithm for solving dynamic economic dispatch problem with security constraints  

Microsoft Academic Search

This paper presents a heuristic optimization methodology, Bacterial foraging and PSO-DE (BPSO-DE) algorithm by integrating Bacterial foraging optimization Algorithm (BFOA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving Non-Convex Dynamic Economic Dispatch problem (DED). The DED problem exhibits non-convex nature due to valve-point loading effects, ramp rate limits, spinning reserve capacity and security constraints. Even though PSO and

P. Praveena; K. Vaisakh; S. R. M. Rao

2010-01-01

247

Optimal multisensor decision fusion of mine detection algorithms  

NASA Astrophysics Data System (ADS)

Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the Receiver Operating Characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper we present multi-sensor decision fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers, in certain situations, an expedient, attractive and much simpler alternative to "starting over" with the redesign of a new algorithm which fuses multiple sensors at the data level. The goal in our multi-sensor decision fusion approach is to exploit complimentary strengths of existing multi-sensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multi-sensor decision fusion is based on optimal signal detection theory, using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors, GPR (ground penetrating radar) and MD (metal detector). A new robust algorithm for decision fusion is presented that addresses the problem that the statistics of the training data is not likely to exactly match the statistics of the test data. ROC's are presented and compared for real data.

Liao, Yuwei; Nolte, Loren W.; Collins, Leslie M.

2003-09-01

248

A Parallel Tempering algorithm for probabilistic sampling and multimodal optimization  

NASA Astrophysics Data System (ADS)

Non-linear inverse problems in the geosciences often involve probabilistic sampling of multimodal density functions or global optimization and sometimes both. Efficient algorithmic tools for carrying out sampling or optimization in challenging cases are of major interest. Here results are presented of some numerical experiments with a technique, known as Parallel Tempering, which originated in the field of computational statistics but is finding increasing numbers of applications in fields ranging from Chemical Physics to Astronomy. To date, experience in use of Parallel Tempering within earth sciences problems is very limited. In this paper, we describe Parallel Tempering and compare it to related methods of Simulated Annealing and Simulated Tempering for optimization and sampling, respectively. A key feature of Parallel Tempering is that it satisfies the detailed balance condition required for convergence of Markov chain Monte Carlo (McMC) algorithms while improving the efficiency of probabilistic sampling. Numerical results are presented on use of Parallel Tempering for trans-dimensional inversion of synthetic seismic receiver functions and also the simultaneous fitting of multiple receiver functions using global optimization. These suggest that its use can significantly accelerate sampling algorithms and improve exploration of parameter space in optimization. Parallel Tempering is a meta-algorithm which may be used together with many existing McMC sampling and direct search optimization techniques. It's generality and demonstrated performance suggests that there is significant potential for applications to both sampling and optimization problems in the geosciences.

Sambridge, Malcolm

2013-10-01

249

Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design  

PubMed Central

Background Microbial hosts offer a number of unique advantages when used as production systems for both native and heterologous small-molecules. These advantages include high selectivity and benign environmental impact; however, a principal drawback is low yield and/or productivity, which limits economic viability. Therefore a major challenge in developing a microbial production system is to maximize formation of a specific product while sustaining cell growth. Tools to rationally reconfigure microbial metabolism for these potentially conflicting objectives remain limited. Exhaustively exploring combinations of genetic modifications is both experimentally and computationally inefficient, and can become intractable when multiple gene deletions or insertions need to be considered. Alternatively, the search for desirable gene modifications may be solved heuristically as an evolutionary optimization problem. In this study, we combine a genetic algorithm and elementary mode analysis to develop an optimization framework for evolving metabolic networks with energetically favorable pathways for production of both biomass and a compound of interest. Results Utilization of thermodynamically-weighted elementary modes for flux reconstruction of E. coli central metabolism revealed two clusters of EMs with respect to their ?Gp°. For proof of principle testing, the algorithm was applied to ethanol and lycopene production in E. coli. The algorithm was used to optimize product formation, biomass formation, and product and biomass formation simultaneously. Predicted knockouts often matched those that have previously been implemented experimentally for improved product formation. The performance of a multi-objective genetic algorithm showed that it is better to couple the two objectives in a single objective genetic algorithm. Conclusion A computationally tractable framework is presented for the redesign of metabolic networks for maximal product formation combining elementary mode analysis (a form of convex analysis), pathway thermodynamics, and a genetic algorithm to optimize the production of two industrially-relevant products, ethanol and lycopene, from E. coli. The designed algorithm can be applied to any small-scale model of cellular metabolism theoretically utilizing any substrate and applied towards the production of any product.

2010-01-01

250

An Optimal Approximation Algorithm for Bayesian Inference  

Microsoft Academic Search

Approximating the inference probability Pr[X = xjE = e] in any sense, even fora single evidence node E, is NP-hard. This result holds for belief networks that areallowed to contain extreme conditional probabilities---that is, conditional probabilitiesarbitrarily close to 0. Nevertheless, all previous approximation algorithms have failedto approximate efficiently many inferences, even for belief networks without extremeconditional probabilities.We prove that we

Paul Dagum; Michael Luby

1997-01-01

251

Environmental Optimization: Applications of Genetic Algorithms  

Microsoft Academic Search

The genetic algorithm (GA) has found wide acceptance in many fields, ranging from economics through engineering. In the environmental\\u000a sciences, some disciplines are using GAs regularly as a tool to solve typical problems; while in other areas, they have hardly\\u000a been assessed for use in research projects. The key to using GAs in environmental sciences is to pose the problem

Sue Ellen Haupt

252

Simulation and Optimization of Asynchronous AC Motor Control by Particle Swarm Optimization (PSO) and Emperor Algorithm  

Microsoft Academic Search

In the present paper, the simulation and optimization of asynchronous AC motor through its controlling and modeling by convert d-q with Simulink of Mat lab Software has been studied. By utilization of classic PI controller and also with phase controller has been optimized which membership function center of it has been optimized by new intelligent algorithms such as Emperor and

Masoud Nabipour Afrozi; Masoud Hassanpour Aghdam; Ahmad Naebi; Saeed Hassanpour Aghdam

2011-01-01

253

A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram  

Microsoft Academic Search

The coverage problem is a crucial issue in wireless sensor networks (WSN), where a high coverage rate ensures a high quality of service of the WSN. This paper proposes a new algorithm to optimize sensor coverage using particle swarm optimization (PSO) and Voronoi diagram. PSO is used to find the optimal deployment of the sensors that gives the best coverage

N. A. B. A. Aziz; A. W. Mohemmed; M. Y. Alias

2009-01-01

254

Direct search algorithms for optimization calculations  

Microsoft Academic Search

: Many different procedures have been proposed for optimization calculationswhen first derivatives are not available. Further, several researchers havecontributed to the subject, including some who wish to prove convergence theorems,and some who wish to make any reduction in the least calculated valueof the objective function. There is not even a key idea that can be used as afoundation of a

M. J. D. Powell

1998-01-01

255

Optimal and Suboptimal Singleton Arc Consistency Algorithms  

Microsoft Academic Search

Singleton arc consistency (SAC) enhances the pruning capability of arc consistency by ensuring that the network cannot become arc inconsistent af- ter the assignment of a value to a variable. Algo- rithms have already been proposed to enforce SAC, but they are far from optimal time complexity. We give a lower bound to the time complexity of en- forcing SAC,

Christian Bessière; Romuald Debruyne

2005-01-01

256

Optimal Step-Size Constant Modulus Algorithm  

Microsoft Academic Search

The step size leading to the absolute minimum of the constant modulus (CM) criterion along the search direction can be obtained algebraically at each iteration among the roots of a third-degree polynomial. The resulting optimal step-size CMA (OS-CMA) is compared with other CM-based iterative techniques in terms of performance-versus-complexity trade-off.

Vicente Zarzoso; Pierre Comon

2008-01-01

257

Boosted sampling: approximation algorithms for stochastic optimization  

Microsoft Academic Search

Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the Steiner Tree problem, for example, edges must be chosen to connect terminals (clients); in Vertex Cover, vertices must be chosen to cover edges (clients); in Facility Location, facilities must be chosen and demand vertices (clients) connected to

Anupam Gupta; Martin Pál; R. Ravi; Amitabh Sinha

2004-01-01

258

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

259

Multimodal optimization using a bi-objective evolutionary algorithm.  

PubMed

In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are deemphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable bi-objective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500 optima. Most past multimodal EA studies considered problems having only a few variables. In this paper, we have solved up to 16-variable test problems having as many as 48 optimal solutions and for the first time suggested multimodal constrained test problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application. PMID:21591888

Deb, Kalyanmoy; Saha, Amit

2011-12-02

260

SamACO: variable sampling ant colony optimization algorithm for continuous optimization.  

PubMed

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising. PMID:20371409

Hu, Xiao-Min; Zhang, Jun; Chung, Henry Shu-Hung; Li, Yun; Liu, Ou

2010-04-05

261

A Near Optimal Isosurface Extraction Algorithm Using the Span Space  

Microsoft Academic Search

We present the "Near Optimal IsoSurface Extraction"(NOISE) algorithm for rapidly extracting isosurfaces fromstructured and unstructured grids. Using the span space, anew representation of the underlying domain, we developan isosurface extraction algorithm with a worst case complexityof O(pn + k) for the search phase, where n is the sizeof the data set and k is the number of cells intersected bythe

Yarden Livnat; Han-wei Shen; Christopher R. Johnson

1996-01-01

262

Multiobjective Optimization by a Modified Artificial Immune System Algorithm  

Microsoft Academic Search

http:\\/\\/www.polito.it\\/cadema Abstract. The aim of this work is to propose and validate a new multi- objective optimization algorithm based on the emulation of the immune system behavior. The rationale of this work is that the artificial im- mune system has, in its elementary structure, the main features required by other multiobjective evolutionary algorithms described in literature. The proposed approach is

Fabio Freschi; Maurizio Repetto

2005-01-01

263

Optimization algorithms intended for self-tuning feedwater heater model  

NASA Astrophysics Data System (ADS)

This work presents a self-tuning feedwater heater model. This work continues the work on first-principle gray-box methodology applied to diagnostics and condition assessment of power plant components. The objective of this work is to review and benchmark the optimization algorithms regarding the time required to achieve the best model fit to operational power plant data. The paper recommends the most effective algorithm to be used in the model adjustment process.

Czop, P.; Barszcz, T.; Bednarz, J.

2013-06-01

264

Genetic algorithm for multi-objective experimental optimization  

PubMed Central

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations).

Link, Hannes

2006-01-01

265

Searching for empty convex polygons  

Microsoft Academic Search

A key problem in computational geometry is the identification of subsets of a point set having particular properties. We study this problem for the properties of convexity and emptiness. We show that finding empty triangles is related to the problem of determining pairs of vertices that see each other in a star-shaped polygon. A linear time algorithm for this problem

David P. Dobkin; Herbert Edelsbrunner; Mark H. Overmars

1988-01-01

266

Efficient Distance Computation Between Non-Convex Objects  

Microsoft Academic Search

This paper describes an efficient algorithm for comput- ing the distance between non-convex objects. Objects are modeled as the union of a set of convex components. From this model we construct a hierarchical bounding representation based on spheres. The distance between objects is determined by computing the distance between pairs of convex components using preexisting techniques. The key to efficiency

Sean Quinlan

1994-01-01

267

On Optimal Parameters for Ant Colony Optimization Algorithms  

Microsoft Academic Search

Ant Colony Optimization (ACO) is a meta- heuristic introduced by Dorigo et al. (9) which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. In this paper we analyse the parameter settings of the ACO algo- rithm. These determine the behaviour of each ant and are critical for fast

Dorian Gaertner; Keith L. Clark

2005-01-01

268

A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)  

NASA Astrophysics Data System (ADS)

Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.

Cantó, J.; Curiel, S.; Martínez-Gómez, E.

2009-07-01

269

Multi-Objective Optimization of Spectra Using Genetic Algorithms  

Microsoft Academic Search

This paper applies genetic algorithms (GAs), a powerful general-purpose biologically motivated optimization technique, to the multi-objective problem of spectrum optimization. Two objectives, color and efficiency, are address using real spectra, although the addition of other objectives (e.g., color rendering, color temperature) is relatively straightforward. The direct application of the method presented is to transform the spectrum of newly developed lighting

Neil H. Eklund; Oak Grove Scientific; Mark J. Embrechts

270

Steady-state genetic algorithms for discrete optimization of trusses  

Microsoft Academic Search

This paper presents the applications of steady-state genetic algorithms to discrete optimization of trusses. It is mathematically formulated as a constrained nonlinear optimization problem with discrete design variables. Discrete design variables are treated by a two-stage mapping process which is constructed by the mapping relationships between unsigned decimal integers and discrete values. With small generation gap and careful modification, steady-state

Shyue-Jian Wu; Pei-Tse Chow

1995-01-01

271

Optimal Scheduling of Multiple Dam System Using Harmony Search Algorithm  

Microsoft Academic Search

Musician’s behavior-inspired harmony search (HS) algorithm was first applied to the optimal operation scheduling of a multiple\\u000a dam system. The HS model tackled a popular benchmark system with four dams. Results showed that the HS model found five different\\u000a global optimal solutions with identical maximum benefit from hydropower generation and irrigation, while enhanced GA model\\u000a (real-value coding, tournament selection, uniform

Zong Woo Geem

2007-01-01

272

Infeasibility Driven Evolutionary Algorithm (IDEA) for Engineering Design Optimization  

Microsoft Academic Search

Engineering design often requires solutions to constrained optimization problems with highly nonlinear objective and constraint\\u000a functions. The optimal solutions of most design problems lie on the constraint boundary. In this paper, Infeasibility Driven\\u000a Evolutionary Algorithm (IDEA) is presented that searches for optimum solutions near the constraint boundary. IDEA explicitly\\u000a maintains and evolves a small proportion of infeasible solutions. This behavior is

Hemant K. Singh; Amitay Isaacs; Tapabrata Ray; Warren Smith

2008-01-01

273

A New Local Search Based Ant Colony Optimization Algorithm for Solving Combinatorial Optimization Problems  

NASA Astrophysics Data System (ADS)

Ant Colony Optimization (ACO) algorithms are a new branch of swarm intelligence. They have been applied to solve different combinatorial optimization problems successfully. Their performance is very promising when they solve small problem instances. However, the algorithms' time complexity increase and solution quality decrease for large problem instances. So, it is crucial to reduce the time requirement and at the same time to increase the solution quality for solving large combinatorial optimization problems by the ACO algorithms. This paper introduces a Local Search based ACO algorithm (LSACO), a new algorithm to solve large combinatorial optimization problems. The basis of LSACO is to apply an adaptive local search method to improve the solution quality. This local search automatically determines the number of edges to exchange during the execution of the algorithm. LSACO also applies pheromone updating rule and constructs solutions in a new way so as to decrease the convergence time. The performance of LSACO has been evaluated on a number of benchmark combinatorial optimization problems and results are compared with several existing ACO algorithms. Experimental results show that LSACO is able to produce good quality solutions with a higher rate of convergence for most of the problems.

Hassan, Md. Rakib; Islam, Md. Monirul; Murase, Kazuyuki

274

Optimal and sub-optimal maximum a posteriori algorithms suitable for turbo decoding  

Microsoft Academic Search

For estimating the states or outputs of a Markov process, the symbol-by-symbol maximum a posteriori(MAP) algorithm is optimal. However, this algorithm, even in its recursive form, poses technical difficultiesbecause of numerical representation problems, the necessity of non-linear functions and a high number ofadditions and multiplications. MAP like algorithms operating in the logarithmic domain presented in thepast solve the numerical problem

Patrick Robertson; Peter Hoeher; Emmanuelle Villebrun

1997-01-01

275

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

276

Heuristic optimality criterion algorithm for shape design of fluid flow  

NASA Astrophysics Data System (ADS)

This paper presents a heuristic optimality criterion algorithm for shape design of fluid flow. In this algorithm, the lattice Boltzmann method (LBM) is utilized to calculate the flow field of a fluid domain which is divided into elemental cells. A heuristic optimality criterion is applied for cells at the solid-fluid interface, i.e. the dynamic pressure for fluid cells and the viscous stress on their neighboring solid cells. An automatic program is processed step by step to exchange the positions of solid and fluid cells identified by the optimality criterion, with the objective of decreasing the flow resistance at the constraint of constant fluid volume. To illustrate the procedure of this algorithm for shape design of fluid flow, two simple examples are presented: one with fluid flowing through a right angle elbow and the other through a converging T-junction. Numerical results show that this algorithm can successfully reduce the total pressure drop of the system, demonstrating its potential applications in engineering optimal design.

Wang, Limin; Fan, Yilin; Luo, Lingai

2010-10-01

277

Parallel evolutionary algorithms for optimization problems in aerospace engineering  

Microsoft Academic Search

This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the notion of interconnected sub-populations evolving independently. Previous studies have shown the advantages of introducing a multi-layered hierarchical topology in parallel GAs. Such a topology allows the use of

J. F. Wang; J. Periaux; M. Sefrioui

2002-01-01

278

Learning Fuzzy Rules Using Ant Colony Optimization Algorithms 1  

Microsoft Academic Search

Within the Linguistic Modeling Þeld, one of the most important applications of Fuzzy Rule-Based Systems, the automatic learning from numerical data of the fuzzy linguistic rules composing these systems is an important task. In this paper we introduce a novel way of addressing the problem making use of Ant Colony Optimization (ACO) algorithms. To do so, the learning task will

Jorge Casillas; Oscar Cordon; Francisco Herrera

2000-01-01

279

Runtime Analysis of a Simple Ant Colony Optimization Algorithm  

Microsoft Academic Search

Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical founda- tion of this randomized search heuristic is rather weak. Building up such a theory is demanded to understand how these heuristics work as well as to come up with better algorithms for certain problems. Up to now, only convergence results

Frank Neumann; Carsten Witt

2006-01-01

280

Particle swarm optimization algorithm for emergency resource allocation on expressway  

Microsoft Academic Search

In order to allocate traffic emergency rescue resources on expressway, considering rescue time and resources costs as the objective, stochastic variables are introduced into constraints and a corresponding stochastic programming model is established, due to the stochastic resource requirements of accidents. Because of large numbers of rescue depots and black-spots, a stochastic simulation of particle swarm optimization (PSO) algorithm is

Chai Gan; Sun Ying-ying; Zhu Cang-hui

2009-01-01

281

Genetic algorithm design of Pareto optimal broadband microwave absorbers  

Microsoft Academic Search

The concept of Pareto optimality is applied to the study of choice tradeoffs between reflectivity and thickness in the design of multilayer microwave absorbers. Absorbers composed of a given number of layers of absorbing materials selected from a predefined database of available materials are considered. Three types of Pareto genetic algorithms for absorber synthesis are introduced and compared to each

D. S. Weile; E. Michielssen; D. E. Goldberg

1996-01-01

282

Novel Algorithms for Optimal Compression Using Classification Metrics  

Microsoft Academic Search

In image processing, classification and compression are very common operations. Compression and classification algorithms are conventionally independent of each other and performed sequentially. However, some class distinctions may be lost after a minimum distortion compression. In this paper, two new schemes are developed that combine the compression and classification operations in order to optimize some classification metrics. In other words,

Bei Xie; T. Bose; E. Merenyi

2008-01-01

283

An optimal partition algorithm for minimization of paging costs  

Microsoft Academic Search

A novel paging scheme under delay bounds is proposed for personal communication systems. This paging scheme is independent of the location probability distributions of the mobile users and satisfies the delay bounds, while minimizing the amount of bandwidth used for locating a mobile user. The proposed paging scheme includes the optimal partition algorithm and paging procedure with respect to paging

Wenye Wang; I. F. Akyildiz; G. L. Stuber

2000-01-01

284

Global Structural Optimizations of Surface Systems with a Genetic Algorithm.  

National Technical Information Service (NTIS)

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) 7x7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstruc...

F. Chuang

2005-01-01

285

Fractional Order Dynamics in a Particle Swarm Optimization Algorithm  

Microsoft Academic Search

This article reports the study of fractional dynamics during the evolution of a particle swarm optimization (PSO) algorithm. Some initial swarm particles are randomly changed, for stimulating the system response, and its effect is compared with a non-perturbed reference. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behavior of the best particle.

E. J. Solteiro Pires; P. B. de Oliveira; J. A. T. Machado; I. S. Jesus

2007-01-01

286

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

287

The particle swarm optimization algorithm: convergence analysis and parameter selection  

Microsoft Academic Search

The particle swarm optimization algorithm is analyzed using standard results from the dynamic system theory. Graphical parameter selection guidelines are derived. The exploration–exploitation tradeoff is discussed and illustrated. Examples of performance on benchmark functions superior to previously published results are given.

Ioan Cristian Trelea

2003-01-01

288

Improving ant colony optimization algorithm for data clustering  

Microsoft Academic Search

Data mining is a process that uses technology to bridge the gap between data and logical decision-making. The jargon itself offers a promising view of organized data manipulation for extracting valuable information and knowledge from high volume of data. Copious techniques are developed to fulfill this aspiration. This paper outlines an ant colony optimization algorithm which is used newly in

R. Tiwari; M. Husain; S. Gupta; A. Srivastava

2010-01-01

289

Optimal operation of pipeline systems using Genetic Algorithm  

Microsoft Academic Search

A Genetic Algorithm (GA) is used in this paper for the optimal operation, result in better solution than the existing one, of the pipeline systems under transient conditions caused by valve closure. Simulation of pipeline system is carried out here by the Implicit Method of Characteristics, a method recently developed and introduced by the authors. This method uses an element-wise

Mohamad Hadi Afshar; Maryam Rohani

2009-01-01

290

Optimization of Construction of Tire Reinforcement by Genetic Algorithm  

Microsoft Academic Search

A new tire design procedure capable of determining the optimum tire construction was developed by combining a finite element method approach with mathematical programming and a genetic algorithm (GA). Both procedures successfully generated optimized belt structures. The design variables in the mathematical programming were belt angle and belt width. Using the merits of a GA which enabled the use of

Akihiko Abe; Tatsuihko Kamegawa; Yukio Nakajima

2004-01-01

291

An Optimal Algorithm for Intersecting Line Segments in the Plane  

Microsoft Academic Search

Themain contribution ofthiswork is an O(nlogr~ +k)-timeal gorithmfo rcomputingall k intersections among n line segments in the plane, This time complexity IS easdy shown to be optimal. Within thesame asymptotic cost, ouralgorithm canalso construct thesubdiwslon of theplancdefmed by the segments and compute which segment (if any) lies right above (or below) each intersection and each endpoint. The algorithm has been

Bernard Chazelle; Herbert Edelsbrunner

1988-01-01

292

Autonomous Distributed Optimization Algorithm for Intelligent Lighting System  

Microsoft Academic Search

In recent years, various types of equipment have become more intelligent. In this research, we propose an intelligent lighting system for providing required illuminance to specified locations, and develop autonomous distributed optimization algorithm which enables advanced lighting control. This system consists of multiple intelligent lighting fixtures, multiple movable illumination sensors and a power meter connected to a network. There is

Keiko Ono; Mitsunori Miki; Motoi Yonezawa

2010-01-01

293

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

294

Novel precompression rate-distortion optimization algorithm for JPEG 2000  

NASA Astrophysics Data System (ADS)

In this paper, a novel pre-compression rate-distortion optimization algorithm is proposed, which can reduce computation power and memory requirement of JPEG 2000 encoder. It can reduce the wasted computational power of the entropy coder (EBCOT Tier-1) and unnecessary memory requirement for the code-stream. Distortion and rate of coding passes are calculated and estimated before coding, and therefore truncation point is selected before coding. Experimental results show that the computation time of EBCOT Tier-1 and memory requirement for the code-stream can be greatly reduced,especially at high compression ratio. The quality of the proposed algorithm is slightly lower than that of post-compression rate-distortion optimization algorithm.

Chang, Yu-Wei; Fang, Hung-Chi; Lian, Chung-Jr; Chen, Liang-Gee

2004-01-01

295

Endgame implementations for the Efficient Global Optimization (EGO) algorithm  

NASA Astrophysics Data System (ADS)

Efficient Global Optimization (EGO) is a competent evolutionary algorithm which can be useful for problems with expensive cost functions [1,2,3,4,5]. The goal is to find the global minimum using as few function evaluations as possible. Our research indicates that EGO requires far fewer evaluations than genetic algorithms (GAs). However, both algorithms do not always drill down to the absolute minimum, therefore the addition of a final local search technique is indicated. In this paper, we introduce three "endgame" techniques. The techniques can improve optimization efficiency (fewer cost function evaluations) and, if required, they can provide very accurate estimates of the global minimum. We also report results using a different cost function than the one previously used [2,3].

Southall, Hugh L.; O'Donnell, Teresa H.; Kaanta, Bryan

2009-05-01

296

Logit Model based Performance Analysis of an Optimization Algorithm  

NASA Astrophysics Data System (ADS)

In this paper, the performance of the Multi Dynamics Algorithm for Global Optimization (MAGO) is studied through simulation using five standard test functions. To guarantee that the algorithm converges to a global optimum, a set of experiments searching for the best combination between the only two MAGO parameters -number of iterations and number of potential solutions, are considered. These parameters are sequentially varied, while increasing the dimension of several test functions, and performance curves were obtained. The MAGO was originally designed to perform well with small populations; therefore, the self-adaptation task with small populations is more challenging while the problem dimension is higher. The results showed that the convergence probability to an optimal solution increases according to growing patterns of the number of iterations and the number of potential solutions. However, the success rates slow down when the dimension of the problem escalates. Logit Model is used to determine the mutual effects between the parameters of the algorithm.

Hernández, J. A.; Ospina, J. D.; Villada, D.

2011-09-01

297

RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.

Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay

298

Optimal brushless DC motor design using genetic algorithms  

NASA Astrophysics Data System (ADS)

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.

Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.

2010-11-01

299

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

300

Approximating convex Pareto surfaces in multiobjective radiotherapy planning  

SciTech Connect

Radiotherapy planning involves inherent tradeoffs: the primary mission, to treat the tumor with a high, uniform dose, is in conflict with normal tissue sparing. We seek to understand these tradeoffs on a case-to-case basis, by computing for each patient a database of Pareto optimal plans. A treatment plan is Pareto optimal if there does not exist another plan which is better in every measurable dimension. The set of all such plans is called the Pareto optimal surface. This article presents an algorithm for computing well distributed points on the (convex) Pareto optimal surface of a multiobjective programming problem. The algorithm is applied to intensity-modulated radiation therapy inverse planning problems, and results of a prostate case and a skull base case are presented, in three and four dimensions, investigating tradeoffs between tumor coverage and critical organ sparing.

Craft, David L.; Halabi, Tarek F.; Shih, Helen A.; Bortfeld, Thomas R. [Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114 (United States)

2006-09-15

301

Genetic algorithms and their statistical applications: an introduction  

Microsoft Academic Search

Genetic algorithms (GA) are stochastic optimization tools that work on “Darwinian” models of population biology and are capable of solving for near-optimal solution for multivariable functions without the usual mathematical requirements of strict continuity, differentiability, convexity and other properties. The algorithm begins by choosing a large number of candidate solutions which propagate themselves through a “selection criteria” and are changed

Sangit Chatterjee; Matthew Laudato; Lucy A. Lynch

1996-01-01

302

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

303

Penalty adapting ant algorithm: application to pipe network optimization  

NASA Astrophysics Data System (ADS)

A penalty adapting ant algorithm is presented in an attempt to eliminate the dependency of ant algorithms on the penalty parameter used for the solution of constrained optimization problems. The method uses an adapting mechanism for determination of the penalty parameter leading to elimination of the costly process of penalty parameter tuning. The method is devised on the basis of observation that for large penalty parameters, infeasible solutions will have a higher total cost than feasible solutions and vice versa. The method therefore uses the best feasible and infeasible solution costs of the iteration to adaptively adjust the penalty parameter to be used in the next iteration. The pheromone updating procedure of the max-min ant system is also modified to keep ants on and around the boundary of the feasible search space where quality solutions can be found. The sensitivity of the proposed method to the initial value of the penalty parameter is investigated and indicates that the method converges to optimal or near-optimal solutions irrespective of the initial starting value of the penalty parameter. This is significant as it eliminates the need for sensitivity analysis of the method with respect to the penalty factor, thus adding to the computational efficiency of ant algorithms. Furthermore, it is shown that the success rate of the search algorithm in locating an optimal solution is increased when a self-adapting mechanism is used. The presented method is applied to a benchmark pipe network optimization problem in the literature and the results are presented and compared with those of existing algorithms.

Afshar, M. H.

2008-10-01

304

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

305

Optimization of circuits using a constructive learning algorithm  

SciTech Connect

The paper presents an application of a constructive learning algorithm to optimization of circuits. For a given Boolean function f. a fresh constructive learning algorithm builds circuits belonging to the smallest F{sub n,m} class of functions (n inputs and having m groups of ones in their truth table). The constructive proofs, which show how arbitrary Boolean functions can be implemented by this algorithm, are shortly enumerated An interesting aspect is that the algorithm can be used for generating both classical Boolean circuits and threshold gate circuits (i.e. analogue inputs and digital outputs), or a mixture of them, thus taking advantage of mixed analogue/digital technologies. One illustrative example is detailed The size and the area of the different circuits are compared (special cost functions can be used to closer estimate the area and the delay of VLSI implementations). Conclusions and further directions of research are ending the paper.

Beiu, V.

1997-05-01

306

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

307

Modeling IrisCode and its variants as convex polyhedral cones and its security implications.  

PubMed

IrisCode, developed by Daugman, in 1993, is the most influential iris recognition algorithm. A thorough understanding of IrisCode is essential, because over 100 million persons have been enrolled by this algorithm and many biometric personal identification and template protection methods have been developed based on IrisCode. This paper indicates that a template produced by IrisCode or its variants is a convex polyhedral cone in a hyperspace. Its central ray, being a rough representation of the original biometric signal, can be computed by a simple algorithm, which can often be implemented in one Matlab command line. The central ray is an expected ray and also an optimal ray of an objective function on a group of distributions. This algorithm is derived from geometric properties of a convex polyhedral cone but does not rely on any prior knowledge (e.g., iris images). The experimental results show that biometric templates, including iris and palmprint templates, produced by different recognition methods can be matched through the central rays in their convex polyhedral cones and that templates protected by a method extended from IrisCode can be broken into. These experimental results indicate that, without a thorough security analysis, convex polyhedral cone templates cannot be assumed secure. Additionally, the simplicity of the algorithm implies that even junior hackers without knowledge of advanced image processing and biometric databases can still break into protected templates and reveal relationships among templates produced by different recognition methods. PMID:23193454

Kong, Adams Wai-Kin

2012-11-16

308

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.

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

2009-01-01

309

Coupled Aerostructural Design Optimization Using the Kriging Model and Integrated Multiobjective Optimization Algorithm  

Microsoft Academic Search

The paper develops and implements a highly applicable framework for the computation of coupled aerostructural design optimization.\\u000a The multidisciplinary aerostructural design optimization is carried out and validated for a tested wing and can be easily\\u000a extended to complex and practical design problems. To make the framework practical, the study utilizes a high-fidelity fluid\\/structure\\u000a interface and robust optimization algorithms for an

X. B. Lam; Y. S. Kim; A. D. Hoang; C. W. Park

2009-01-01

310

Optimization of Circular Ring Microstrip Antenna Using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

Circular ring microstrip antennas have several interesting properties that make it attractive in wireless applications. Although several analysis techniques such as cavity model, generalized transmission line model, Fourier-Hankel transform domain and the method of matched asymptotic expansion have been studied by researchers, there is no efficient design tool that has been incorporated with a suitable optimization algorithm. In this paper, the cavity model analysis along with the genetic optimization algorithm is presented for the design of circular ring microstrip antennas. The method studied here is based on the well-known cavity model and the optimization of the dimensions and feed point location of the circular ring antenna is performed via the genetic optimization algorithm, to achieve an acceptable antenna operation around a desired resonance frequency. The antennas designed by this efficient design procedure were realized experimentally, and the results are compared. In addition, these results are also compared to the results obtained by the commercial electromagnetic simulation tool, the FEM based software, HFSS by ANSOFT.

Sathi, V.; Ghobadi, Ch.; Nourinia, J.

2008-10-01

311

Facial Skin Segmentation Using Bacterial Foraging Optimization Algorithm  

PubMed Central

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%).

Bakhshali, Mohamad Amin; Shamsi, Mousa

2012-01-01

312

A filter-based evolutionary algorithm for constrained optimization.  

SciTech Connect

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 convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.

Clevenger, Lauren M. (University of New Mexico); Hart, William Eugene; Ferguson, Lauren Ann (Texas Tech University)

2004-02-01

313

GMG - A guaranteed global optimization algorithm: Application to remote sensing  

SciTech Connect

We investigate the role of additional information in reducing the computational complexity of the global optimization problem (GOP). Following this approach, we develop GMG -- an algorithm to find the Global Minimum with a Guarantee. The new algorithm breaks up an originally continuous GOP into a discrete (grid) search problem followed by a descent problem. The discrete search identifies the basin of attraction of the global minimum after which the actual location of the minimizer is found upon applying a descent algorithm. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions. We then illustrate the performance of the the validated algorithm on a simple realization of the monocular passive ranging (MPR) problem in remote sensing, which consists of identifying the range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem is set as a GOP whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. We solve the GOP using GMG and report on the performance of the algorithm.

D'Helon, Cassius [ORNL; Protopopescu, Vladimir A [ORNL; Wells, Jack C [ORNL; Barhen, Jacob [ORNL

2007-01-01

314

Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm  

NASA Astrophysics Data System (ADS)

This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.

Wen, Feng; Gen, Mitsuo; Yu, Xinjie

315

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

316

Runtime Analysis of a Simple Ant Colony Optimization Algorithm  

Microsoft Academic Search

Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical\\u000a foundation of this randomized search heuristic is rather weak. Building up such a theory is demanded to understand how these\\u000a heuristics work as well as to come up with better algorithms for certain problems. Up to now, only convergence results have

Frank Neumann; Carsten Witt

2009-01-01

317

Optimized design of embedded DSP system hardware supporting complex algorithms  

Microsoft Academic Search

The paper presents an optimized design method for a flexible and economical embedded DSP system that can implement complex processing algorithms as biometric recognition, real-time image processing, etc. It consists of a floating-point DSP, 512 Kbytes data RAM, 1 Mbytes FLASH program memory, a CPLD for achieving flexible logic control of input channel and a RS-485 transceiver for local network

Yanhua Li; Xiangjun Wang; Xinling Zhou

2003-01-01

318

A Non-Generational Genetic Algorithm for Multiobjective Optimization  

Microsoft Academic Search

This paper describes a non-generational geneticalgorithm for multiobjective optimization.The fitness of each individual in thepopulation is calculated incrementally basedon the degree in which it is dominated inthe Pareto sense, or close to other individuals.The closeness of individuals is measuredusing a sharing function. The performanceof the algorithm presented is comparedto previous efforts on three multiobjectiveoptimization problems of growing difficulty.The behavior ...

Manuel Valenzuela-rendón; Eduardo Uresti-charre

1997-01-01

319

Optimal Trajectory of Robot Manipulator Using Harmony Search Algorithms  

Microsoft Academic Search

This research proposes a method of obtaining an optimal trajectory of robot manipulator by using Harmony Search (HS). Despite\\u000a the fact that the Sequential Quadratic Programming (SQP) is popular as a solving method for optimum trajectory problems, SQP\\u000a needs a suitable initial value. The HS algorithm does however not require such process of setting initial value. Two results\\u000a are compared

Panwadee Tangpattanakul; Anupap Meesomboon; Pramin Artrit

2010-01-01

320

Toward Virtual Machine Packing Optimization Based on Genetic Algorithm  

Microsoft Academic Search

To enable efficient resource provisioning in HaaS (Hardware as a Service) cloud systems, virtual machine packing, which migrate\\u000a virtual machines to minimize running real node, is essential. The virtual machine packing problem is a multi-objective optimization\\u000a problem with several parameters and weights on parameters change dynamically subject to cloud provider preference. We propose\\u000a to employ Genetic Algorithm (GA) method, that

Hidemoto Nakada; Takahiro Hirofuchi; Hirotaka Ogawa; Satoshi Itoh

2009-01-01

321

Optimization of Heat Exchanger Enhanced Surfaces through Multiobjective Genetic Algorithms  

Microsoft Academic Search

Heat transfer enhancing surfaces are of interest for a wide range of industrial applications. The aim of this article is to provide a robust automated method for the design of two-dimensional enhanced surfaces. Multiobjective optimization algorithms are employed; the competing objectives addressed are the maximization of the heat transfer and the minimization of the pressure drop for Re = 1,000 and Pr = 0.74.

M. Cavazzuti; M. A. Corticelli

2008-01-01

322

Pareto optimization using the struggle genetic crowding algorithm  

Microsoft Academic Search

Many real-world engineering design problems involve the simultaneous optimization of several conflicting objectives. In this paper, a method combining the struggle genetic crowding algorithm with Pareto-based population ranking is proposed to elicit trade-off frontiers. The new method has been tested on a variety of published problems, reliably locating both discontinuous Pareto frontiers as well as multiple Pareto frontiers in multi-modal

Johan Andersson; David Wallace

2002-01-01

323

Managing and learning with multiple models: Objectives and optimization algorithms  

USGS Publications Warehouse

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

Probert, W. J. M.; Hauser, C. E.; McDonald-Madden, E.; Runge, M. C.; Baxter, P. W. J.; Possingham, H. P.

2011-01-01

324

Parallel algorithms for integrated structural/control optimization  

NASA Astrophysics Data System (ADS)

Optimization of combined structural and control systems is a complex problem requiring an inordinate amount of computer-processing time, especially the solution of the eigenvalue problem of a general unsymmetric square real matrix with complex eigenvalues and eigenvectors, which is frequently used in such problem. The few algorithms presented in the literature thus far have been applied to small structures with a few members and controllers only. Parallel processing on new-generation multiprocessor computers provides an opportunity to solve large-scale problems. In this paper, the integrated structural and control optimization problem is formulated by including constraints on displacements, stresses, and closed-loop eigenvalues and the corresponding damping factors. Then, parallel algorithms are presented for integrated optimization of structures on shared-memory multiprocessors such as the Cray YMP 8/864 supercomputer. In particular, parallel algorithms are presented for the solution of complex eigenvalue problems encountered in structural control problems using the method of matrix iteration for dominant eigenvalue(s). The solution is divided into two parts. The first part is the iteration for dominant eigenvalue(s) and the corresponding eigenvector(s) and the second part is the reduction of the matrix to obtain the smaller eigenvalue(s) and the corresponding eigenvector(s).

Saleh, A.; Adeli, H.

1994-07-01

325

Threshold matrix for digital halftoning by genetic algorithm optimization  

NASA Astrophysics Data System (ADS)

Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.

Alander, Jarmo T.; Mantere, Timo; Pyylampi, Tero

1998-10-01

326

Economic Dispatch of Power Systems Based on the Modified Particle Swarm Optimization Algorithm  

Microsoft Academic Search

This paper presents a new versatile optimization algorithm called modified particle swarm optimization algorithm (MPSO) for solving the economic dispatch (ED) problem of power systems. Compared with the classical PSO algorithm, several modified operators, such as mutation operator and neighborhood magnifying operator, are employed by MPSO to ensure the algorithm search globally in theory. Based on the stochastic analysis theorem,

Yun-He Hou; Li-Juan Lu; Xin-Yin Xiong; Yao-Wu Wu

2005-01-01

327

Design of Optimal Digital IIR Filters by using a Bandwidth Adaptive Harmony Search Algorithm  

Microsoft Academic Search

Evolutionary optimization algorithms have been recently applied to optimal digital IIR filter design. In this paper, we apply a Bandwidth Adaptive Harmony Search (BAHS) algorithm to the design of 1- dimensional IIR filters. Harmony Search is an evolutionary algorithm, which emulates the improvisation process of musicians. We have modified the algorithm by setting the bandwidth equal to the standard deviation

Sayan Ghosh; Debarati Kundu; Kaushik Suresh; Swagatam Das; Ajith Abraham

2009-01-01

328

An Improved Algorithm for Searching Local Optimal Path in Intelligent Transport System  

Microsoft Academic Search

This paper provides an improved search algorithm for optimal route planning by rebuilding the search area in intelligent transport system. The theory foundation is that the classical Dijsktra algorithm has not any directional feature during searching the optimal path, and the bidirectional Dijsktra has its own limit. Based on the analysis of the two algorithms, the new improved algorithm proposed

Zhide Liu; Jiabin Chen; Chunlei Song; Lu Ding

2008-01-01

329

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

330

Ordinal Hill Climbing Algorithms for Discrete Manufacturing Process Design Optimization Problems  

Microsoft Academic Search

Thispaper introduces ordinal hill climbing algorithms for addressingdiscrete manufacturing process design optimization problems usingcomputer simulation models. Ordinal hill climbing algorithmscombine the search space reduction feature of ordinal optimizationwith the global search feature of generalized hill climbing algorithms.By iteratively applying the ordinal optimization strategy withinthe generalized hill climbing algorithm framework, the resultinghybrid algorithm can be applied to intractable discrete optimizationproblems. Computational

Kelly A. Sullivan; Sheldon H. Jacobson

2000-01-01

331

Optimization of Optical Systems Using Genetic Algorithms: a Comparison Among Different Implementations of The Algorithm  

NASA Astrophysics Data System (ADS)

The Genetic Algorithms, GAs, are a method of global optimization that we use in the stage of optimization in the design of optical systems. In the case of optical design and optimization, the efficiency and convergence speed of GAs are related with merit function, crossover operator, and mutation operator. In this study we present a comparison between several genetic algorithms implementations using different optical systems, like achromatic cemented doublet, air spaced doublet and telescopes. We do the comparison varying the type of design parameters and the number of parameters to be optimized. We also implement the GAs using discreet parameters with binary chains and with continuous parameter using real numbers in the chromosome; analyzing the differences in the time taken to find the solution and the precision in the results between discreet and continuous parameters. Additionally, we use different merit function to optimize the same optical system. We present the obtained results in tables, graphics and a detailed example; and of the comparison we conclude which is the best way to implement GAs for design and optimization optical system. The programs developed for this work were made using the C programming language and OSLO for the simulation of the optical systems.

López-Medina, Mario E.; Vázquez-Montiel, Sergio; Herrera-Vázquez, Joel

2008-04-01

332

[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

333

Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning  

Microsoft Academic Search

The development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the

Eugene Y. C. Wong; Henry S. C. Yeung; Henry Y. K. Lau

2009-01-01

334

The conversion of limited-entry decision tables to optimal and near-optimal flowcharts: two new algorithms  

Microsoft Academic Search

Two new algorithms for deriving optimal and near-optimal flowcharts from limited entry decision tables are presented. Both take into account rule frequencies and the time needed to test conditions. One of the algorithms, called the optimum-finding algorithm, leads to a flowchart which truly minimizes execution time for a decision table in which simple rules are already contracted to complex rules.

M. Verhelst

1972-01-01

335

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

336

Reconfiguring convex polygons  

Microsoft Academic Search

We prove that there is a motion from any convex polygon to any convex polygon with the same counterclockwise sequence of edge lengths, that preserves the lengths of the edges, and keeps the polygon convex at all times. Furthermore, the motion is “direct” (avoiding any intermediate canonical configuration like a subdivided triangle) in the sense that each angle changes monotonically

Oswin Aichholzer; Erik D. Demaine; Jeff Erickson; Ferran Hurtado; Mark H. Overmars; Michael A. Soss; Godfried T. Toussaint

2001-01-01

337

Searching for Empty Convex Polygons  

Microsoft Academic Search

A key problem in computational geometry is the identification of subsets of a point set having particular properties. We study\\u000a this problem for the properties of convexity and emptiness. We show that finding empty triangles is related to the problem\\u000a of determining pairs of vertices that see each other in a star-shaped polygon. A linear-time algorithm for this problem which

David P. Dobkin; Herbert Edelsbrunner; Mark H. Overmars

1990-01-01

338

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

339

Multiobjective optimization of steam reformer performance using genetic algorithm  

SciTech Connect

An existing side-fired stream reformer is simulated using a rigorous model with proven reaction kinetics, incorporating aspects of heat transfer in the furnace and diffusion in the catalyst pellet. Thereafter, optimal conditions, which could lead to an improvement in its performance, are obtained. An adaptation of the nondominated sorting genetic algorithm is employed to perform a multiobjective optimization. For a fixed production rate of hydrogen from the unit, the simultaneous minimization of the methane feed rate and the maximization of the flow rate of carbon monoxide in the syngas are chosen as the two objective functions, keeping in mind the processing requirements, heat integration, and economics. For the design configuration considered in this study, sets of Pareto-optimal operating conditions are obtained. The results are expected to enable the engineer to gain useful insights into the process and guide him/her in operating the reformer to minimize processing costs and to maximize profits.

Rajesh, J.K.; Gupta, S.K.; Rangaiah, G.P.; Ray, A.K.

2000-03-01

340

A heterogeneous algorithm for PDT dose optimization for prostate  

NASA Astrophysics Data System (ADS)

The object of this study is to develop optimization procedures that account for both the optical heterogeneity as well as photosensitizer (PS) drug distribution of the patient prostate and thereby enable delivery of uniform photodynamic dose to that gland. We use the heterogeneous optical properties measured for a patient prostate to calculate a light fluence kernel (table). PS distribution is then multiplied with the light fluence kernel to form the PDT dose kernel. The Cimmino feasibility algorithm, which is fast, linear, and always converges reliably, is applied as a search tool to choose the weights of the light sources to optimize PDT dose. Maximum and minimum PDT dose limits chosen for sample points in the prostate constrain the solution for the source strengths of the cylindrical diffuser fibers (CDF). We tested the Cimmino optimization procedures using the light fluence kernel generated for heterogeneous optical properties, and compared the optimized treatment plans with those obtained using homogeneous optical properties. To study how different photosensitizer distributions in the prostate affect optimization, comparisons of light fluence rate and PDT dose distributions were made with three distributions of photosensitizer: uniform, linear spatial distribution, and the measured PS distribution. The study shows that optimization of individual light source positions and intensities are feasible for the heterogeneous prostate during PDT.

Altschuler, Martin D.; Zhu, Timothy C.; Hu, Yida; Finlay, Jarod C.; Dimofte, Andreea; Wang, Ken; Li, Jun; Cengel, Keith; Malkowicz, S. B.; Hahn, Stephen M.

2009-02-01

341

An optimized algorithm for lossy compression of real-time data  

Microsoft Academic Search

The Swinging Door Trending algorithm and the Douglas-Peucker algorithm are both staple lossy compression algorithms. The former one is widely used in real-time database software of industry, while the latter one is more popular for spatial data processing. In this paper, these two algorithms are compared first to summarize their advantages and disadvantages. And then, an optimized lossy compression algorithm

Gang Chen; Li Li

2010-01-01

342

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

343

Left ventricle segmentation in MRI via convex relaxed distribution matching.  

PubMed

A fundamental step in the diagnosis of cardiovascular diseases, automatic left ventricle (LV) segmentation in cardiac magnetic resonance images (MRIs) is still acknowledged to be a difficult problem. Most of the existing algorithms require either extensive training or intensive user inputs. This study investigates fast detection of the left ventricle (LV) endo- and epicardium surfaces in cardiac MRI via convex relaxation and distribution matching. The algorithm requires a single subject for training and a very simple user input, which amounts to a single point (mouse click) per target region (cavity or myocardium). It seeks cavity and myocardium regions within each 3D phase by optimizing two functionals, each containing two distribution-matching constraints: (1) a distance-based shape prior and (2) an intensity prior. Based on a global measure of similarity between distributions, the shape prior is intrinsically invariant with respect to translation and rotation. We further introduce a scale variable from which we derive a fixed-point equation (FPE), thereby achieving scale-invariance with only few fast computations. The proposed algorithm relaxes the need for costly pose estimation (or registration) procedures and large training sets, and can tolerate shape deformations, unlike template (or atlas) based priors. Our formulation leads to a challenging problem, which is not directly amenable to convex-optimization techniques. For each functional, we split the problem into a sequence of sub-problems, each of which can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Unlike related graph-cut approaches, the proposed convex-relaxation solution can be parallelized to reduce substantially the computational time for 3D domains (or higher), extends directly to high dimensions, and does not have the grid-bias problem. Our parallelized implementation on a graphics processing unit (GPU) demonstrates that the proposed algorithm requires about 3.87s for a typical cardiac MRI volume, a speed-up of about five times compared to a standard implementation. We report a performance evaluation over 400 volumes acquired from 20 subjects, which shows that the obtained 3D surfaces correlate with independent manual delineations. We further demonstrate experimentally that (1) the performance of the algorithm is not significantly affected by the choice of the training subject and (2) the shape description we use does not change significantly from one subject to another. These results support the fact that a single subject is sufficient for training the proposed algorithm. PMID:23851075

Nambakhsh, Cyrus M S; Yuan, Jing; Punithakumar, Kumaradevan; Goela, Aashish; Rajchl, Martin; Peters, Terry M; Ayed, Ismail Ben

2013-06-10

344

Distance Between a Point and a Convex Cone in $n$ Dimensional Space: Computation and Applications  

Microsoft Academic Search

This paper presents an algorithm to compute the minimum distance from a point to a convex cone in n-dimensional space. The convex cone is represented as the set of all nonnegative combinations of a given set. The algorithm generates a sequence of simplicial cones in the convex cone, such that their distances to the single point converge to the desired

Chee-Meng Chew

2009-01-01

345

Linear array synthesis using an ant-colony-optimization-based algorithm  

Microsoft Academic Search

The aim of this work is to show the use of a well-known type of evolutionary computational optimization technique, ant colony optimization (ACO), in a typical electromagnetic problem: linear array synthesis. To this aim, an algorithm based on the fundamentals of ant colony optimization has been developed. The algorithm uses real numbers. Some examples using different optimization criteria are presented.

E. Rajo-lglesias; Oscar Quevedo-Teruel

2007-01-01

346

The application of combinatorial optimization by Genetic Algorithm and Neural Network  

Microsoft Academic Search

A optimization model of sizing the storage section in a renewable power generation system was set up, and two methods were used to solve the model: genetic algorithm or combinatorial optimization by genetic algorithm and neural network. The system includes the photovoltaic arrays, the lead-acid battery and a flywheel. The optimal sizing can be considered as a constrained optimization problem:

Shiqiong Zhou; Longyun Kang; Guifang Guo; Yanning Zhang; Jianbo Cao; Binggang Cao

2008-01-01

347

GSP-ANT: An efficient ant colony optimization algorithm with multiple good solutions for pheromone update  

Microsoft Academic Search

Ant colony optimization (ACO) is a metaheuristic for various optimization problems, especially the hard combinatorial optimization problems. However, existing ACO algorithms suffer from search stagnation and exorbitantly long computation time. To alleviate these shortcomings, an improved ACO algorithm, called GSP-ANT, is presented in this paper. It maintains a good solution pool (GSP) and alternately uses the optimal solution and suboptimal

Zhigang Ren; Zuren Feng; Zhaojun Zhang

2009-01-01

348

Horizontal axis wind turbine systems: optimization using genetic algorithms  

NASA Astrophysics Data System (ADS)

A method for the optimization of a grid-connected wind turbine system is presented. The behaviour of the system components is coupled in a non-linear way, and optimization must take into account technical and economical aspects of the complete system design. The annual electrical energy cost is estimated using a cost model for the wind turbine rotor, nacelle and tower and an energy output model based on the performance envelopes of the power coefficient of the rotor, CP, on the Weibull parameters k and c and on the power law coefficient of the wind profile. In this study the site is defined with these three parameters and the extreme wind speed Vmax. The model parameters vary within a range of possible values. Other elements of the project (foundation, grid connection, financing cost, etc.) are taken into account through coefficients. The optimal values of the parameters are determined using genetic algorithms, which appear to be efficient for such a problem. These optimal values were found to be very different for a Mediterranean site and a northern European site using our numerical model. Optimal wind turbines at the Mediterranean sites considered in this article have an excellent profitability compared with reference northern European wind turbines. Most of the existing wind turbines appear to be well designed for northern European sites but not for Mediterranean sites.

Diveux, T.; Sebastian, P.; Bernard, D.; Puiggali, J. R.; Grandidier, J. Y.

2001-10-01

349

Lens design and optimization using multi-objective evolutionary algorithms  

NASA Astrophysics Data System (ADS)

Non-Dominated Sorting Genetic Algorithm 2 (NSGA 2) was used to optimize optical systems with multiple objectives. The systems selected for study are Cooke triplets, Petzval lens and double Gauss lens. The objectives are minimization of aberration coefficients for spherical aberration, distortion, and the sum of coefficients of all third order monochromatic aberrations. CODE V RTM was used as a ray tracer. A set of trade-off solutions representing the optima, known as Pareto-Optima in multi-objective analysis, was obtained. A comparison of obtained optima to the known optima was done. Pareto-Optima in objective space for the selected Petzval lens design problem are shown to exhibit saddle points having unique trade-off features, which can not be detected in traditional gradient-based scalar optimization. Various optimization strategies are illustrated which ensure a diverse set of Pareto-Optima offering alternate manufacturing choices. Based on the results, a fourth objective was identified (sum of lateral and axial color coefficients) that is necessary to make valid trade-off decisions. The expansion of objectives followed by re-optimization provided unique trade-off solutions. Based on power and symmetry distribution of the component elements for the Cooke triplet system, addition and deletion of elements were carried out. The fourth objective added for that study is the minimization of the required number of elements. For the double Gauss lens system, the Pareto optimal surface indicated alternate manufacturing choices. There is a clear diversity of the Pareto optimal front in both objective and decision vector space. These studies have clearly illustrated the advantages of evolutionary multi-objective optimization techniques in optical system design.

Joseph, Shaine

350

Development of nonsingular optimization algorithm and its application to chemical engineering systems  

Microsoft Academic Search

A nonsingular transformation algorithm which converts a singular control problem to nonsingular one was developed to calculate\\u000a optimal control profiles. Several chemical engineering problems for applying a nonsingular transformation algorithm were presented\\u000a and optimal profiles were calculated in this paper. The singular control algorithm and nonsingular transformation algorithm\\u000a were compared. The efficiency of the transformation algorithm was displayed in this

Jung Heon Lee; Henry C. Lim

1999-01-01

351

A Hybrid Rough K-Means Algorithm and Particle Swarm Optimization for Image Classification  

Microsoft Academic Search

This paper proposes a hybrid rough K-means algorithm for image classification. The rough set theory is used to establish the\\u000a lower and upper bound for data clustering in the K-means algorithm. Then, the particle swarm optimization (PSO) is employed\\u000a to optimize the solutions of the rough K-means algorithm. The combined algorithm is called the Rough K-means PSO algorithm.\\u000a Experimental results

Chih-cheng Hung; Hendri Purnawan

2008-01-01

352

Hybridizing the harmony search algorithm with a spreadsheet ‘Solver’ for solving continuous engineering optimization problems  

Microsoft Academic Search

In this article, a hybrid global–local optimization algorithm is proposed to solve continuous engineering optimization problems. In the proposed algorithm, the harmony search (HS) algorithm is used as a global-search method and hybridized with a spreadsheet ‘Solver’ to improve the results of the HS algorithm. With this purpose, the hybrid HS–Solver algorithm has been proposed. In order to test the

M. Tamer Ayvaz; Ali Haydar Kayhan; Huseyin Ceylan; Gurhan Gurarslan

2009-01-01

353

An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design  

Microsoft Academic Search

As there is a growing interest in applications of multi-objective optimization methods to real-world problems, it is essential to develop efficient algorithms to achieve better performance in engineering design and resources optimization. An efficient algorithm for multi-objective optimization, based on swarm intelligence principles, is presented in this article. The proposed algorithm incorporates a Pareto dominance relation into particle swarm optimization

M. Janga Reddy; D. Nagesh Kumar

2007-01-01

354

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/.

2013-01-01

355

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

356

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

357

Optimum packing of convex-polygons by a new data structure sequence-table  

Microsoft Academic Search

The convex-polygon packing problem is to arrange given convex-polygons without overlapping each other in a bounding box of minimum area. This paper proposes a new algorithm. A solution space of convex-polygon packings is constructed by a new data-structure called the sequence-table which can represent topological relations between two convex-polygons. The algorithm searches the solution space stochastically for an optimum packing.

Hiroyuki Yamazaki; Keishi Sakanushi; Yoji Kajitani

2000-01-01

358

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

359

Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm  

Microsoft Academic Search

A new method based on a fuzzy mutated genetic algorithm for optimal reconfiguration of radial distribution systems (RDS) is presented. The proposed algorithm overcomes the combinatorial nature of the reconfiguration problem and deals with noncontinuous multi-objective optimization. The attractive features of the algorithm are: preservation of radial property of the network without islanding any load point by an elegant coding

K. Prasad; R. Ranjan; N. C. Sahoo; A. Chaturvedi

2005-01-01

360

Optimization of the quadratic assignment problem using an ant colony algorithm  

Microsoft Academic Search

Ant algorithm is a multi-agent systems inspired by the behaviors of real ant colonies function to solve optimization problems. In this paper an ant colony optimization algorithm is developed to solve the quadratic assignment problem. The local search process of the algorithm is simulated annealing. In the exploration of the search space, the evaluation of pheromones which are laid on

Nihan Çetin Demirel; M. Duran Toksari

2006-01-01

361

A new structural optimization method based on the harmony search algorithm  

Microsoft Academic Search

Most structural optimization methods are based on mathematical algorithms that require substantial gradient information. The selection of the starting values is also important to ensure that the algorithm converges to the global optimum. This paper describes a new structural optimization method based on the harmony search (HS) meta-heuristic algorithm, which was conceptualized using the musical process of searching for a

Kang Seok Lee; Zong Woo Geem

2004-01-01

362

On the Optimal Convergence Probability of Univariate Estimation of Distribution Algorithms  

Microsoft Academic Search

In this paper we obtain bounds on the probability of convergence to the optimal solution for the compact genetic algorithm (cGA) and the population based incremental learning (PBIL). Moreover, we give a sufficient condition for convergence of these algorithms to the optimal solution and compute a range of possible values for algorithm parameters at which there is convergence to the

Reza Rastegar

2011-01-01

363

Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search  

Microsoft Academic Search

Marriage in Honey Bees Optimization (MBO) is swarm-intelligence methods. In this paper, the author proposed a new swarm-intelligence method, named as Wolf Pack Search (WPS), which is abstracted from the behavior feature of the wolf pack. Utilizing the WPS algorithm into the local search process of Marriage in Honey Bees Optimization algorithm, the paper gives a new algorithm, Wolf Pack

Chenguang Yang; Xuyan Tu; Jie Chen

2007-01-01

364

GMG: A Guaranteed, Efficient Global Optimization Algorithm for Remote Sensing.  

SciTech Connect

The monocular passive ranging (MPR) problem in remote sensing consists of identifying the precise range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem may be set as a global optimization problem (GOP) whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. Using additional information about the error function between the predicted and observed radiances of the target, we developed GMG, a new algorithm to find the Global Minimum with a Guarantee. The new algorithm transforms the original continuous GOP into a discrete search problem, thereby guaranteeing to find the position of the global minimum in a reasonably short time. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions and then applied to various realizations of the MPR problem.

D'Helon, CD

2004-08-18

365

Convex profiles from asteroid lightcurves  

NASA Astrophysics Data System (ADS)

A lightcurve inversion method that yields a two-dimensional convex profile is introduced. The number of parameters that characterize the profile is limited only by the number of Fourier harmonics used to represent the parent lightcurve. The implementation of the method is outlined by a recursive quadratic programming algorithm, and its application to photoelectric lightcurves and radar measurements is discussed. Special properties of the lightcurves of geometrically scattering ellipsoids are pointed out, and those properties are used to test the inversion method and obtain a criterion for judging whether any lightcurve could actually be due to such an object. Convex profiles for several asteroids are shown, and the method's validity is discussed from a physical as well as purely statistical point of view.

Ostro, S. J.; Connelly, R.

1984-03-01

366

Tractable fitting with convex polynomials via sum-of-squares  

Microsoft Academic Search

We consider the problem of fitting given data (u1,y1),...,(um,ym) where ui? Rnand yi? R with a convex polynomial f. A technique to solve this problem using sum of squares polynomials is presented. This technique is extended to enforce convexity of f only on a specified region. Also, an algorithm to fit the convex hull of a set of points with

Alessandro Magnani; Sanjay Lall; Stephen Boyd

2005-01-01

367

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

368

Simultaneous topology and size optimization of structures by genetic algorithm using minimal length chromosome  

Microsoft Academic Search

Purpose – Layout optimization of structures aims to find the optimal topology and member sizes in an integrated manner. For this purpose, the most successful attempts have addressed the outstanding features of the genetic algorithms. Design\\/methodology\\/approach – This paper utilizes a direct index coding (DIC) in a way that the optimization algorithm can simultaneously integrate topology and size in a

A. Kaveh; M. Shahrouzi

2006-01-01

369

The study of copper electrolysis rectifier system based on PSO algorithm to optimize PID parameters  

Microsoft Academic Search

This paper mainly studies the PSO algorithm to optimize PID parameters for copper electrolytic system, research and analysis the development trend of high-power electrolytic devices at home and abroad. Based on the theoretical guidance, we use of particle swarm optimization (PSO) to design a new control method to optimize PID parameters, simulation results show the effectiveness of the algorithm and

Xue Jian; Bai Jing

2011-01-01

370

Optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm  

Microsoft Academic Search

Gasoline blending is a key process in the petroleum refinery industry posed as a nonlinear optimization problem with heavily nonlinear constraints. This paper presents a DNA based hybrid genetic algorithm (DNA-HGA) to optimize such nonlinear optimization problems. In the proposed algorithm, potential solutions are represented with nucleotide bases. Based on the complementary properties of nucleotide bases, operators inspired by DNA

Xiao Chen; Ning Wang

2010-01-01

371

Multi-machine power system stabilizers design using chaotic optimization algorithm  

Microsoft Academic Search

In this paper, a multiobjective design of the multi-machine power system stabilizers (PSSs) using chaotic optimization algorithm (COA) is proposed. Chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum, is a promising tool for the engineering applications. The PSSs parameters tuning problem is converted to an optimization

H. Shayeghi; H. A. Shayanfar; S. Jalilzadeh; A. Safari

2010-01-01

372

Design optimization of a two-dimensional hydrofoil by applying a genetic algorithm  

Microsoft Academic Search

In an effort to optimize river-flow training structures, a study is undertaken to explore the utility of genetic algorithms. The study includes the development of a numerical procedure for optimization of a two-dimensional hydrofoil; the optimization of shape is performed using a genetic algorithm. A formula utilizing two Bezier splines for the construction of the foil shape is introduced. The

H. Ouyang; L. J. Weber; A. J. Odgaard

2006-01-01

373

Partially constrained ant colony optimization algorithm for the solution of constrained optimization problems: Application to storm water network design  

Microsoft Academic Search

This paper exploits the unique feature of the Ant Colony Optimization Algorithm (ACOA), namely incremental solution building mechanism, to develop partially constraint ACO algorithms for the solution of optimization problems with explicit constraints. The method is based on the provision of a tabu list for each ant at each decision point of the problem so that some constraints of the

M. H. Afshar

2007-01-01

374

Improved Harmony Search algorithm based optimal design of the brushless DC wheel motor  

Microsoft Academic Search

The paper presents an optimal design method to optimize the efficiency of the brushless DC wheel motor. The optimal design of a brushless DC wheel motor is a nonlinear multimodal benchmark optimization problem. Hence, conventional methods fail to provide optimal solution. Recently developed, Harmony Search (HS) algorithm has been used for maximizing the efficiency of the brushless DC wheel motor

Parikshit Yadav; Rajesh Kumar; S. K. Panda; C. S. Chang

2010-01-01

375

Experimental optimization of protein refolding with a genetic algorithm  

PubMed Central

Refolding of proteins from solubilized inclusion bodies still represents a major challenge for many recombinantly expressed proteins and often constitutes a major bottleneck. As in vitro refolding is a complex reaction with a variety of critical parameters, suitable refolding conditions are typically derived empirically in extensive screening experiments. Here, we introduce a new strategy that combines screening and optimization of refolding yields with a genetic algorithm (GA). The experimental setup was designed to achieve a robust and universal method that should allow optimizing the folding of a variety of proteins with the same routine procedure guided by the GA. In the screen, we incorporated a large number of common refolding additives and conditions. Using this design, the refolding of four structurally and functionally different model proteins was optimized experimentally, achieving 74–100% refolding yield for all of them. Interestingly, our results show that this new strategy provides optimum conditions not only for refolding but also for the activity of the native enzyme. It is designed to be generally applicable and seems to be eligible for all enzymes.

Anselment, Bernd; Baerend, Danae; Mey, Elisabeth; Buchner, Johannes; Weuster-Botz, Dirk; Haslbeck, Martin

2010-01-01

376

Optimization algorithm of digital watermarking anti-coalition attacks in DWT-domain based on genetic algorithm  

NASA Astrophysics Data System (ADS)

An adaptive optimization watermarking algorithm based on Genetic Algorithm (GA) and discrete wavelet transform (DWT) is proposed in this paper. The core of this algorithm is the fitness function optimization model for digital watermarking based on GA. The embedding intensity for digital watermarking can be modified adaptively, and the algorithm can effectively ensure the imperceptibility of watermarking while the robustness is ensured. The optimization model research may provide a new idea for anti-coalition attacks of digital watermarking algorithm. The paper has fulfilled many experiments, including the embedding and extracting experiments of watermarking, the influence experiments by the weighting factor, the experiments of embedding same watermarking to the different cover image, the experiments of embedding different watermarking to the same cover image, the comparative analysis experiments between this optimization algorithm and human visual system (HVS) algorithm and etc. The simulation results and the further analysis show the effectiveness and advantage of the new algorithm, which also has versatility and expandability. And meanwhile it has better ability of anti-coalition attacks. Moreover, the robustness and security of watermarking algorithm are improved by scrambling transformation and chaotic encryption while preprocessing the watermarking.

Que, Dashun; Li, Gang; Yue, Peng

2007-11-01

377

A sparse representation-based deployment method for optimizing the observation quality of camera networks.  

PubMed

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

2013-08-28

378

An Improved Graph-Based FPGA Techology Mapping Algorithm For Delay Optimization  

Microsoft Academic Search

We present a graph based technology mapping algorithm,called DAG-Map, for delay optimization inlookup-table based FPGA designs. Our algorithm carriesout technology mapping and delay optimization on theentire Boolean network, instead of decomposing it intofanout-free trees and mapping each tree separately as inmost previous algorithms. As a preprocessing step, weintroduce a general algorithm which transforms an arbitraryn-input network into a two-input network

Jason Cong; Yuzheng Ding; Andrew B. Kahng; Peter Trajmar; Kuang-chien Chen

1992-01-01

379

A TIPSO algorithm assessment for aerothermodynamic optimization of hypersonic compression systems  

Microsoft Academic Search

A two-step improved particle swarm optimization (TIPSO) algorithm was recently proposed and used for the optimization of flexible satellite-control parameters. It was found to be much more stable and much less complex than other evolutionary algorithms. In this article the efficacy of the TIPSO algorithm is investigated for multidisciplinary optimization of aerothermodynamic parameters of performance, cowl deflection angle and shock–boundary

Ali Sarosh; Hu Di; Dong Yun-Feng

2012-01-01

380

On the optimal convergence probability of univariate estimation of distribution algorithms.  

PubMed

In this paper we obtain bounds on the probability of convergence to the optimal solution for the compact genetic algorithm (cGA) and the population based incremental learning (PBIL). Moreover, we give a sufficient condition for convergence of these algorithms to the optimal solution and compute a range of possible values for algorithm parameters at which there is convergence to the optimal solution with a predefined confidence level. PMID:20807083

Rastegar, Reza

2010-08-31

381

Computing the Center of Area of a Convex Polygon  

Microsoft Academic Search

The center of area of a convex planar set is the point for which the minimum area of intersected by any halfplane containing is maximized. We describe a simple randomized linear-time algorithm for computing the center of area of a convex -gon.

Peter Braß; Laura Heinrich-litan; Pat Morin

2003-01-01

382

On Fixed Convex Combinations of No-Regret Learners  

Microsoft Academic Search

Abstract No-regret algorithms are powerful tools for learning in online convex problems that have received increased attention in recent years. Considering affine and external regret, we investigate what happens when a set of no-regret learners (voters) merge their respective strategies in each learn- ing iteration to a single, common one in form of a convex combination. We show that an

Jan-P. Calliess

2009-01-01

383

A Taxonomy of Evolutionary Algorithms in Combinatorial Optimization  

Microsoft Academic Search

This paper shows how evolutionary algorithms can be described in a concise, yet comprehensive and accurate way. A classification scheme is introduced and presented in a tabular form called TEA (Table of Evolutionary Algorithms). It distinguishes between different classes of evolutionary algorithms (e.g., genetic algorithms, ant systems) by enumerating the fundamental ingredients of each of these algorithms. At the end,

Patrice Calégari; Giovanni Coray; Alain Hertz; Daniel Kobler; Pierre Kuonen

1999-01-01

384

Convex and Polaroid Extensions.  

National Technical Information Service (NTIS)

In an effort towards a comprehensive and unified theory, this note represents some new results in the area of non-convex programming within the framework of convex (sets and function) analysis. The entire study is primarily devoted to the development of u...

C. A. Burdet

1974-01-01

385

A Simplex-Like Algorithm for Fisher Markets  

NASA Astrophysics Data System (ADS)

We propose a new convex optimization formulation for the Fisher market problem with linear utilities. Like the Eisenberg-Gale formulation, the set of feasible points is a polyhedral convex set while the cost function is non-linear; however, unlike that, the optimum is always attained at a vertex of this polytope. The convex cost function depends only on the initial endowments of the buyers. This formulation yields an easy simplex-like pivoting algorithm which is provably strongly polynomial for many special cases.

Adsul, Bharat; Babu, Ch. Sobhan; Garg, Jugal; Mehta, Ruta; Sohoni, Milind

386

The selection of optimal ICA algorithm parameters for robust AEP component estimates using 3 popular ICA algorithms  

Microsoft Academic Search

Many authors have used the Auditory Evoked Potential (AEP) recordings to evaluate the performance of their ICA algorithms and have demonstrated that this procedure can remove the typical EEG artifact in these recordings (i.e. blinking, muscle noise, line noise, etc.). However, there is little work in the literature about the optimal parameters, for each of those algorithms, for the estimation

N. Castaneda-Villa; C. J. James

2008-01-01

387

Optimization of interference filters with genetic algorithms applied to silver-based heat mirrors  

NASA Astrophysics Data System (ADS)

Using a genetic algorithm, an optimization procedure is presented that optimizes the sequence of materials and their thickness simultaneously. An example is given of heat mirrors for thermal solar energy applications.

Eisenhammer, T.; Lazarov, M.; Leutbecher, M.; Schoffel, U.; Sizmann, R.

1993-11-01

388

Aerodynamics Design and Genetic Algorithms for Optimization of Airship Bodies  

NASA Astrophysics Data System (ADS)

A special and effective aerodynamics calculation method has been applied for the flow field around a body of revolution to find the drag coefficient for a wide range of Reynolds numbers. The body profile is described by a first order continuous axial singularity distribution. The solution of the direct problem then gives the radius and inviscid velocity distribution. Viscous effects are considered by means of an integral boundary layer procedure, and for determination of the transition location the forced transition criterion is applied. By avoiding those profiles, which result in the separation of the boundary layer, the drag can be calculated at the end of the body by using Young's formula. In this study, a powerful optimization procedure known as a Genetic Algorithms (GA) is used for the first time in the shape optimization of airship hulls. GA represents a particular artificial intelligence technique for large spaces, striking a remarkable balance between exploration and exploitation of search space. This method could reach to minimum objective function through a better path, and also could minimize the drag coefficient faster for different Reynolds number regimes. It was found that GA is a powerful method for such multi-dimensional, multi-modal and nonlinear objective function.

Nejati, Vahid; Matsuuchi, Kazuo

389

Ant colony optimization algorithm with random perturbation behavior to the problem of optimal unit commitment with probabilistic spinning reserve determination  

Microsoft Academic Search

In this paper, a novel ant colony optimization algorithm with random perturbation behavior (RPACO) based on combination of general ant colony optimization and stochastic mechanism is developed for the solution of optimal unit commitment (UC) with probabilistic spinning reserve determination. In general, the purpose of UC is to enhance the economical efficiency as could as possible while simultaneously satisfying physical

Libao Shi; Jin Hao; Jiaqi Zhou; Guoyu Xu

2004-01-01

390

A novel weight-improved particle swarm optimization algorithm for optimal power flow and economic load dispatch problems  

Microsoft Academic Search

The optimization problems of optimal power flow (OPF) and the economic load dispatch (ELD) with valve-point effects in power systems are recently solved by some types of artificial intelligent (AI) algorithms. In this paper, based on improving the function of weight parameters, we present a novel weight-improved particle swarm optimization (WIPSO) method for computing two above problems. To evaluate the

PhanTu Vu; DinhLuong Le; NgocDieu Vo; Josef Tlusty

2010-01-01

391

Wrapper: a surface optimization algorithm that preserves highly curved areas  

NASA Astrophysics Data System (ADS)

Software to construct polygonal models of anatomical structures embedded as isosurfaces in 3D medical images has been available since the mid 1970s. Such models are used for visualization, simulation, measurements (single and multi-modality image registration), and statistics. When working with standard MR- or CT-scans, the surface obtained can contain several million triangles. These models contain data an order of magnitude larger than that which can be efficiently handled by current workstations or transmitted through networks. These algorithms generally ignore efficient combinations that would produce fewer, well shaped triangles. An efficient algorithm must not create a larger data structure than present in the raw data. Recently, much research has been done on the simplification and optimization of surfaces ([Moore and Warren, 1991]); [Schroeder et al., 1992]; [Turk, 1992]; [Hoppe et al., 1993]; [Kalvin and Taylor, 1994]). All of these algorithms satisfy two criteria, consistency and accuracy, to some degree. Consistent simplification occurs via predictable patterns. Accuracy is measured in terms of fidelity to the original surface, and is a prerequisite for collecting reliable measurements from the simplified surface. We describe the 'Wrapper' algorithm that simplifies triangulated surfaces while preserving the same topological characteristics. We employ the same simplification operation in all cases. However, simplification is restricted but not forbidden in high curvature areas. This hierarchy of operations results in homogeneous triangle aspect and size. Images undergoing compression ratios between 10 and 20:1 are visually identical to full resolution images. More importantly, the metric accuracy of the simplified surfaces appears to be unimpaired. Measurements based upon 'ridge curves; (sensu [Cutting et al., 1993]) extracted on polygonal models were recently introduced [Ayache et al., 1993]. We compared ridge curves digitized from full resolution, Wrapper, and volume subsampled CT-scan isosurfaces. [Dean, 1993] introduced a method for measuring distances between space curves. In the best case this method demonstrated that ridge curves digitized from the Wrapper simplified images were two orders of magnitude closer to the full resolution image than those taken from the volume subsampled images.

Gueziec, Andre; Dean, David

1994-09-01

392

The differential Ant-Stigmergy Algorithm for large-scale global optimization  

Microsoft Academic Search

Ant-colony optimization (ACO) is a popular swarm intelligence metaheuristic scheme that can be applied to almost any optimization problem. In this paper, we address a performance evaluation of an ACO-based algorithm for solving large-scale global optimization problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The DASA transforms a real-parameter optimization problem into a graph-search problem. The parameters' differences assigned

Peter Korosec; Katerina Tashkova; Jurij Silc

2010-01-01

393

An optimized parallel LSQR algorithm for seismic tomography  

NASA Astrophysics Data System (ADS)

The LSQR algorithm developed by Paige and Saunders (1982) is considered one of the most efficient and stable methods for solving large, sparse, and ill-posed linear (or linearized) systems. In seismic tomography, the LSQR method has been widely used in solving linearized inversion problems. As the amount of seismic observations increase and tomographic techniques advance, the size of inversion problems can grow accordingly. Currently, a few parallel LSQR solvers are presented or available for solving large problems on supercomputers, but the scalabilities are generally weak because of the significant communication cost among processors. In this paper, we present the details of our optimizations on the LSQR code for, but not limited to, seismic tomographic inversions. The optimizations we have implemented to our LSQR code include: reordering the damping matrix to reduce its band-width for simplifying the communication pattern and reducing the amount of communication during calculations; adopting sparse matrix storage formats for efficiently storing and partitioning matrices; using the MPI I/O functions to parallelize the date reading and result writing processes; providing different data partition strategies for efficiently using computational resources. A large seismic tomographic inversion problem, the full-3D waveform tomography for Southern California, is used to explain the details of our optimizations and examine the performance on Yellowstone supercomputer at the NCAR-Wyoming Supercomputing Center (NWSC). The results showed that the required wall time of our code for the same inversion problem is much less than that of the LSQR solver from the PETSc library (Balay et al., 1997).

Lee, En-Jui; Huang, He; Dennis, John M.; Chen, Po; Wang, Liqiang

2013-12-01

394

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

395

Topologies m -Convexes dans les algebres a-convexes  

Microsoft Academic Search

\\u000a Abstract  We endow anyA-convex algebra (E, ?) with a multiplicatively-convex topologyM(?) finer than ?. We then show that this is the weakestm-convex topology finer than ?. We are then led to consider two classes ofA-convex algebras examples and (or) characterizations of which are given.

Lahbib Oubbi

1992-01-01

396

The selection of optimal ICA algorithm parameters for robust AEP component estimates using 3 popular ICA algorithms.  

PubMed

Many authors have used the Auditory Evoked Potential (AEP) recordings to evaluate the performance of their ICA algorithms and have demonstrated that this procedure can remove the typical EEG artifact in these recordings (i.e. blinking, muscle noise, line noise, etc.). However, there is little work in the literature about the optimal parameters, for each of those algorithms, for the estimation of the AEP components to reliably recover both the auditory response and the specific artifacts generated for the normal function of a Cochlear Implant (CI), used for the rehabilitation of deaf people. In this work we determine the optimal parameters of three ICA algorithms, each based on different independence criteria, and assess the resulting estimations of both the auditory response and CI artifact. We show that the algorithm utilizing temporal structure, such as TDSEP-ICA, is better in estimating the components of the auditory response, in recordings contaminated by CI artifacts, than higher order statistics based algorithms. PMID:19163893

Castañeda-Villa, N; James, C J

2008-01-01

397

Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem  

Microsoft Academic Search

This work extends the research related to genetic algorithms (GA) in core design optimization problems, which basic investigations were presented in previous work. Here we explore the use of the Island Genetic Algorithm (IGA), a coarse-grained parallel GA model, comparing its performance to that obtained by the application of a traditional non-parallel GA. The optimization problem consists on adjusting several

Cláudio M. N. A. Pereira; Celso M. F. Lapa

2003-01-01

398

Optimal Maintenance Scheduling of Power Systems using an Algorithm Inspired by Swarm Intelligence and Quantum Evolution  

Microsoft Academic Search

This research work applies the techniques of particle swarm optimization (PSO) and the quantum evolution algorithm (QEA) in solving the complex power systems maintenance scheduling (PSMS) problem. The algorithm is applied for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system, while satisfying system load demand and crew constraints. Modified discrete particle

Yusuf Yare

399

Multi-response simulation optimization using genetic algorithm within desirability function framework  

Microsoft Academic Search

This paper presents a new methodology to solve multi-response statistical optimization problems. This methodology integrates desirability function and simulation approach with a genetic algorithm. The desirability function is responsible for modeling the multi-response statistical problem, the simulation approach generates required input data from a simulated system, and finally the genetic algorithm tries to optimize the model. This methodology includes four

Seyed Hamid Reza Pasandideh; Seyed Taghi Akhavan Niaki

2006-01-01

400

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

401

Modified differential evolution with local search algorithm for real world optimization  

Microsoft Academic Search

Real world optimization problems are used to judge the performance of any Evolutionary Algorithm (EA) over real world applications. This is why the performance of any EA over the real world optimization problems is very important for judging its efficiency. In this work, we represent a multi- population based memetic algorithm CDELS. It is hybridization of a competitive variant of

Ankush Mandal; Aveek Kumar Das; Prithwijit Mukherjee; Swagatam Das; Ponnuthurai Nagaratnam Suganthan

2011-01-01

402

A short convergence proof for a class of ant colony optimization algorithms  

Microsoft Academic Search

We prove some convergence properties for a class of ant colony optimization algorithms. In particular, we prove that for any small constant ? > 0 and for a sufficiently large number of algorithm iterations t, the probability of finding an optimal solution at least once is P*(t) ⩾ 1 - ? and that this probability tends to 1 for t??.

Thomas Stützle; Marco Dorigo

2002-01-01

403

Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors  

Microsoft Academic Search

An improved genetic algorithm approach, based on a new ranking strategy, has been proposed to conduct multi-objective optimization of chemical engineering problems. New operators have been introduced to enhance the algorithm performance and reduce the computational effort. A Pareto-set filter operator has been implemented to avoid missing Pareto optimal points during the evolutionary process. A niche operator has been adopted

C. M. Silva; E. C. Biscaia Jr

2003-01-01

404

A robust stochastic genetic algorithm (StGA) for global numerical optimization  

Microsoft Academic Search

Many real-life problems can be formulated as numerical optimization of certain objective functions. However, often an objective function possesses numerous local optima, which could trap an algorithm from moving toward the desired global solution. Evolutionary algorithms (EAs) have emerged to enable global optimization; however, at the present stage, EAs are basically limited to solving small-scale problems due to the constraint

Zhenguo Tu; Yong Lu

2004-01-01

405

SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization  

Microsoft Academic Search

The Strength Pareto Evolutionary Algorithm (SPEA) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies ,2 SPEA has shown very good performance in comparison to other multiobjective evolutionary algorithms, and therefore it has been a point of reference in various recent investigations? Furthermore, it has been used in different applications.

E. Zitzler; M. Laumanns; L. Thiele

2002-01-01

406

Application of an ant algorithm for layout optimization of tree networks  

Microsoft Academic Search

This article presents an application of the ant algorithm to the layout optimization of tree networks. Two different formulations are used to represent the layout optimization problem of tree networks in the proper form required for the application of the ant algorithm. In the first formulation, each link of the base graph, from which the optimum layout is to be

Mohammad H. Afshar; Miguel A. Mariño

2006-01-01

407

Novel simulated annealing algorithm in order to optimal adjustment of digital PID controller  

Microsoft Academic Search

This article presents a method for optimal tuning of PID controllers by using simulated annealing optimization algorithm and tries to increase speed and decrease errors in PID controller by adding intelligent techniques to the basic algorithm. The objective of this paper is to reduce errors and minimize IAE, ITAE, ISE and ITSE assessment criteria to be able to obtain a

J. Mohammad Dashti; Kambiz Shojaee; S. Mohammad H. Seyedkashi

2010-01-01

408

Optimality in reserve selection algorithms: When does it matter and how much?  

Microsoft Academic Search

This paper responds to recent criticisms in Biological Conservation of heuristic reserve selection algorithms. These criticisms primarily concern the fact that heuristic algorithms cannot guarantee an optimal solution to the problem of representing a group of targeted natural features in a subset of the sites in a region. We discuss optimality in the context of a range of needs for

C. R. Margules; H. P. Possingham

1996-01-01

409

A Memetic Algorithm for Multiple-Drug Cancer Chemotherapy Schedule Optimization  

Microsoft Academic Search

This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The objective is to minimize the tumor size under a set of constraints. We combine the adaptive elitist genetic algorithm

Sui-man Tse; Yong Liang; Kwong-sak Leung; Kin-hong Lee; Tony Shu-Kam Mok

2007-01-01

410

On the Optimal Convergence Probability of Univariate Estimation of Distribution Algorithms  

Microsoft Academic Search

In this paper, we obtain bounds on the probability of convergence to the optimal so- lution for the compact Genetic Algorithm (cGA) and the Population Based Incremen- tal Learning (PBIL). We also give a sufficient condition for convergence of these al- gorithms to the optimal solution and compute a range of possible values of the pa- rameters of these algorithms

Reza Rastegar

2009-01-01

411

Application of optimal trajectory algorithms to a solar-panel handling industrial manipulator: A case study  

Microsoft Academic Search

Several algorithms have been proposed in the last 25 years on the problem of generating time-optimal trajectories for robot manipulators along specified paths. This article describes an application of an optimal trajectory algorithm to an industrial manipulator used in the transfer of solar panel substrates between process modules. These manipulators operate in a vacuum environment and have constraints on substrate

Jayaraman Krishnasamy; Martin Hosek; Jairo Moura

2011-01-01

412

UCAV path planning based on Ant Colony Optimization and satisficing decision algorithm  

Microsoft Academic Search

Path planning of uninhabited combat air vehicle (UCAV) is a complicated global optimum problem. Ant colony optimization (ACO) algorithm was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. In this paper, we propose a hybrid ACO with satisficing decision algorithm

Haibin Duan; Yaxiang Yu; Rui Zhou

2008-01-01

413

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

414

Optimization algorithms and weighting factors for analysis of dynamic PET studies  

Microsoft Academic Search

Positron emission tomography (PET) pharmacokinetic analysis involves fitting of measured PET data to a PET pharmacokinetic model. The fitted parameters may, however, suffer from bias or be unrealistic, especially in the case of noisy data. There are many optimization algorithms, each having different characteristics. The purpose of the present study was to evaluate (1) the performance of different optimization algorithms

Maqsood Yaqub; Ronald Boellaard; Marc A. Kropholler; Adriaan A. Lammertsma

2006-01-01

415

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

416

Robust coordinated design of UPFC damping controller and PSS using Chaotic Optimization Algorithm  

Microsoft Academic Search

A chaotic optimization algorithm (COA) based approach for the robust coordinated design of the UPFC power oscillation damping controller and the conventional power system stabilizer has been investigated in this paper. Chaotic Optimization Algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from local optimum, is a promising tool for engineering applications. The

S. Jalilzadeh; H. Shayeghi; A. Safari; E. Aliabadi

2009-01-01

417

Computation of penetration measures for convex polygons and polyhedra for graphics applications  

Microsoft Academic Search

Algorithms to compute measures of penetration between convex polygonal objects in ℜ2 and convex polyhedral objects in ℜ3 are presented. The algorithms are analyzed for their asymptotic complexity. Details of implementation on a single processor machine are given. Parallelization of the algorithms is discussed

K. Sridharan

1998-01-01

418

Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical engineering optimization problem  

Microsoft Academic Search

This paper introduces a new constraint handling scheme developed for the differential evolutionary algorithm to solve constrained optimization problems. The developed approach uses a repair algorithm based on the gradient information derived from the equality constraint set to correct infeasible solutions. A dominance-based selection scheme is also applied to incorporate constraints into the objective function. To illustrate the developed algorithm

Soorathep Kheawhom

2010-01-01

419

HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network  

Microsoft Academic Search

Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for

Jianping Wang; Eseosa Osagie; Parimala Thulasiraman; Ruppa K. Thulasiram

2009-01-01

420

Cost optimization of a composite floor system using an improved harmony search algorithm  

Microsoft Academic Search

In this study, cost optimization of a composite floor system is performed utilizing the harmony search algorithm and an improved harmony search algorithm. These algorithms imitate the musical performance process that takes place when a musician searches for a better state of harmony, similar to the optimum design process which looks for the optimum solution. A composite floor system is

A. Kaveh; A. Shakouri Mahmud Abadi

2010-01-01

421

Global Convergence Theory for a Class of Trust Region Algorithms for Constrained Optimization.  

National Technical Information Service (NTIS)

This research presents a trust region algorithm for solving the equality constrained optimization problem. This algorithm is a variant of the 1984 Celis-Dennis-Tapia algorithm. The augmented Lagrangian function is used as a merit function. A scheme for up...

M. M. El-Alem

1988-01-01

422

An optimal algorithm for scheduling soft-aperiodic tasks in fixed-priority preemptive systems  

Microsoft Academic Search

A novel algorithm for servicing soft deadline aperiodic tasks in a real-time system in which hard deadline periodic tasks are scheduled using a fixed priority algorithm is presented. This algorithm is proved to be optimal in the sense that it provides the shortest aperiodic response time among all possible aperiodic service methods. Simulation studies show that it offers substantial performance

John P. Lehoczkyt; S. Ramos-thuel

1992-01-01

423

Branch-and-bound-based fast optimal algorithm for multiuser detection in synchronous CDMA  

Microsoft Academic Search

A fast optimal algorithm based on the branch and bound (BBD) method is proposed for the joint detection of binary symbols of K users in a synchronous code-division multiple access (CDMA) channel with Gaussian noise. Relationships between the proposed algorithms (depth-first BBD and fast BBD) and both the decorrelating decision feedback (DF) detector and sphere decoding (SD) algorithm are clearly

J. Luo; K. Pattipati; P. Willett; L. Brunel

2003-01-01

424

Stable, effective fuzzy DMC algorithms with on-line quadratic optimization  

Microsoft Academic Search

Stable fuzzy DMC (FDMC) algorithms are presented in the paper. The algorithms consist in solving only quadratic optimization problems at each time instant. Therefore, they are effective and relatively little complicated. The stabilization mechanisms introduced are simple and easy to implement. These mechanisms can be used in conjunction with any variant of FDMC (fuzzy dynamic matrix control) algorithm. Exemplary simulation

P. Marusak; P. Tatjewski

2003-01-01

425

Improvement of characteristic statistic algorithm and its application on equilibrium cycle reloading optimization  

SciTech Connect

A brief introduction of characteristic statistic algorithm (CSA) is given in the paper, which is a new global optimization algorithm to solve the problem of PWR in-core fuel management optimization. CSA is modified by the adoption of back propagation neural network and fast local adjustment. Then the modified CSA is applied to PWR Equilibrium Cycle Reloading Optimization, and the corresponding optimization code of CSA-DYW is developed. CSA-DYW is used to optimize the equilibrium cycle of 18 month reloading of Daya bay nuclear plant Unit 1 reactor. The results show that CSA-DYW has high efficiency and good global performance on PWR Equilibrium Cycle Reloading Optimization. (authors)

Hu, Y.; Liu, Z.; Shi, X.; Wang, B. [Inst. of Nuclear and New Energy Technology INET, Tsinghua Univ., 100084, Beijing (China)

2006-07-01

426

An ordinal optimization theory-based algorithm for solving the optimal power flow problem with discrete control variables  

Microsoft Academic Search

The optimal power flow (OPF) problem with discrete control variables is an NP-hard problem in its exact formulation. To cope with the immense computational-difficulty of this problem, we propose an ordinal optimization theory-based algorithm to solve for a good enough solution with high probability. Aiming for hard optimization problems, the ordinal optimization theory, in contrast to heuristic methods, guarantee to

Shin-Yeu Lin; Yu-Chi Ho; Ch'i-Hsin Lin

2004-01-01

427

Optimal design of binary phase-only filters using genetic algorithms  

NASA Astrophysics Data System (ADS)

The genetic algorithm is a mathematical optimization technique which has generally been applied to one-dimensional problems. In this work, the genetic algorithm was applied to a two-dimensional problem--the construction of binary phase-only spatial filters for optical pattern recognition. Spatial filters that are invariant to range and aspect changes are required for robust pattern recognition. Construction of invariant filters is an optimization problem where the correlation is the objective function for the genetic algorithm. Results are presented for correlation of a genetic algorithm-constructed filter with a multiple aspect angle target set. Filters using a hill-climber algorithm were also constructed and tested.

Deb, Kalyanmoy

1993-08-01

428

Optimal parameter estimation for Muskingum model based on Gray-encoded accelerating genetic algorithm  

NASA Astrophysics Data System (ADS)

In order to reduce the computational amount and improve the computational precision for parameter optimization of Muskingum model, a new algorithm, Gray-encoded accelerating genetic algorithm (GAGA) is proposed. With the shrinking of searching range, the method gradually directs to an optimal result with the excellent individuals obtained by Gray genetic algorithm (GGA). The global convergence is analyzed for the new genetic algorithm. Its efficiency is verified by application of Muskingum model. Compared with the nonlinear programming methods, least residual square method and the test method, GAGA has higher precision. And compared with GGA and BGA (binary-encoded genetic algorithm), GAGA has rapider convergent speed.

Chen, Jianjun; Yang, Xiaohua

2007-08-01

429

Convexification of different classes of non-convex MINLP problems  

Microsoft Academic Search

In the present paper, convexification strategies for certain kinds of discrete and integer non-convex optimization problems are introduced and discussed. We show how to solve problems with both posynomial and negative binomial terms in the constraints. The convexification technique may in some cases be generalized to include continuous variables. Posynomial functions are non-convex and for such functions no straightforward methods

Ray Pörn; Iiro Harjunkoski; Tapio Westerlund

1999-01-01

430

Solving the Convex Cost Integer Dual Network Flow Problem  

Microsoft Academic Search

In this paper, we consider an integer convex optimization problem where the objective functionis the sum of separable convex functions (that is, of the form (i,j)Q ij ij F(w)+ iP ii B( ) ), theconstraints are similar to those arising in the dual of a minimum cost flow problem (that is, of the form i - j w ij ,

Ravindra K. Ahuja; Dorit S. Hochbaum; James B. Orlin

1999-01-01

431

Approximate convex decomposition of polygons  

Microsoft Academic Search

We propose a strategy to decompose a polygon, containing zero or more holes, into ``approximately convex'' pieces. For many applications, the approximately convex components of this decomposition provide similar benefits as convex components, while the resulting decomposition is significantly smaller and can be computed more efficiently. Moreover, our approximate convex decomposition (ACD) provides a mechanism to focus on key structural

Jyh-Ming Lien; Nancy M. Amato

2004-01-01

432

PCE: Piecewise Convex Endmember Detection  

Microsoft Academic Search

A new hyperspectral endmember detection method that represents endmembers as distributions, autonomously partitions the input data set into several convex regions, and simultaneously determines endmember distributions (EDs) and proportion values for each convex region is presented. Spectral unmixing methods that treat endmembers as distributions or hyperspectral images as piecewise convex data sets have not been previously developed. Piecewise convex endmember

Alina Zare; Paul Gader

2010-01-01

433

Computational Intelligence Algorithms for Optimized Vehicle Routing Applications in Geographic Information Systems  

Microsoft Academic Search

This project seeks to explore the application of two developing algorithmic paradigms from the field of computational intelligence towards optimized vehicle routing within geographic information systems (GIS). Ant Colony Optimization (ACO) is a type of multi-agent, swarm-based algorithm designed to mimic the emergent problem-solving behavior of real ants within a colony. Genetic Algorithms (GA) are another nature- inspired type of

Michael Rice

434

Partially and Fully Constrained Ant Algorithms for the Optimal Solution of Large Scale Reservoir Operation Problems  

Microsoft Academic Search

This paper presents a constrained formulation of the ant colony optimization algorithm (ACOA) for the optimization of large\\u000a scale reservoir operation problems. ACO algorithms enjoy a unique feature namely incremental solution building capability.\\u000a In ACO algorithms, each ant is required to make a decision at some points of the search space called decision points. If the\\u000a constraints of the problem

M. H. Afshar; R. Moeini

2008-01-01

435

Adaptive Fuzzy Inventory Control Algorithm for Replenishment Process Optimization in an Uncertain Environment  

Microsoft Academic Search

This paper presents a real case study of warehouse replenishment process optimization on a selected sample of representative\\u000a materials. Optimization is performed with simulation model supported by inventory control algorithms. The adaptive fuzzy inventory\\u000a control algorithm based on fuzzy stock-outs, highest stock level and total cost is introduced. The algorithm is tested and\\u000a compared to the simulation results of the

Davorin Kofja?; Miroljub Kljaji?; Andrej Škraba; Blaž Rodi?

436

A comparison of different optimization algorithms for retrieving aerosol optical depths from satellite data: an example of using a dual-angle algorithm  

Microsoft Academic Search

Optimization techniques are often used in remote sensing retrieval of surface or atmospheric parameters. Nevertheless, different algorithms may exhibit different performances for the same optimization problem. Comparison of some classic optimization approaches in this article aims to select the best method for retrieving aerosol opacity, or even for other parameters, from remotely sensed data. Eight frequently used optimization algorithms were

Xihan Mu; Qingfeng Shen; Zhao-Liang Li; Guangjian Yan; José Antonio Sobrino

2011-01-01

437

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

438

Generalized convexity and inequalities  

NASA Astrophysics Data System (ADS)

Let and let be the family of all mean values of two numbers in (some examples are the arithmetic, geometric, and harmonic means). Given , we say that a function is (m1,m2)-convex if f(m1(x,y))[less-than-or-equals, slant]m2(f(x),f(y)) for all . The usual convexity is the special case when both mean values are arithmetic means. We study the dependence of (m1,m2)-convexity on m1 and m2 and give sufficient conditions for (m1,m2)-convexity of functions defined by Maclaurin series. The criteria involve the Maclaurin coefficients. Our results yield a class of new inequalities for several special functions such as the Gaussian hypergeometric function and a generalized Bessel function.

Anderson, G. D.; Vamanamurthy, M. K.; Vuorinen, M.

2007-11-01

439

A hybrid particle swarm optimization-genetic algorithm for optimal location of svc devices in power system planning  

Microsoft Academic Search

The particle swarm optimization (PSO) was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence

Amir Mohammadi; Mostafa Jazaeri

2007-01-01

440

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

441

Optimal multiple source location estimation via the EM algorithm  

Microsoft Academic Search

We developed an algorithm for multiple source localization based on the Estimate-Maximize (EM) method. The EM method is an iterative algorithm that converges to the Maximum Likelihood (ML) estimate of the unknown parameters by exploiting the stochastic syctem under consideration. In our case the algorithm will converge to the exact ML estimates of the various sources location parameters, where each

M. Feder; E. Weinstein

1985-01-01

442

A Scheduling Algorithm to Optimize Real-World Applications  

Microsoft Academic Search

This article presents a scheduling algorithm that assigns tasks represented in a directed acyclic graph (DAG). The behavior of the proposed algorithm is compared with the dominant sequence clustering (DSC) algorithm on a set of DAG tests. The tests were carried out on two sets of DAGs, one with tasks of arbitrary duration, and the other with tasks of unit

Mauricio Solar; Mario Inostroza

2004-01-01

443

A software model to prototype ant colony optimization algorithms  

Microsoft Academic Search

The study of multi-agent systems usually begins by implementing a base-algorithm, which is changed as required by the aim of the research. In this context, carrying out different algorithms, which have already been established, is not a trivial task as it requires implementing these algorithms. This paper presents a software model that allows one to prototype variations of the Ant

Roberto Fernandes Tavares Neto; Moacir Godinho Filho

2011-01-01

444

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

445

A mixed back-propagation\\/Marquardt-Levenberg algorithm for optimizing the distribution electrical systems operation  

Microsoft Academic Search

This paper presents a new methodology to be applied to the distribution electrical systems optimization for reducing the resistive electrical losses of the systems in real-time. This methodology combines the best features of two known algorithms (back-propagation algorithm (BP) and Marquardt-Levenberg algorithm (ML)) for improving the convergence of artificial neural networks (ANN) training. Since sometimes neither the first algorithm nor

E. Gauche; J. Coelho; R. C. G. Teive

1999-01-01

446

Optimal design of a squeeze film damper using an enhanced genetic algorithm  

Microsoft Academic Search

This paper represents that an enhanced genetic algorithm (EGA) is applied to optimal design of a squeeze film damper (SFD)\\u000a to minimize the maximum transmitted load between the bearing and foundation in the operational speed range. A general genetic\\u000a algorithm (GA) is well known as a useful global optimization technique for complex and nonlinear optimization problems. The\\u000a EGA consists of

Young Kong Ahn; Young-Chan Kim; Bo-Suk Yang

2003-01-01

447

Experimental adaptive optimization of mass spectrometer ion optic voltages using a genetic algorithm  

Microsoft Academic Search

We use a genetic algorithm (GA) to optimize the mass resolution and detection efficiency of a multi-photon ionization, time-of-flight mass spectrometer. The algorithm uses experimental fitness functions to optimize eight voltages supplied to a set of ion optics. The GA optimized the ion detection efficiency by a factor of 10 and the mass resolution by a factor of 11 over

Scott D. Carpenter; Carolyn P. Schick; Peter M. Weber

1999-01-01

448

Low emittance lattice optimization using a multi-objective evolutionary algorithm  

NASA Astrophysics Data System (ADS)

A low emittance lattice design and optimization procedure are systematically studied with a non-dominated sorting-based multi-objective evolutionary algorithm which not only globally searches the low emittance lattice, but also optimizes some beam quantities such as betatron tunes, momentum compaction factor and dispersion function simultaneously. In this paper the detailed algorithm and lattice design procedure are presented. The Hefei light source upgrade project storage ring lattice, with fixed magnet layout, is designed to illustrate this optimization procedure.

Gao, Wei-Wei; Wang, Lin; Li, Wei-Min; He, Duo-Hui

2011-09-01

449

Optimal design of viscous damper connectors for adjacent structures using genetic algorithm and Nelder-Mead algorithm  

NASA Astrophysics Data System (ADS)

Passive dampers can be used to connect two adjacent structures in order to mitigate earthquakes induced pounding damages. Theoretical and experimental studies have confirmed efficiency and applicability of various connecting devices, such as viscous damper, MR damper, etc. However, few papers employed optimization methods to find the optimal mechanical properties of the dampers, and in most papers, dampers are assumed to be uniform. In this study, we optimized the optimal damping coefficients of viscous dampers considering a general case of non-uniform damping coefficients. Since the derivatives of objective function to damping coefficients are not known, to optimize damping coefficients, a heuristic search method, i.e. the genetic algorithm, is employed. Each structure is modeled as a multi degree of freedom dynamic system consisting of lumped-masses, linear springs and dampers. In order to examine dynamic behavior of the structures, simulations in frequency domain are carried out. A pseudo-excitation based on Kanai-Tajimi spectrum is used as ground acceleration. The optimization results show that relaxing the uniform dampers coefficient assumption generates significant improvement in coupling effectiveness. To investigate efficiency of genetic algorithm, solution quality and solution time of genetic algorithm are compared with those of Nelder-Mead algorithm.

Bigdeli, Kasra; Hare, Warren; Tesfamariam, Solomon

2012-03-01

450

A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound  

Microsoft Academic Search

A novel feature selection algorithm is presented based on the global minimization of a data-dependent generalization error bound. Feature selection and scaling algorithms often lead to non-convex optimization problems, which in many previous approaches were addressed through gradient descent procedures that can only guarantee convergence to a lo- cal minimum. We propose an alternative approach, whereby the global solution of

Dori Peleg; Ron Meir

2004-01-01

451

A fast and accurate first-order algorithm for compressed sensing  

Microsoft Academic Search

This paper introduces a new, fast and accurate algorithm for solving problems in the area of compressed sensing, and more generally, in the area of signal and image reconstruction from indirect measurements. This algorithm is inspired by recent progress in the development of novel first-order methods in convex optimization, most notably Nesterov's smoothing technique. In particular, there is a crucial

Jerome Bobin; Emmanuel J. Candès

2009-01-01

452

Multi-label Moves for MRFs with Truncated Convex Priors  

NASA Astrophysics Data System (ADS)

Optimization with graph cuts became very popular in recent years. As more applications rely on graph cuts, different energy functions are being employed. Recent evaluation of optimization algorithms showed that the widely used swap and expansion graph cut algorithms have an excellent performance for energies where the underlying MRF has Potts prior. Potts prior corresponds to assuming that the true labeling is piecewise constant. While surprisingly useful in practice, Potts prior is clearly not appropriate in many circumstances. However for more general priors, the swap and expansion algorithms do not perform as well. Both algorithms are based on moves that give each pixel a choice of only two labels. Therefore such moves can be referred to as binary moves. Recently, range moves that act on multiple labels simultaneously were introduced. As opposed to swap and expansion, each pixel has a choice of more than two labels in a range move. Therefore we call them multi-label moves. Range moves were shown to work better for problems with truncated convex priors, which imply a piecewise smooth labeling. Inspired by range moves, we develop several different variants of multi-label moves. We evaluate them on the problem of stereo correspondence and discuss their relative merits.

Veksler, Olga

453

Algorithms for Optimizing Rheology and Loading Forces in Finite Element Models of Lithospheric Deformation  

NASA Astrophysics Data System (ADS)

Lithospheric deformation results from dynamic interplay of tectonic driving forces (loading) and lithospheric properties (rheology and structure). Unlike engineering problems, in lithospheric dynamics both the loading conditions and lithospheric properties are often hard to constrain, forcing many computer models to oversimplification. These simplified models usually cannot take the full advantage of the fast growing observational constraints. In this article, we present algorithms that help to seek optimal loading conditions and rheological parameters in models of lithospheric deformation. In particular, we use genetic algorithms to iterate for the optimal rheological structure, and a regression algorithm for optimizing tectonic loading. We illustrate these algorithms in two models: a plate flexure and a viscous three-dimensional lithospheric deformation. In both cases these algorithms utilize the observational constraints to obtain the optimal driving forces and lithospheric rheology. The results significantly improve over those derived from traditional approaches.

Yang, Youqing; Liu, Mian

454

MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)  

EPA Science Inventory

A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

455

Harmonic optimization in multi-level inverters using harmony search algorithm  

Microsoft Academic Search

This paper presents a new method to optimize harmonic stepped waveform for multi-level inverters using harmony search algorithm. The method has the benefit of high rate of convergence and precision compared to other conventional optimization methods. The proposed technique can be applied to multi-level inverters with any number of levels. The goal of optimization is to eliminate some low order

B. Majidi; H. R. Baghaee; G. B. Gharehpetian; J. Milimonfared; M. Mirsalim

2008-01-01

456

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

457

Algorithm of Fast Marriage in Honey Bees Optimization and Convergence Analysis  

Microsoft Academic Search

Marriage in Honey Bees Optimization (MBO) is a new optimization technique to simulate the social system. But the calculation process is complex and the speed is slow. The paper proposed a faster Marriage in Honey Bees Optimization (FMBO) algorithm with global convergence. By randomly initializing drones and restricting the condition of iteration, the computation process becomes easier and faster. The

Chenguang Yang; Jie Chen; Xuyan Tu

2007-01-01

458

Online optimization of an engine controller by means of a genetic algorithm using history of search  

Microsoft Academic Search

In the present paper, online optimization of an engine controller by means of genetic algorithms (GA) is discussed. In optimization of real complex systems through experiments and computer simulation using random variables, optimization methods must cope with uncertainty of objective function and limitation of possible number of evaluation. Sano et al. (2000) proposed a GA utilizing history of search (GA

Y. Sano; H. Kita; I. Kamihira; M. Yamaguchi

2000-01-01

459

Models for the optimization of promotion campaigns: exact and heuristic algorithms  

Microsoft Academic Search

This paper presents an optimization model for the selection of sets of clients that will receive an offer for one or more products during a promotion campaign. The complexity of the problem makes it very difficult to produce optimal solutions using standard optimization methods. We propose an alternative set covering formulation and develop a branch-and-price algorithm to solve it. We

Fabrice Talla Nobibon; Roel Leus; Frederik Spieksma

2008-01-01

460

An improved ACO algorithm to optimize the agricultural commodity distribution routing under BtoC environment  

Microsoft Academic Search

With the rapid development of electronic commerce, the logistics distribution system brings to the widespread attention. And the agricultural commodity distribution routing (ACDR) optimization is playing the very important role in the agricultural industry. This paper proposed the ant colony optimization (ACO) algorithm to optimize the agricultural commodity distribution routing effectively, which uses the ACO advantages of the concurrency, the

Liang Wang; Tong Li; Tielin Li; Zhibin Liu

2009-01-01

461

Supplementary optimality properties of the restoration phase of sequential gradient-restoration algorithms for optimal control problems  

Microsoft Academic Search

In this paper, sequential gradient-restoration algorithms for optimal control problems are considered, and attention is focused on the restoration phase. It is shown that the Lagrange multipliers associated with the restoration phase not only solve the auxiliary minimization problem of the restoration phase, but are also endowed with a supplementary optimality property: they minimize a special functional, quadratic in the

A. Miele; T. Wang

1983-01-01

462

Optimization and Improvement of FOA Corner Cube Algorithm  

SciTech Connect

Alignment of laser beams based on video images is a crucial task necessary to automate operation of the 192 beams at the National Ignition Facility (NIF). The final optics assembly (FOA) is the optical element that aligns the beam into the target chamber. This work presents an algorithm for determining the position of a corner cube alignment image in the final optics assembly. The improved algorithm was compared to the existing FOA algorithm on 900 noise-simulated images. While the existing FOA algorithm based on correlation with a synthetic template has a radial standard deviation of 1 pixel, the new algorithm based on classical matched filtering (CMF) and polynomial fit to the correlation peak improves the radial standard deviation performance to less than 0.3 pixels. In the new algorithm the templates are designed from real data stored during a year of actual operation.

McClay, W A; Awwal, A S; Burkhart, S C; Candy, J V

2004-10-01

463

Data mining with an ant colony optimization algorithm  

Microsoft Academic Search

This work proposes an algorithm for data mining called Ant-Miner (Ant Colony-based Data Miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts and principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for

Rafael S. Parpinelli; Heitor S. Lopes; Alex Alves Freitas

2002-01-01

464

Optimal support arrangement of piping systems using genetic algorithm  

SciTech Connect

The support arrangement is one of the important factors in the design of piping systems. Much time is required to decide the arrangement of the supports. The authors applied a genetic algorithm to find the optimum support arrangement for piping systems. Examples are provided to illustrate the effectiveness of the genetic algorithm. Good results are obtained when applying the genetic algorithm to the actual designing of the piping system.

Chiba, T.; Okado, S.; Fujii, I.; Itami, K. [Ishikawajima-Harima Heavy Industries Co., Ltd., Yokohama (Japan). Nuclear Power Div.; Hara, F. [Science Univ. of Tokyo (Japan)

1996-11-01

465

A Constrained Optimization Algorithm for Total Energy Minimizationin Electronic Structure Calculation  

SciTech Connect

A new direct constrained optimization algorithm forminimizing the Kohn-Sham (KS) total energy functional is presented inthis paper. The key ingredients of this algorithm involve projecting thetotal energy functional into a sequences of subspaces of small dimensionsand seeking the minimizer of total energy functional within eachsubspace. The minimizer of a subspace energy functional not only providesa search direction along which the KS total energy functional decreasesbut also gives an optimal "step-length" to move along this searchdirection. A numerical example is provided to demonstrate that this newdirect constrained optimization algorithm can be more efficient than theself-consistent field (SCF) iteration.

Yang, Chao; Meza, Juan C.; Wang, Lin-Wang

2005-07-26

466

A constrained optimization algorithm for total energy minimization in electronic structure calculations  

SciTech Connect

A new direct constrained optimization algorithm for minimizing the Kohn-Sham (KS) total energy functional is presented in this paper. The key ingredients of this algorithm involve projecting the total energy functional into a sequence of subspaces of small dimensions and seeking the minimizer of total energy functional within each subspace. The minimizer of a subspace energy functional not only provides a search direction along which the KS total energy functional decreases but also gives an optimal 'step-length' to move along this search direction. Numerical examples are provided to demonstrate that this new direct constrained optimization algorithm can be more efficient than the self-consistent field (SCF) iteration.

Yang Chao [Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 (United States)]. E-mail: cyang@lbl.gov; Meza, Juan C. [Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 (United States); Wang Linwang [Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 (United States)

2006-09-20

467

Metaheuristic algorithms in structural dynamics: An application of tuned mass damper optimization  

NASA Astrophysics Data System (ADS)

Metaheuristic algorithms imitate natural phenomena in order to solve optimization problems. These algorithms are effective on the optimization of structural dynamics problems including vibration control with tuned mass damper (TMD). In this paper, structural dynamics optimization problems were briefly reviewed. As an example, a TMD optimization problem was presented. Harmony Search (HS) algorithm was used to find optimum parameters of TMD mass, stiffness and damping coefficient. The optimization process was conducted to reduce structural displacements of a five story structure. The properties of the structure are the same for all stories except the third story mass. According to the analyses results, the TMD optimized with HS approach is effective to reduce all maximum story displacements.

Bekda?, Gebrail; Nigdeli, Sinan Melih

2012-09-01

468

An optimal coarse-grained arc consistency algorithm  

Microsoft Academic Search

The use of constraint propagation is the main feature of any constraint solver. It is thus of prime importance to manage the propagation in an efficient and effective fashion. There are two classes of propagation algorithms for general constraints: fine-grained algorithms where the removal of a value for a variable will be propagated to the corresponding values for other variables,

Christian Bessière; Jean-charles Régin; Roland H. C. Yap; Yuanlin Zhang

2005-01-01

469

Bi-Criterion Optimization with Multi Colony Ant Algorithms  

Microsoft Academic Search

Abstract: this paper we propose a new approachtosolve bi-criterionoptimization problems with ant algorithms where several colonies of antscooperate innding good solutions. Weintroduce two methods for cooperationbetween the colonies and compare them with a multistart antalgorithm that corresponds to the case of no cooperation. Heterogeneouscolonies are used in the algorithm, i.e. the ants dier in their preferencesbetween the two criteria. Every

Steffen Iredi; Daniel Merkle; Martin Middendorf

2001-01-01

470

An efficient algorithm for optimizing adaptive quantum metrology processes  

Microsoft Academic Search

We introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures to replace what is otherwise an difficult and inefficient problem. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

Barry C. Sanders; Alexander Hentschel

2011-01-01

471

Efficient approximation and optimization algorithms for computational metrology  

Microsoft Academic Search

We give efficient algorithms for solving several geometricproblems in computational metrology, focusing on the fundamentalissues of "flatness" and "roundness." Specifically,we give approximate and exact algorithms for 2- and3-dimensional roundness primitives, deriving results thatimprove previous approaches in several respects, includingproblem definition, running time, underlying computationalmodel, and dimensionality of the input. We also study methodsfor determining the width of a...

Christian A. Duncan; Michael T. Goodrich; Edgar A. Ramos

1997-01-01

472

Application of genetic algorithm for optimization gasoline fractions blending compounding  

Microsoft Academic Search

The paper deals with the application of a genetic algorithm for the solution of the task of gasoline fraction blending compounding, keeping the given conditions on octane numbers and the amount of given types of commodity gasolines. The principle of coding solutions is described. Several results of experiments on the determination of the effective genetic algorithm configuration are given.

J. A. Burgher; V. S. Vyshemirskij; N. A. Sokolova

2002-01-01

473

Optimizing for Reduced Code Space using Genetic Algorithms  

Microsoft Academic Search

Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimizations are designed to reduce the execution time of compiled code, but not necessarily the size of the compiled code. Further, different results can be achieved by running some optimizations more than once and changing the order in which optimizations are applied. Register allocation

Keith D. Cooper; Philip J. Schielke; Devika Subramanian

1999-01-01

474

Optimization of UHF RFID tag antennas using a genetic algorithm  

Microsoft Academic Search

UHF band (860~960 MHz) RFID tag strip-line antennas for non-metallic object and slotted patch RFID antenna for metallic objects have been optimized with a GA. The antennas are optimized for commercially available RFID tag IC chips. Different cell sizes of FDTD have been tried while the GA optimizes the symmetrical shape of RFID antennas

Goojo Kim; YouChung Chung

2006-01-01

475

Optimum band selection in hyperspectral imagery using swarm intelligence optimization algorithms  

Microsoft Academic Search

Despite rich and fine spectral information of hyperspectral imagery, curse of dimensionality and Hughes phenomenon affect the land use\\/cover classification accuracy of such images. In this situation, optimal feature\\/band selection based on optimization procedures has high potential to improve the accuracy of hyperspectral image pattern recognition and classification. Among other optimization techniques, Meta heuristic optimization algorithms such as Swarm intelligence-based

F. Samadzadegan; F. Tabib Mahmoudi

2011-01-01

476

Investigation into the optimization control technique of hydrogen-fueled engines based on genetic algorithms  

Microsoft Academic Search

An optimization control model which is about the relationship between power, economy, emission performances and operating parameters of hydrogen-fueled engines is established in the text, and optimality of the solution's Pareto is realized through multi-objective optimization algorithm which is combined with Pareto rank and partial decision-making, and the optimization process of multi-objective is carried out parallelly in the searching process,

Zhenzhong Yang; Lijun Wang; Shilei Li

2008-01-01

477

Image Segmentation of Thermal Waving Inspection based on Particle Swarm Optimization Fuzzy Clustering Algorithm  

NASA Astrophysics Data System (ADS)

The Fuzzy C-Mean clustering (FCM) algorithm is an effective image segmentation algorithm which combines the clustering of non-supervised and the idea of the blurry aggregate, it is widely applied to image segmentation, but it has many problems, such as great amount of calculation, being sensitive to initial data values and noise in images, and being vulnerable to fall into the shortcoming of local optimization. To conquer the problems of FCM, the algorithm of fuzzy clustering based on Particle Swarm Optimization (PSO) was proposed, this article first uses the PSO algorithm of a powerful global search capability to optimize FCM centers, and then uses this center to partition the images, the speed of the image segmentation was boosted and the segmentation accuracy was improved. The results of the experiments show that the PSO-FCM algorithm can effectively avoid the disadvantage of FCM, boost the speed and get a better image segmentation result.

Guofeng, Jin; Wei, Zhang; Zhengwei, Yang; Zhiyong, Huang; Yuanjia, Song; Dongdong, Wang; Gan, Tian

2012-12-01

478

Digital Pattern Search and Its Hybridization with Genetic Algorithms for Bound Constrained Global Optimization  

NASA Astrophysics Data System (ADS)

In this paper, we present a recently developed pattern search method called Genetic Pattern Search algorithm (GPSA) for the global optimization of cost function subject to simple bounds. GPSA is a combined global optimization method using genetic algorithm (GA) and Digital Pattern Search (DPS) method, which has the digital structure represented by binary strings and guarantees convergence to stationary points from arbitrary starting points. The performance of GPSA is validated through extensive numerical experiments on a number of well known functions and on robot walking application. The optimization results confirm that GPSA is a robust and efficient global optimization method.

Kim, Nam-Geun; Park, Youngsu; Kim, Jong-Wook; Kim, Eunsu; Kim, Sang Woo

479

Optimization of Electric Power Leveling Systems by a Novel Hybrid Algorithm with Simulated Evolution and Genetic Algorithms  

NASA Astrophysics Data System (ADS)

Electric power demand has an increasing tendency year by year. The fluctuation of the electric power causes further increase in the cost of the electric power facility and electricity charges. The development of the electric power-leveling systems (EPLS) using energy storage technology is desired to improve the electric power quality. The EPLS with a SMES is proposed as one of the countermeasures for the electric power quality improvement. However, the SMES is very expensive and it is difficult to decide the gains of the controller. It is essential in the practical use that the reduction of SMES capacity is realized. This paper proposes a new optimization method of the EPLS. The proposed algorithm is hybrid architecture with a combination of SimE (Simulated Evolution) and GA (Genetic Algorithms). The optimization of the EPLS can be achieved by the proposed hybrid algorithm compared to the SimE and the GA.

Itoh, Jyunpei; Yamamoto, Masayoshi; Funabiki, Shigeyuki

480

Calibration of Neural Networks Using Genetic Algorithms, with Application to Optimal Path Planning.  

National Technical Information Service (NTIS)

Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other te...

T. R. Smith G. A. Pitney D. Greenwood

1987-01-01

481

Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem  

PubMed Central

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-01-01

482

Application of particle swarm optimization algorithm in the heating system planning problem.  

PubMed

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem. PMID:23935429

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-07-01

483

CALIBRATION, OPTIMIZATION, AND SENSITIVITY AND UNCERTAINTY ALGORITHMS APPLICATION PROGRAMMING INTERFACE (COSU-API)  

EPA Science Inventory

The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, and Parameter Estimation (UA/SA/PE API) tool development, here fore referred to as the Calibration, Optimization, and Sensitivity and Uncertainty Algorithms API (COSU-API), was initially d...

484

Optimization of stiffened plates by genetic search  

Microsoft Academic Search

This paper deals with the optimization of stiffeners on plates by varying their positions, while having well-defined dimensions. Considering the nonlinearity, the non-convexity and the discontinuity of this problem, we have chosen to use a stochastic method, that is the genetic algorithm (GA). This work is above all a study of feasibility. Some improvements in the GA have been used

A. Kallassy; J.-L. Marcelin

1997-01-01

485

Optimal Resource Allocation for OFDMA Downlink Systems  

Microsoft Academic Search

This paper proposes efficient rate and power allocation algorithms for OFDMA downlink systems where each tone is taken by at most one user. Weighted sum rate maximization (WSRmax) and weighted sum power minimization (WSPmin) problems are considered. Since these resource allocation problems are non-convex, complexity of finding the optimal solutions is prohibitively high. This paper employs the Lagrange dual decomposition

Kibeom Seong; Mehdi Mohseni; John M. Cioffi

2006-01-01

486

An ellipsoid algorithm for probabilistic robust controller design  

Microsoft Academic Search

In this paper, a new iterative approach to probabilistic robust controller design is presented, which is applicable to any robust controller\\/filter design problem that can be represented as an LMI feasibility problem. Recently, a probabilistic Subgradient Iteration algorithm was proposed for solving LMIs. It transforms the initial feasibility problem to an equivalent convex optimization problem, which is subsequently solved by

S. Kanev; B. De Schutter; M. Verhaegen

2003-01-01

487

Unidirectional Loop Facility Layout Optimization Design Based on Niche Genetic Algorithm  

Microsoft Academic Search

\\u000a Based on the study of the unidirectional loop layout, this paper analyzed the necessity of genetic algorithm to the workshop\\u000a layout optimization. To minimize transportation travel, the mathematical model of the unidirectional loop workshop facility\\u000a layout was set up, using the niche genetic algorithm with introduction of penalty function and elite reservation strategy\\u000a to accomplish the facility layout optimization. Through

Lu Tong-Tong; Lu Chao; Han Jun

488

An efficient parallel optimization algorithm for the Token Bucket control mechanism  

Microsoft Academic Search

The Token Bucket algorithm, one of the most widely used control mechanism in computer communication network, has been extensively used to ensure the QoS needs for various applications. Recently, [N.U. Ahmed, Bo Li, Luis Orozco-Barbosa, Modelling and optimization of computer network traffic controllers, Mathematical Problems in Engineering, 2005, 6(2005), 617–640] an optimization technique, based on dynamic programming and genetic algorithm,

N. U. Ahmed; X. Lu; L. O. Barbosa

2006-01-01

489

Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks  

Microsoft Academic Search

Training an artificial neural network is an optimization task since it is desired to find optimal weight set of a neural network\\u000a in training process. Traditional training algorithms has some drawbacks such as getting stuck in local minima and computational\\u000a complexity. Therefore, evolutionary algorithms are employed to train neural networks to overcome these issues. In this work,\\u000a Artificial Bee Colony

Dervis Karaboga; Bahriye Akay; Celal Ozturk

2007-01-01

490

Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots  

Microsoft Academic Search

A novel method for the real-time globally optimal path planning of mobile robots is proposed based on the ant colony system (ACS) algorithm. This method includes three steps: the first step is utilizing the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is utilizing the Dijkstra algorithm to find a sub-optimal collision-free

Guan-Zheng TAN; Huan HE; Aaron SLOMAN

2007-01-01

491

A Stochastic Augmented Lagrangian Equality Constrained-Based Algorithm for Global Optimization  

NASA Astrophysics Data System (ADS)

This paper presents a numerical study of a stochastic augmented Lagrangian algorithm to solve continuous constrained global optimization problems. The algorithm approximately solves a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is solved by a population-based method that uses an electromagnetism-like mechanism to move points towards optimality. A comparison with another stochastic technique is also reported.

Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.

2010-09-01

492

A Genetic Algorithm-Based Multiobjective Optimization for Analog Circuit Design  

Microsoft Academic Search

Multiple, often conflicting objectives are specific to analog design. This paper presents a multiobjective optimization algorithm\\u000a based on GA for design optimization of analog circuits. The fitness of each individual in the population is determined using\\u000a a multiobjective ranking method. The algorithm found a set of feasible solutions on the Pareto front. Thus, the circuit designers\\u000a can explore more possible

Gabriel Oltean; Sorin Hintea; Emilia Sipos

2009-01-01

493

Scalability of a Hybrid Extended Compact Genetic Algorithm for Ground State Optimization of Clusters  

Microsoft Academic Search

We analyze the utility and scalability of extended compact genetic algorithm (eCGA)—a genetic algorithm (GA) that automatically and adaptively mines the regularities of the fitness landscape using machine learning methods and information theoretic measures—for ground state optimization of clusters. In order to reduce the computational time requirements while retaining the high reliability of predicting near-optimal structures, we employ two efficiency-enhancement

Kumara Sastry; David. E. Goldberg; D. D. Johnson

2007-01-01

494

An Improved Genetic Algorithm for Target Assignment, Optimization of Naval Fleet Air Defense  

Microsoft Academic Search

As a weapon-target assignment problem (WTA), the fire-allocation of naval fleet air defense was studied and an optimal target assignment model was established. By combining the features of naval fleet anti-air combat, several classical assignment algorithms were discussed. To find global optimal results efficiently, an effective global search method i.e. genetic algorithms (GAs) was applied. However, this method still has

Houqing Lu; Hongjun Zhang; Xiaojuan Zhang; Ruixin