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1

A Computational Study of the Homogeneous Algorithm for Large-scale Convex Optimization  

Microsoft Academic Search

Recently the authors have proposed a homogeneous and self-dual algorithm for solving the monotone complementarity problem (MCP) [5]. The algorithm is a single phase interior-point type method; nevertheless, it yields either an approximate optimal solution or detects a possible infeasibility of the problem. In this paper we specialize the algorithm to the solution of general smooth convex optimization problems, which

Erling D. Andersen; Yinyu Ye

1998-01-01

2

Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm  

PubMed Central

The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in the article, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity X-ray illumination is presented.

Sidky, Emil Y.; J?rgensen, Jakob H.; Pan, Xiaochuan

2012-01-01

3

Algorithms for optimal area triangulations of a convex polygon  

Microsoft Academic Search

Given a convex polygon with n vertices in the plane, we are interested in triangulations of its interior, i.e., maximal sets of non-intersecting diagonals that subdivide the interior of the polygon into triangles. The MaxMin area triangulation is the triangulation of the polygon that maximizes the area of the smallest triangle in the triangulation. Similarly, the MinMax area triangulation is

J. Mark Keil; Tzvetalin S. Vassilev

2006-01-01

4

Convex Optimization II  

NSDL National Science Digital Library

Concentrates on recognizing and solving convex optimization problems that arise in engineering.Topics include: Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.Prerequisites: Good knowledge of linear algebra. Exposure to numerical computing, optimization, and application fields helpful but not required; the engineering applications will be kept basic and simple.

Boyd, Stephen

2010-12-13

5

Convex Optimization I  

NSDL National Science Digital Library

Concentrates on recognizing and solving convex optimization problems that arise in engineering.Topics include: Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.Prerequisites: Good knowledge of linear algebra. Exposure to numerical computing, optimization, and application fields helpful but not required; the engineering applications will be kept basic and simple.

Boyd, Stephen

2010-12-10

6

Implementation of a Point Algorithm for Real-Time Convex Optimization  

NASA Technical Reports Server (NTRS)

The primal-dual interior-point algorithm implemented in G-OPT is a relatively new and efficient way of solving convex optimization problems. Given a prescribed level of accuracy, the convergence to the optimal solution is guaranteed in a predetermined, finite number of iterations. G-OPT Version 1.0 is a flight software implementation written in C. Onboard application of the software enables autonomous, real-time guidance and control that explicitly incorporates mission constraints such as control authority (e.g. maximum thrust limits), hazard avoidance, and fuel limitations. This software can be used in planetary landing missions (Mars pinpoint landing and lunar landing), as well as in proximity operations around small celestial bodies (moons, asteroids, and comets). It also can be used in any spacecraft mission for thrust allocation in six-degrees-of-freedom control.

Acikmese, Behcet; Motaghedi, Shui; Carson, John

2007-01-01

7

From Nonlinear Optimization to Convex Optimization through Firefly Algorithm and Indirect Approach with Applications to CAD/CAM  

PubMed Central

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

Galvez, Akemi; Iglesias, Andres

2013-01-01

8

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

9

Optimal Inequalities in Probability Theory: A Convex Optimization Approach  

Microsoft Academic Search

Abstract. We propose a semidefinite optimization approach to the problem of deriving tight moment inequalities for P (X ? S), for a set S defined by polynomial inequalities and a random vector X defined on ? ?R ,, we present an efficient algorithm for finding tight bounds when S is a union of convex sets, over which convex quadratic functions

Dimitris Bertsimas; Ioana Popescu

2005-01-01

10

A Convex Guidance Algorithm for Formation Reconfiguration  

NASA Technical Reports Server (NTRS)

In this paper, a reconfiguration guidance algorithm for formation flying spacecraft is presented. The formation reconfiguration guidance problem is first formulated as a continuous-time minimum-fuel or minimum-energy optimal control problem with collision avoidance and control constraints. The optimal control problem is then discretized to obtain a finite dimensional parameter optimization problem. In this formulation, the collision avoidance constraints are imposed via separating planes between each pair of spacecraft. A heuristic is introduced to choose these separating planes that leads to the convexification of the collision avoidance constraints. Additionally, convex constraints are imposed to guarantee that no collisions occur between discrete time samples. The resulting finite dimensional optimization problem is a second order cone program, for which standard algorithms can compute the global optimum with deterministic convergence and a prescribed level of accuracy. Consequently, the formation reconfiguration algorithm can be implemented onboard a spacecraft for real-time operations.

Acikmese, A. Behcet; Schar, Daniel P.; Murray, Emmanuell A.; Hadaeghs, Fred Y.

2006-01-01

11

Convex separable optimization is not much harder than linear optimization  

Microsoft Academic Search

The polynomiality of nonlinear separable convex (concave) optimization problems, on linear constraints with a matrix with “small” subdeterminants, and the polynomiality of such integer problems, provided the inteter linear version of such problems ins polynomial, is proven. This paper presents a general-purpose algorithm for converting procedures that solves linear programming problems. The conversion is polynomial for constraint matrices with polynomially

Dorit S. Hochbaum; J. George Shanthikumar

1990-01-01

12

Robust boosting via convex optimization  

NASA Astrophysics Data System (ADS)

In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseen examples. We consider the case where the resulting classification or regression rule is a combination of simple rules - also called base hypotheses. The so-called boosting algorithms iteratively find a weighted linear combination of base hypotheses that predict well on unseen data. We address the following issues: o The statistical learning theory framework for analyzing boosting methods. We study learning theoretic guarantees on the prediction performance on unseen examples. Recently, large margin classification techniques emerged as a practical result of the theory of generalization, in particular Boosting and Support Vector Machines. A large margin implies a good generalization performance. Hence, we analyze how large the margins in boosting are and find an improved algorithm that is able to generate the maximum margin solution. o How can boosting methods be related to mathematical optimization techniques? To analyze the properties of the resulting classification or regression rule, it is of high importance to understand whether and under which conditions boosting converges. We show that boosting can be used to solve large scale constrained optimization problems, whose solutions are well characterizable. To show this, we relate boosting methods to methods known from mathematical optimization, and derive convergence guarantees for a quite general family of boosting algorithms. o How to make Boosting noise robust? One of the problems of current boosting techniques is that they are sensitive to noise in the training sample. In order to make boosting robust, we transfer the soft margin idea from support vector learning to boosting. We develop theoretically motivated regularized algorithms that exhibit a high noise robustness. o How to adapt boosting to regression problems? Boosting methods are originally designed for classification problems. To extend the boosting idea to regression problems, we use the previous convergence results and relations to semi-infinite programming to design boosting-like algorithms for regression problems. We show that these leveraging algorithms have desirable theoretical and practical properties. o Can boosting techniques be useful in practice? The presented theoretical results are guided by simulation results either to illustrate properties of the proposed algorithms or to show that they work well in practice. We report on successful applications in a non-intrusive power monitoring system, chaotic time series analysis and a drug discovery process. --- Anmerkung: Der Autor ist Träger des von der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam vergebenen Michelson-Preises für die beste Promotion des Jahres 2001/2002. In dieser Arbeit werden statistische Lernprobleme betrachtet. Lernmaschinen extrahieren Informationen aus einer gegebenen Menge von Trainingsmustern, so daß sie in der Lage sind, Eigenschaften von bisher ungesehenen Mustern - z.B. eine Klassenzugehörigkeit - vorherzusagen. Wir betrachten den Fall, bei dem die resultierende Klassifikations- oder Regressionsregel aus einfachen Regeln - den Basishypothesen - zusammengesetzt ist. Die sogenannten Boosting Algorithmen erzeugen iterativ eine gewichtete Summe von Basishypothesen, die gut auf ungesehenen Mustern vorhersagen. Die Arbeit behandelt folgende Sachverhalte: o Die zur Analyse von Boosting-Methoden geeignete Statistische Lerntheorie. Wir studieren lerntheoretische Garantien zur Abschätzung der Vorhersagequalität auf ungesehenen Mustern. Kürzlich haben sich sogenannte Klassifikationstechniken mit großem Margin als ein praktisches Ergebnis dieser Theorie herausgestellt - insbesondere Boosting und Support-Vektor-Maschinen. Ein großer Margin impliziert eine hohe Vorhersagequalität der Entscheidungsregel. Deshalb wird analysiert, wie groß der Margin bei Boosting ist und ein verbesserter Algorithmus vorgeschl

Rätsch, Gunnar

2001-12-01

13

A non-parametric heuristic algorithm for convex and non-convex data clustering based on equipotential surfaces  

Microsoft Academic Search

In this paper, using the concepts of field theory and potential functions a sub-optimal non-parametric algorithm for clustering of convex and non-convex data is proposed. For this purpose, equipotential surfaces, created by interaction of the potential functions, are applied. Equipotential surfaces are the geometric location of the points in the space on which the potential is constant. It means all

Farhad Bayat; Ehsan Adeli Mosabbeb; Ali Akbar Jalali; Farshad Bayat

2010-01-01

14

First-order convex feasibility algorithms for x-ray CT  

SciTech Connect

Purpose: Iterative image reconstruction (IIR) algorithms in computed tomography (CT) are based on algorithms for solving a particular optimization problem. Design of the IIR algorithm, therefore, is aided by knowledge of the solution to the optimization problem on which it is based. Often times, however, it is impractical to achieve accurate solution to the optimization of interest, which complicates design of IIR algorithms. This issue is particularly acute for CT with a limited angular-range scan, which leads to poorly conditioned system matrices and difficult to solve optimization problems. In this paper, we develop IIR algorithms which solve a certain type of optimization called convex feasibility. The convex feasibility approach can provide alternatives to unconstrained optimization approaches and at the same time allow for rapidly convergent algorithms for their solution-thereby facilitating the IIR algorithm design process. Methods: An accelerated version of the Chambolle-Pock (CP) algorithm is adapted to various convex feasibility problems of potential interest to IIR in CT. One of the proposed problems is seen to be equivalent to least-squares minimization, and two other problems provide alternatives to penalized, least-squares minimization. Results: The accelerated CP algorithms are demonstrated on a simulation of circular fan-beam CT with a limited scanning arc of 144 Degree-Sign . The CP algorithms are seen in the empirical results to converge to the solution of their respective convex feasibility problems. Conclusions: Formulation of convex feasibility problems can provide a useful alternative to unconstrained optimization when designing IIR algorithms for CT. The approach is amenable to recent methods for accelerating first-order algorithms which may be particularly useful for CT with limited angular-range scanning. The present paper demonstrates the methodology, and future work will illustrate its utility in actual CT application.

Sidky, Emil Y.; Pan Xiaochuan [Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois, 60637 (United States); Jorgensen, Jakob S. [Department of Applied Mathematics and Computer Science, Technical University of Denmark, Matematiktorvet, Building 303B, 2800 Kongens Lyngby (Denmark)

2013-03-15

15

Convex optimization for the design of learning machines  

Microsoft Academic Search

This paper reviews the recent surge of interest in convex optimization in a context of pattern recognition and machine learning. The main thesis of this paper is that the design of task-specific learning machines is aided substantially by using a convex optimization solver as a back-end to implement the task, liberating the designer from the concern of designing and analyzing

Kristiaan Pelckmans; Johan A. K. Suykens; Bart De Moor

2007-01-01

16

Algorithms for Generating Convex Sets in Acyclic  

Microsoft Academic Search

A set X of vertices of an acyclic digraph D is convex if X 6= ; and there is no directed path between vertices of X which contains a vertex not in X. A set X is connected if X 6= ; and the underlying undirected graph of the subgraph of D induced by X is connected. Connected con- vex

P. Balister; S. Gerkey G. Gutinz; A. Johnstone; J. Reddington; E. Scottk; A. Soleimanfallah; A. Yeoyy

17

Convex optimization under inequality constraints in rank-deficient systems  

NASA Astrophysics Data System (ADS)

Many geodetic applications require the minimization of a convex objective function subject to some linear equality and/or inequality constraints. If a system is singular (e.g., a geodetic network without a defined datum) this results in a manifold of solutions. Most state-of-the-art algorithms for inequality constrained optimization (e.g., the Active-Set-Method or primal-dual Interior-Point-Methods) are either not able to deal with a rank-deficient objective function or yield only one of an infinite number of particular solutions. In this contribution, we develop a framework for the rigorous computation of a general solution of a rank-deficient problem with inequality constraints. We aim for the computation of a unique particular solution which fulfills predefined optimality criteria as well as for an adequate representation of the homogeneous solution including the constraints. Our theoretical findings are applied in a case study to determine optimal repetition numbers for a geodetic network to demonstrate the potential of the proposed framework.

Roese-Koerner, Lutz; Schuh, Wolf-Dieter

2014-05-01

18

Stitching algorithm for subaperture test of convex aspheres with a test plate  

NASA Astrophysics Data System (ADS)

Subaperture stitching interferometry combined with a test plate is attractive for testing large convex aspheres, but the stitching algorithm is challenging because the aberrations induced by misaligned test surface or test plate are coupled with the surface figure. By relating the subaperture configuration to the overlapping deviations through ray trace and coordinate transformation, the subaperture misalignment is optimally recognized and corrected to give a minimal overlapping inconsistency in an iterative way. Allowing for misaligned test plate, we decompose the induced aberrations into three parts which are corrected by the stitching algorithm, removed in the form of the Zernike polynomials and left uncorrected as residuals. Finally we present simulation results of testing a convex aspheric mirror with a computer generated hologram which shows the algorithm successfully retrieves the surface figure with the test mirror or the hologram misaligned.

Chen, Shanyong; Zhao, Chunyu; Dai, Yifan; Li, Shengyi

2013-07-01

19

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

2013-10-01

20

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

21

New Results on Optimal Area Triangulations of Convex Polygons  

Microsoft Academic Search

We consider the problems of flnding two optimal triangulations of convex polygon: MaxMin area and MinMax area. These are the triangulations that maximize the area of the smallest area triangle in a triangulation, and respectively minimize the area of the largest area triangle in a triangulation, over all possible triangulations. The problem was originally solved by Klincsek by dynamic programming

Tigran Mirzoev; Tzvetalin S. Vassilev

22

Sample-path optimization of convex stochastic performance functions  

Microsoft Academic Search

In this paper we propose a method for optimizing convex performance functions in stochastic systems. These functions can include\\u000a expected performance in static systems and steady-state performance in discrete-event dynamic systems; they may be nonsmooth.\\u000a The method is closely related to retrospective simulation optimization; it appears to overcome some limitations of stochastic\\u000a approximation, which is often applied to such problems.

Erica L. Plambeck; Bor-ruey Fu; Stephen M. Robinson; Rajan Suri

1996-01-01

23

Position-Patch Based Face Hallucination Using Convex Optimization  

Microsoft Academic Search

We provide a position-patch based face halluci- nation method using convex optimization. Recently, a novel position-patch based face hallucination method has been proposed to save computational time and achieve high-quality hallucinated results. This method has employed least square estimation to obtain the optimal weights for face hallucination. However, the least square estimation approach can provide biased solutions when the number

Cheolkon Jung; Licheng Jiao; Bing Liu; Maoguo Gong

2011-01-01

24

A Fixed Parameter Algorithm for the Minimum Number Convex Partition Problem  

Microsoft Academic Search

\\u000a Given an input consisting of an n-vertex convex polygon with k hole vertices or an n-vertex planar straight line graph (PSLG) with k holes and\\/or reflex vertices inside the convex hull, the parameterized minimum number convex partition (MNCP) problem asks\\u000a for a partition into a minimum number of convex pieces. We give a fixed-parameter tractable algorithm for this problem that\\u000a runs

Magdalene Grantson; Christos Levcopoulos

2004-01-01

25

INTEROP; Nonlinear Optimization Algorithms.  

National Technical Information Service (NTIS)

The INTERactive OPtimization system (INTEROP) is a collection of search direction algorithms for constrained and unconstrained nonlinear optimization problems. The search direction algorithms available in INTEROP include: modified Fletcher-Powell, modifie...

D. M. Rasmuson R. L. Thurgood

1984-01-01

26

Cuckoo Optimization Algorithm  

Microsoft Academic Search

In this paper a novel evolutionary algorithm, suitable for continuous nonlinear optimization problems, is introduced. This optimization algorithm is inspired by the life of a bird family, called Cuckoo. Special lifestyle of these birds and their characteristics in egg laying and breeding has been the basic motivation for development of this new evolutionary optimization algorithm. Similar to other evolutionary methods,

Ramin Rajabioun

2011-01-01

27

Convergent LMI relaxations for non-convex optimization over polynomials in control  

Microsoft Academic Search

Most of the analysis or design problems arising in robust or non-linear control can be formulated as global optimization problems with multivariable polynomial ob- jective and constraints. These non-convex optimization problems, which in general are dicult to solve, have motivated the development of tractable, but potentially conservative relaxation techniques relying on convex optimization and most notably semidenite programming, or optimization

Didier Henrion; Jean-Bernard Lasserre

28

ALGORITHMS AND SOFTWARE FOR CONVEX MIXED INTEGER NONLINEAR PROGRAMS  

Microsoft Academic Search

This paper provides a survey of recent progress and software for solving mixed integer nonlinear programs (MINLP) wherein the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have received sustained attention in very years. By exploiting analogies to the case of well-known techniques for solving mixed

PIERRE BONAMI; MUSTAFA KILINC; JEFF LINDEROTH

29

Convex Optimization-based Beamforming: From Receive to Transmit and Network Designs  

Microsoft Academic Search

In this article, an overview of advanced convex optimization approaches to multi-sensor beamforming is pre- sented, and connections are drawn between different types of optimization-based beamformers that apply to a broad class of receive, transmit, and network beamformer design problems. It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution

Alex B. Gershman; Nicholas D. Sidiropoulos; Shahram Shahbazpanahi; Mats Bengtsson

30

Algorithms for bilevel optimization  

NASA Technical Reports Server (NTRS)

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

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

1994-01-01

31

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

32

Hybrid Bacteria Foraging-DE based algorithm for Economic Load Dispatch with non-convex loads  

Microsoft Academic Search

An algorithm based on hybridization of Bacteria Foraging (BF) and Differential Evolution (DE) was developed to solve the problem of finding the optimum load allocation amongst the committed units in a power system with non-convex loads. The performance of the proposed algorithm is evaluated on a test case of 15 units. The performance of the proposed algorithm is compared with

Nidul Sinha; Loi Lei Lai; L. C. Saikia; T. Malakar

2010-01-01

33

A parallel matching algorithm for convex bipartite graphs and applications to scheduling  

SciTech Connect

An efficient parallel algorithm to obtain maximum matchings in convex bipartite graphs is developed. This algorithm can be used to obtain efficient parallel algorithms for several scheduling problems. Some examples are: job scheduling with release times and deadlines; scheduling to minimize maximum cost; and preemptive scheduling to minimize maximum completion time.

Dekel, E.; Sahni, S.

1984-11-01

34

OPTIMAL INEQUALITIES IN PROBABILITY THEORY: A CONVEX OPTIMIZATION APPROACH  

Microsoft Academic Search

We propose a semidefinite optimization approach to the problem of deriving tight moment inequalities for P (X ? S), for a set S defined by polynomial inequalities and a random vector X defined on ? ?R n that has a given collection of up to kth-order moments. In the univariate case, we provide optimal bounds on P (X ? S),

DIMITRIS BERTSIMAS; IOANA POPESCU

2000-01-01

35

Extension of Karmarkar's algorithm onto convex quadratically constrained quadratic problems  

Microsoft Academic Search

Convex quadratically constrained quadratic problems are considered. It is shown that such problems can be transformed to aconic form. The feasible set of the conic form is the intersection of a direct product of standard quadratic cones intersected with\\u000a a hyperplane (the analogue of a simplex), and a linear subspace. For a problem of such form, the analogue of Karmarkar's

Arkadii Nemirovskii; Katya Scheinberg

1996-01-01

36

Algorithms for Generating Convex Sets in Acyclic Digraphs  

Microsoft Academic Search

A set X of vertices of an acyclic digraph D is convex if X ?= ? and there is no directed path between vertices of X which contains a vertex not in X. A set X is connected if X ?= ? and the underlying undirected graph of the subgraph of D induced by X is connected. Connected con- vex

Paul N. Balister; Stefanie Gerke; Gregory Gutin; Adrian Johnstone; Joseph Reddington; Elizabeth Scott; A. Soleimanfallah; Anders Yeo

2007-01-01

37

Performance Bounds for Optimal Control of Polynomial Systems: A Convex Optimization Approach  

NASA Astrophysics Data System (ADS)

This paper is concerned with an approach for a nonlinear optimal control of polynomial systems. The Hamilton-Jacobi-Bellman (HJB) equation is relaxed into HJB inequalities. Both an upper bound and a lower bound on the cost function, as well as a suboptimal controller, can be computed from solutions of the resulting inequalities. Solving the HJB inequalities can be cast as state-dependent matrix inequalities (SDMIs), whose derivation is based on representation of the given polynomial system in a linear-like form. The resulting SDMI for the upper-bound computation is nonconvex in the decision variables, and hence an iterative procedure is proposed to deal with the non-convexity. On the other hand, the resulting SDMI for the lower-bound computation can be written as a state-dependent linear matrix inequality, which is a convex optimization problem solvable by existing numerical tools. Numerical examples are provided to illustrate the proposed approach.

Jennawasin, Tanagorn; Kawanishi, Michihiro; Narikiyo, Tatsuo

38

Optimal control problems with terminal functionals represented as the difference of two convex functions  

NASA Astrophysics Data System (ADS)

Two control problems for a state-linear control system are considered: the minimization of a terminal functional representable as the difference of two convex functions (d.c. functions) and the minimization of a convex terminal functional with a d.c. terminal inequality contraint. Necessary and sufficient global optimality conditions are proved for problems in which the Pontryagin and Bellman maximum principles do not distinguish between locally and globally optimal processes. The efficiency of the approach is illustrated by examples.

Strekalovsky, A. S.

2007-11-01

39

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

PubMed Central

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

Tran, Giang; Shi, Yonggang

2014-01-01

40

Coordination and Control of Multiple Spacecraft using Convex Optimization Techniques  

NASA Astrophysics Data System (ADS)

Formation flying of multiple spacecraft is an enabling technology for many future space science missions. These future missions will, for example, use the highly coordinated, distributed array of vehicles for earth mapping interferometers and synthetic aperture radar. This thesis presents coordination and control algorithms designed for a fleet of spacecraft. These algorithms are embedded in a hierarchical fleet archi- tecture that includes a high-level coordinator for the fleet maneuvers used to form, re-size, or re-target the formation configuration and low-level controllers to generate and implement the individual control inputs for each vehicle. The trajectory and control problems are posed as linear programming (LP) optimizations to solve for the minimum fuel maneuvers. The combined result of the high-level coordination and low-level controllers is a very flexible optimization framework that can be used off-line to analyze aspects of a mission design and in real-time as part of an on-board autonomous formation flying control system. This thesis also investigates several crit- ical issues associated with the implementation of this formation flying approach. In particular, modifications to the LP algorithms are presented to: include robustness to sensor noise, include actuator constraints, ensure that the optimization solutions are always feasible, and reduce the LP solution times. Furthermore, the dynamics for the control problem are analyzed in terms of two key issues: 1) what dynamics model should be used to specify the desired state to maintain a passive aperture; and 2) what dynamics model should be used in the LP to represent the motion about this state. Several linearized models of the relative dynamics are considered in this analysis, including Hill's equations for circular orbits, modified linear dynamics that partially account for the J2 effects, and Lawden's equations for eccentric orbits.

How, Jonathan P.

2002-06-01

41

Optimization and Openmp Parallelization of a Discrete Element Code for Convex Polyhedra on Multi-Core Machines  

NASA Astrophysics Data System (ADS)

We report our experiences with the optimization and parallelization of a discrete element code for convex polyhedra on multi-core machines and introduce a novel variant of the sort-and-sweep neighborhood algorithm. While in theory the whole code in itself parallelizes ideally, in practice the results on different architectures with different compilers and performance measurement tools depend very much on the particle number and optimization of the code. After difficulties with the interpretation of the data for speedup and efficiency are overcome, respectable parallelization speedups could be obtained.

Chen, Jian; Matuttis, Hans-Georg

2013-02-01

42

A low-order decomposition of turbulent channel flow via resolvent analysis and convex optimization  

NASA Astrophysics Data System (ADS)

We combine resolvent-mode decomposition with techniques from convex optimization to optimally approximate velocity spectra in a turbulent channel. The velocity is expressed as a weighted sum of resolvent modes that are dynamically significant, non-empirical, and scalable with Reynolds number. To optimally represent direct numerical simulations (DNS) data at friction Reynolds number 2003, we determine the weights of resolvent modes as the solution of a convex optimization problem. Using only 12 modes per wall-parallel wavenumber pair and temporal frequency, we obtain close agreement with DNS-spectra, reducing the wall-normal and temporal resolutions used in the simulation by three orders of magnitude.

Moarref, R.; Jovanovi?, M. R.; Tropp, J. A.; Sharma, A. S.; McKeon, B. J.

2014-05-01

43

Block Coordinate Descent Method for Multi-Convex Optimization with Applications to Nonnegative Tensor Factorization and Completion.  

National Technical Information Service (NTIS)

This paper considers block multi-convex optimization, where the feasible set and objective function are generally non-convex but convex in each block of variables. We review some of its interesting examples and propose a generalized block coordinate desce...

W. Yin Y. Xu

2012-01-01

44

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

45

Convex Optimization of Coincidence Time Resolution for a High-Resolution PET System  

PubMed Central

We are developing a dual panel breast-dedicated positron emission tomography (PET) system using LSO scintillators coupled to position sensitive avalanche photodiodes (PSAPD). The charge output is amplified and read using NOVA RENA-3 ASICs. This paper shows that the coincidence timing resolution of the RENA-3 ASIC can be improved using certain list-mode calibrations. We treat the calibration problem as a convex optimization problem and use the RENA-3’s analog-based timing system to correct the measured data for time dispersion effects from correlated noise, PSAPD signal delays and varying signal amplitudes. The direct solution to the optimization problem involves a matrix inversion that grows order (n3) with the number of parameters. An iterative method using single-coordinate descent to approximate the inversion grows order (n). The inversion does not need to run to convergence, since any gains at high iteration number will be low compared to noise amplification. The system calibration method is demonstrated with measured pulser data as well as with two LSO-PSAPD detectors in electronic coincidence. After applying the algorithm, the 511 keV photopeak paired coincidence time resolution from the LSO-PSAPD detectors under study improved by 57%, from the raw value of 16.3 ± 0.07 ns full-width at half-maximum (FWHM) to 6.92 ± 0.02 ns FWHM (11.52 ± 0.05 ns to 4.89 ± 0.02 ns for unpaired photons).

Reynolds, Paul D.; Olcott, Peter D.; Pratx, Guillem; Lau, Frances W. Y.

2013-01-01

46

Convex optimization of coincidence time resolution for a high-resolution PET system.  

PubMed

We are developing a dual panel breast-dedicated positron emission tomography (PET) system using LSO scintillators coupled to position sensitive avalanche photodiodes (PSAPD). The charge output is amplified and read using NOVA RENA-3 ASICs. This paper shows that the coincidence timing resolution of the RENA-3 ASIC can be improved using certain list-mode calibrations. We treat the calibration problem as a convex optimization problem and use the RENA-3's analog-based timing system to correct the measured data for time dispersion effects from correlated noise, PSAPD signal delays and varying signal amplitudes. The direct solution to the optimization problem involves a matrix inversion that grows order (n(3)) with the number of parameters. An iterative method using single-coordinate descent to approximate the inversion grows order (n). The inversion does not need to run to convergence, since any gains at high iteration number will be low compared to noise amplification. The system calibration method is demonstrated with measured pulser data as well as with two LSO-PSAPD detectors in electronic coincidence. After applying the algorithm, the 511 keV photopeak paired coincidence time resolution from the LSO-PSAPD detectors under study improved by 57%, from the raw value of 16.3 ±0.07 ns full-width at half-maximum (FWHM) to 6.92 ±0.02 ns FWHM ( 11.52 ±0.05 ns to 4.89 ±0.02 ns for unpaired photons). PMID:20876008

Reynolds, Paul D; Olcott, Peter D; Pratx, Guillem; Lau, Frances W Y; Levin, Craig S

2011-02-01

47

Cutting-set methods for robust convex optimization with pessimizing oracles  

Microsoft Academic Search

We consider a general worst-case robust convex optimization problem, with arbitrary dependence on the uncertain parameters, which are assumed to lie in some given set of possible values. We describe a general method for solving such a problem, which alternates between optimization and worst-case analysis. With exact worst-case analysis, the method is shown to converge to a robust optimal point.

Almir Mutapcic; Stephen Boyd

2009-01-01

48

Compensation of modal dispersion in multimode fiber systems using adaptive optics via convex optimization  

NASA Astrophysics Data System (ADS)

Multimode fibers (MMF) are widely deployed in local-, campus-, and storage-area-networks. Achievable data rates and transmission distances are, however, limited by the phenomenon of modal dispersion. We propose a system to compensate for modal dispersion using adaptive optics. This leads to a 10- to 100-fold improvement in performance over current standards. We propose a provably optimal technique for minimizing inter-symbol interference (ISI) in MMF systems using adaptive optics via convex optimization. We use a spatial light modulator (SLM) to shape the spatial profile of light launched into an MMF. We derive an expression for the system impulse response in terms of the SLM reflectance and the field patterns of the MMF principal modes. Finding optimal SLM settings to minimize ISI, subject to physical constraints, is posed as an optimization problem. We observe that our problem can be cast as a second-order cone program, which is a convex optimization problem. Its global solution can, therefore, be found with minimal computational complexity. Simulations show that this technique opens up an eye pattern originally closed due to ISI. We then propose fast, low-complexity adaptive algorithms for optimizing the SLM settings. We show that some of these converge to the global optimum in the absence of noise. We also propose modified versions of these algorithms to improve resilience to noise and speed of convergence. Next, we experimentally compare the proposed adaptive algorithms in 50-mum graded-index (GRIN) MMFs using a liquid-crystal SLM. We show that continuous-phase sequential coordinate ascent (CPSCA) gives better bit-error-ratio performance than 2- or 4-phase sequential coordinate ascent, in concordance with simulations. We evaluate the bandwidth characteristics of CPSCA, and show that a single SLM is able to simultaneously compensate over up to 9 wavelength-division-multiplexed (WDM) 10-Gb/s channels, spaced by 50 GHz, over a total bandwidth of 450 GHz. We also show that CPSCA is able to compensate for modal dispersion over up to 2.2 km, even in the presence of mid-span connector offsets up to 4 mum (simulated in experiment by offset splices). A known non-adaptive launching technique using a fusion-spliced single-mode-to-multimode patchcord is shown to fail under these conditions. Finally, we demonstrate 10 x 10 Gb/s dense WDM transmission over 2.2 km of 50-mum GRIN MMF. We combine transmitter-based adaptive optics and receiver-based single-mode filtering, and control the launched field pattern for ten 10-Gb/s non-return-to-zero channels, wavelength-division multiplexed on a 200-GHz grid in the C band. We achieve error-free transmission through 2.2 km of 50-mum GRIN MMF for launch offsets up to 10 mum and for worst-case launched polarization. We employ a ten-channel transceiver based on parallel integration of electronics and photonics.

Panicker, Rahul Alex

49

A posteriori error estimates for mixed finite element solutions of convex optimal control problems  

Microsoft Academic Search

In this paper, we present an a posteriori error analysis for mixed finite element approximation of convex optimal control problems. We derive a posteriori error estimates for the coupled state and control approximations under some assumptions which hold in many applications. Such estimates can be used to construct reliable adaptive mixed finite elements for the control problems.

Yanping Chen; Wenbin Liu

2008-01-01

50

On the Relation Between Option and Stock Prices: A Convex Optimization Approach  

Microsoft Academic Search

The idea of investigating the relation of option and stock prices just based on the noarbitrageassumption, but without assuming any model for the underlying price dynamicshas a long history in the financial economics literature. We introduce convex, and in particularsemidefinite, optimization methods, duality and complexity theory to shed new lightto this relation. For the single stock problem, given moments of

Dimitris Bertsimas; Ioana Popescu

2002-01-01

51

Convex optimization and limit analysis: Application to Gurson and porous Drucker–Prager materials  

Microsoft Academic Search

First, we summarize our convex optimization method to solve the static approach of limit analysis. Then, we present the main features of a quadratic extension of a recently proposed mixed finite element method of the kinematic approach. Both methods are applied to obtain precise solutions to a forming problem with Gurson and Drucker–Prager materials. Finally, in order to analyze the

F. Pastor; Ph. Thoré; E. Loute; J. Pastor; M. Trillat

2008-01-01

52

Coordination and Control of Multiple Spacecraft using Convex Optimization Techniques  

Microsoft Academic Search

Formation flying of multiple spacecraft is an enabling technology for many future space science missions. These future missions will, for example, use the highly coordinated, distributed array of vehicles for earth mapping interferometers and synthetic aperture radar. This thesis presents coordination and control algorithms designed for a fleet of spacecraft. These algorithms are embedded in a hierarchical fleet archi- tecture

Jonathan P. How

2002-01-01

53

On the connectedness of the set of weakly efficient points of a vector optimization problem in locally convex spaces  

Microsoft Academic Search

In vector optimization, topological properties of the set of efficient and weakly efficient points are of interest. In this paper, we study the connectedness of the setEw of all weakly efficient points of a subsetZ of a locally convex spaceX with respect to a continuous mappingp:X ?Y,Y locally convex and partially ordered by a closed, convex cone with nonempty interior.

S. Helbig

1990-01-01

54

A primal-dual fixed point algorithm for convex separable minimization with applications to image restoration  

NASA Astrophysics Data System (ADS)

Recently, the minimization of a sum of two convex functions has received considerable interest in a variational image restoration model. In this paper, we propose a general algorithmic framework for solving a separable convex minimization problem from the point of view of fixed point algorithms based on proximity operators (Moreau 1962 C. R. Acad. Sci., Paris I 255 2897-99). Motivated by proximal forward-backward splitting proposed in Combettes and Wajs (2005 Multiscale Model. Simul. 4 1168-200) and fixed point algorithms based on the proximity operator (FP2O) for image denoising (Micchelli et al 2011 Inverse Problems 27 45009-38), we design a primal-dual fixed point algorithm based on the proximity operator (PDFP2O? for ? ? [0, 1)) and obtain a scheme with a closed-form solution for each iteration. Using the firmly nonexpansive properties of the proximity operator and with the help of a special norm over a product space, we achieve the convergence of the proposed PDFP2O? algorithm. Moreover, under some stronger assumptions, we can prove the global linear convergence of the proposed algorithm. We also give the connection of the proposed algorithm with other existing first-order methods. Finally, we illustrate the efficiency of PDFP2O? through some numerical examples on image supper-resolution, computerized tomographic reconstruction and parallel magnetic resonance imaging. Generally speaking, our method PDFP2O (? = 0) is comparable with other state-of-the-art methods in numerical performance, while it has some advantages on parameter selection in real applications.

Chen, Peijun; Huang, Jianguo; Zhang, Xiaoqun

2013-02-01

55

Structural optimization using evolutionary algorithms  

Microsoft Academic Search

The objective of this paper is to investigate the efficiency of various evolutionary algorithms (EA), such as genetic algorithms and evolution strategies, when applied to large-scale structural sizing optimization problems. Both type of algorithms imitate biological evolution in nature and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. In this

Nikolaos D. Lagaros; Manolis Papadrakakis; George Kokossalakis

2002-01-01

56

A difference of convex formulation of value-at-risk constrained optimization  

Microsoft Academic Search

In this article, we present a representation of value-at-risk (VaR) as a difference of convex (D.C.) functions in the case where the distribution of the underlying random variable is discrete and has finitely many atoms. The D.C. representation is used to study a financial risk-return portfolio selection problem with a VaR constraint. A branch-and-bound algorithm that numerically solves the problem

David Wozabal; Ronald Hochreiter; Georg Ch. Pflug

2010-01-01

57

Genetic algorithm with alphabet optimization  

Microsoft Academic Search

In recent years genetic algorithm (GA) was used successfully to solve many optimization problems. One of the most difficult questions of applying GA to a particular problem is that of coding. In this Paper a scheme is derived to optimize one aspect of the coding in an automatic fashion. This is done by using a high cardinality alphabet and optimizing

Gábor J. Tóth; Szabolcs Kovács; András Lörincz

1995-01-01

58

A posteriori error estimates for hp finite element solutions of convex optimal control problems  

Microsoft Academic Search

In this paper, we present a posteriori error analysis for hp finite element approximation of convex optimal control problems. We derive a new quasi-interpolation operator of Clément type and a new quasi-interpolation operator of Scott–Zhang type that preserves homogeneous boundary condition. The Scott–Zhang type quasi-interpolation is suitable for an application in bounding the errors in L2-norm. Then hp a posteriori

Yanping Chen; Yijie Lin

2011-01-01

59

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

60

A superresolution telescope that uses aberration effects suppression, deconvolution by dimensional reduction, optimal convexity and convexity normalization for image size and dark noise  

NASA Astrophysics Data System (ADS)

In this dissertation we claim to have found the solution to the problem of resolving beyond the diffraction limit (superresolution). This problem is solved by dimensional reduction, convexity optimization, and convexity normalization for image size and dark noise. By dimensional reduction we mean deconvolution on isophote ridges, which are one dimensional, thus we have reduced the dimensionality of the problem from two to one. By optimizing convexity we mean that we choose points to test for image sources for which the second derivative (convexity) of the intensity along isophote ridges is the highest. By normalization of convexity for dark noise and image size we are making sure that our optimization of convexity is not biased by dark noise at different exposures or different background convexities for images of different sizes. This biasing would create artifacts. We also invented ways to speed up our computation and overcome inverse matrix errors. For example we found a simple way to solve the illconditioned matrix problem so we could use the inverse matrix technique, and we are allowed here to replace explicit least squares with the more convenient minimum of the sum of amplitudes squared. We use methods to overcome astigmatism and spherical aberration which are not new. With a narrow field of view we don't need to use the usual iterative stochastic methods (such as MAP). This is because smoothing is effective here since the scale of the PSFs (point spread functions) is much larger than the noise scale. In this superresolution telescope we get a narrow field of view by a microscope-telescope combination. Pointing errors must be minimized to ensure that aberration effects are minimized, and astigmatism produced by air turbulence must be corrected for. Experiments have produced repeatable 1/10 Rayleigh distance resolution for SNR = 60 (with no prior knowledge of source configuration assumed). Through significant air turbulence over a 400 foot line of sight we get 1/6 Rayleigh resolution for 1.5 inch reflecting and refracting telescopes, about a factor of 12 better than you would expect.

Maker, David

61

EUROPT Workshop on Advances in Continuous Optimization (8th) Held in Aveiro, Portugal, on July 9-10, 2010.  

National Technical Information Service (NTIS)

Applications of Continuous Optimization to Combinatorial Problems; Complexity and Efficiency of Optimization Algorithms; Convex and Nonsmooth Optimization; Complementarity and Variational Problems; Derivative-free Optimization; Global Optimization; Linear...

D. Cardoso T. Tchemisova

2010-01-01

62

Experimental optimization by evolutionary algorithms  

Microsoft Academic Search

This tutorial addresses applications of evolutionary algorithms to optimization tasks where the function evaluation cannot be done through a computer simulation, but requires the execution of an experiment in the real world (i.e., cosmetics, detergents, wind tunnel experiments, taste experiments, to mention a few). The use of EAs for experimental optimization is placed in its historical context with an overview

Thomas Bäck; Joshua Knowles; Ofer M. Shir

2010-01-01

63

Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization  

NASA Technical Reports Server (NTRS)

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.

Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)

2002-01-01

64

Random search optimization based on genetic algorithm and discriminant function  

NASA Technical Reports Server (NTRS)

The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.

Kiciman, M. O.; Akgul, M.; Erarslanoglu, G.

1990-01-01

65

Another hybrid conjugate gradient algorithm for unconstrained optimization  

NASA Astrophysics Data System (ADS)

Another hybrid conjugate gradient algorithm is subject to analysis. The parameter ? k is computed as a convex combination of beta ^{{HS}}_{k} (Hestenes-Stiefel) and beta ^{{DY}}_{k} (Dai-Yuan) algorithms, i.eE beta ^{C}_{k} = {left( {1 - theta _{k} } right)}beta ^{{HS}}_{k} + theta _{k} beta ^{{DY}}_{k} . The parameter ? k in the convex combination is computed in such a way so that the direction corresponding to the conjugate gradient algorithm to be the Newton direction and the pair (s k , y k ) to satisfy the quasi-Newton equation nabla ^{2} f{left( {x_{{k + 1}} } right)}s_{k} = y_{k} , where s_{k} = x_{{k + 1}} - x_{k} and y_{k} = g_{{k + 1}} - g_{k} . The algorithm uses the standard Wolfe line search conditions. Numerical comparisons with conjugate gradient algorithms show that this hybrid computational scheme outperforms the Hestenes-Stiefel and the Dai-Yuan conjugate gradient algorithms as well as the hybrid conjugate gradient algorithms of Dai and Yuan. A set of 750 unconstrained optimization problems are used, some of them from the CUTE library.

Andrei, Neculai

2008-02-01

66

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

Microsoft Academic Search

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,

Kristian Bredies

2009-01-01

67

Constrained Multiobjective Biogeography Optimization Algorithm  

PubMed Central

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

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

2014-01-01

68

Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT.  

PubMed

One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications. PMID:20798458

Aleman, Dionne M; Glaser, Daniel; Romeijn, H Edwin; Dempsey, James F

2010-09-21

69

Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT  

NASA Astrophysics Data System (ADS)

One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications.

Aleman, Dionne M.; Glaser, Daniel; Romeijn, H. Edwin; Dempsey, James F.

2010-09-01

70

A Linear-Time Algorithm for Symmetric Convex Drawings of Internally Triconnected Plane Graphs  

Microsoft Academic Search

Symmetry is one of the most important aesthetic criteria in Graph Drawing which can reveal the hidden structure in the graph. Convex drawing is a straight-line drawing where every facial cycle is drawn as a convex polygon.\\u000a \\u000a \\u000a In this paper, we prove that given an internally triconnected plane graph with symmetries, there exists a convex drawing of the graph which

Seok-Hee Hong; Hiroshi Nagamochi

2010-01-01

71

Multilevel algorithms for nonlinear optimization  

NASA Technical Reports Server (NTRS)

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

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

1994-01-01

72

MEMS optimization incorporating genetic algorithms  

NASA Astrophysics Data System (ADS)

Micromechanical sensors are routinely simulated using finite element software. Once a structure has ben proposed, various parameters are optimized using experience, intuition, and trial-and-error. However, using proven finite element modeling coupled with a genetic algorithm (GA), optimal designs can be 'evolved' using a hands-free approach on a workstation. Once a problem is defined, the sole task required of the designer is the specification of a mathematical objective function expressing the desired properties of the sensor; the sensor geometry that maximizes the given function is then synthesized by the algorithm. We have developed an optimization tool and have applied it to the design of tuning fork gyroscopes (TFG). In this paper, we demonstrate how a TFG was optimized using GA's. TFG suspension beam lengths were adjusted through the robust search technique, which is resistant to trapping in local maxima. Desired vibration mode order and mode frequency separations were governed by the objective function as specified by the designer. This multidimensional nonlinear optimization problem had a solution space of over eight million possible designs. Industry-standard mechanical computer-aided engineering tools were integrate along with a GA toolbox and a web-based control interface. Designs offering reduced vibration sensitivity and increased sensor dynamic range have been produced. A tenfold decrease in total sensor optimization time has been documented, resulting in reduced development time.

Kirkos, Gregory A.; Jurgilewicz, Robert P.; Duncan, Stephen J.

1999-03-01

73

Firefly Algorithm, Lévy Flights and Global Optimization  

NASA Astrophysics Data System (ADS)

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

Yang, Xin-She

74

Genetic Algorithm for Optimization: Preprocessor and Algorithm  

NASA Technical Reports Server (NTRS)

Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.

Sen, S. K.; Shaykhian, Gholam A.

2006-01-01

75

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

76

Simulation of stochastic systems via polynomial chaos expansions and convex optimization  

NASA Astrophysics Data System (ADS)

Polynomial chaos expansions represent a powerful tool to simulate stochastic models of dynamical systems. Yet, deriving the expansion's coefficients for complex systems might require a significant and nontrivial manipulation of the model, or the computation of large numbers of simulation runs, rendering the approach too time consuming and impracticable for applications with more than a handful of random variables. We introduce a computationally tractable technique for computing the coefficients of polynomial chaos expansions. The approach exploits a regularization technique with a particular choice of weighting matrices, which allows to take into account the specific features of polynomial chaos expansions. The method, completely based on convex optimization, can be applied to problems with a large number of random variables and uses a modest number of Monte Carlo simulations, while avoiding model manipulations. Additional information on the stochastic process, when available, can be also incorporated in the approach by means of convex constraints. We show the effectiveness of the proposed technique in three applications in diverse fields, including the analysis of a nonlinear electric circuit, a chaotic model of organizational behavior, and finally a chemical oscillator.

Fagiano, Lorenzo; Khammash, Mustafa

2012-09-01

77

A discrete shuffled frog optimization algorithm  

Microsoft Academic Search

The shuffled frog leaping (SFL) optimization algorithm has been successful in solving a wide range of real-valued optimization\\u000a problems. In this paper we present a discrete version of this algorithm and compare its performance with a SFL algorithm,\\u000a a binary genetic algorithm (BGA), and a discrete particle swarm optimization (DPSO) algorithm on seven low dimensional and\\u000a five high dimensional benchmark

M. T. Vakil Baghmisheh; Katayoun Madani; Alireza Navarbaf

78

Linesearch Algorithm with Memory for Unconstrained Optimization.  

National Technical Information Service (NTIS)

This paper considers algorithms for unconstrained nonlinear optimization where the model used by the algorithm to represent the objective function explicitly includes memory of the past iterations. This is intended to make the algorithm less 'myopic' in t...

N. I. M. Gould S. Lucidi M. Roma P. L. Toint

1998-01-01

79

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

PubMed Central

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

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

2014-01-01

80

Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.  

PubMed

Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results. PMID:24832358

Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

2014-09-01

81

Coordination and control of distributed spacecraft systems using convex optimization techniques  

Microsoft Academic Search

SUMMARY Formation flying of multiple spacecraft is an enabling technology for many future space science missions. However, the co-ordination and control of these instruments poses many difficult design challenges. This paper presents fuel\\/time-optimal control algorithms for a co-ordination and control architecture that was designed for a fleet of spacecraft. This architecture includes low-level formation-keeping algorithms and a high-level fleet planner

Michael Tillerson; Gokhan Inalhan; Jonathan P. How

2002-01-01

82

Global optimization algorithms for a CAD workstation  

Microsoft Academic Search

This paper describes two new versions of the controlled random search procedure for global optimization (CRS). Designed primarily to suit the user of a CAD workstation, these algorithms can also be used effectively in other contexts. The first, known as CRS3, speeds the final convergence of the optimization by combining a local optimization algorithm with the global search procedure. The

W. L. Price

1987-01-01

83

Convex piecewise-linear fitting  

Microsoft Academic Search

We consider the problem of fitting a convex piecewise-linear function, with some specified form, to given multi-dimensional\\u000a data. Except for a few special cases, this problem is hard to solve exactly, so we focus on heuristic methods that find locally\\u000a optimal fits. The method we describe, which is a variation on the K-means algorithm for clustering, seems to work well

Alessandro Magnani; Stephen P. Boyd

2009-01-01

84

An algorithm for the MaxMin area triangulation of a convex polygon  

Microsoft Academic Search

Given a convex polygon in the plane, we are interested in triangulations of its interior, i.e. maximal sets of non- intersecting diagonals that subdivide the interior of the polygon into triangles. The MaxMin area triangula- tion is the triangulation of the polygon that maximizes the area of the smallest area triangle in the triangula- tion. There exists a dynamic programming

J. Mark Keil; Tzvetalin S. Vassilev

2003-01-01

85

Online estimation of lower and upper bounds for heart sound boundaries in chest sound using Convex-hull algorithm.  

PubMed

Heart sound localization in chest sound is an essential part for many heart sound cancellation algorithms. The main difficulty for heart sound localization methods is the precise determination of the onset and offset boundaries of the heart sound segment. This paper presents a novel method to estimate lower and upper bounds for the onset and offset of the heart sound segment, which can be used as anchor points for more precise estimation. For this purpose, first chest sound is divided into frames and then entropy and smoothed entropy features of these frames are extracted, and used in the Convex-hull algorithm to estimate the upper and lower bounds for heart sound boundaries. The Convex-hull algorithm constructs a special type of envelope function for entropy features and if the maximal difference between the envelope function and the entropy is larger than a certain threshold, this point is considered as a heart sound bound. The results of the proposed method are compared with a baseline method which is a modified version of a well-known heart sound localization method. The results show that the proposed method outperforms the baseline method in terms of accuracy and detection error rate. Also, the experimental results show that smoothing entropy features significantly improves the performance of both baseline and proposed methods. PMID:23366867

Ça?lar, F; Ozbek, I Y

2012-01-01

86

Large scale structural optimization: Computational methods and optimization algorithms  

Microsoft Academic Search

Summary  The objective of this paper is to investigate the efficiency of various optimization methods based on mathematical programming\\u000a and evolutionary algorithms for solving structural optimization problems under static and seismic loading conditions. Particular\\u000a emphasis is given on modified versions of the basic evolutionary algorithms aiming at improving the performance of the optimization\\u000a procedure. Modified versions of both genetic algorithms and

M. Papadrakakis; N. D. Lagaros; Y. Tsompanakis; V. Plevris

2001-01-01

87

Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Sets  

PubMed Central

Accurate segmentation of neonatal brain MR images remains challenging mainly due to their poor spatial resolution, inverted contrast between white matter and gray matter, and high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although active contour/surface models with geometric information constraint have been successfully applied to adult brain segmentation, they are not fully explored in the neonatal image segmentation. In this paper, we propose a novel neonatal image segmentation method by combining local intensity information, atlas spatial prior, and cortical thickness constraint in a single level-set framework. Besides, we also provide a robust and reliable tissue surface initialization for the proposed method by using a convex optimization technique. Thus, tissue segmentation, as well as inner and outer cortical surface reconstruction, can be obtained simultaneously. The proposed method has been tested on a large neonatal dataset, and the validation on 10 neonatal brain images (with manual segmentations) shows very promising results.

Wang, Li; Shi, Feng; Lin, Weili; Gilmore, John H.; Shen, Dinggang

2011-01-01

88

Simulated annealing algorithm for optimal capital growth  

NASA Astrophysics Data System (ADS)

We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.

Luo, Yong; Zhu, Bo; Tang, Yong

2014-08-01

89

Optimization of Multireservoir Systems by Genetic Algorithm  

Microsoft Academic Search

Application of optimization techniques for determining the optimal operating policy of reservoirs is a major issue in water\\u000a resources planning and management. As an optimization Genetic Algorithm, ruled by evolution techniques, have become popular\\u000a in diversified fields of science. The main aim of this study is to explore the efficiency and effectiveness of genetic algorithm\\u000a in optimization of multi-reservoirs. A

Onur H?nçal; A. Burcu Altan-Sakarya; A. Metin Ger

2011-01-01

90

Global search in the optimal control problem with a terminal objective functional represented as the difference of two convex functions  

NASA Astrophysics Data System (ADS)

A nonconvex optimal control problem is examined for a system that is linear with respect to state and has a terminal objective functional representable as the difference of two convex functions. A new local search method is proposed, and its convergence is proved. A strategy is also developed for the search of a globally optimal control process, because the Pontryagin and Bellman principles as applied to the above problem do not distinguish between the locally and globally optimal processes. The convergence of this strategy under appropriate conditions is proved.

Strekalovsky, A. S.; Yanulevich, M. V.

2008-07-01

91

Improved Real Quantum Evolutionary Algorithm for Optimum Economic Load Dispatch with Non-convex Loads  

Microsoft Academic Search

\\u000a An algorithm based on improved real quantum evolutionary algorithm (IRQEA) was developed to solve the problem of highly non-linear\\u000a economic load dispatch problem with valve point loading. The performance of the proposed algorithm is evaluated on a test\\u000a case of 15 units. The performance of the algorithm is compared with floating point genetic algorithm (FPGA) and real quantum\\u000a evolutionary algorithm

Nidul Sinha; Kaustabh Moni Hazarika; Shantanu Paul; Himanshu Shekhar; Amrita Amrita Karmakar

2010-01-01

92

Real-coded Bayesian Optimization Algorithm  

Microsoft Academic Search

This chapter describes a real-coded (i.e., continuous) Estimation of Distribution Algorithm (EDA) that solves real-valued\\u000a (i.e., numerical) optimization problems of bounded difficulty quickly, accurately, and reliably. This is the real-coded Bayesian Optimization Algorithm (rBOA). The objective is to bring the power of (discrete) BOA to bear upon the area of real-valued optimization. That is,\\u000a the rBOA must properly decompose a

Chang Wook Ahn; R. S. Ramakrishna; David E. Goldberg

93

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 algorithm 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 transformations.The algorithm has been implemented in the SUIF

Monica S. Lam; Michael E. Wolf

2004-01-01

94

Optimizing Fagin's TA algorithms (OFTA)  

Microsoft Academic Search

1. Fagin's top-k algorithms are designed for generic aggregation functions, the only requirement being that they are monotone; for specific functions, these algorithms may perform unnecessary operations. Our project will try to assess whether it is possible to tweak the algorithms for two specific aggregation functions (to be chosen from max, max1 + max2 and median) in such way that

Holger Bast; Ingmar Weber; Debapriyo Majumdar; Daniel Dumitriu; Silvana Solomon

95

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

96

An Algorithmic Framework for Multiobjective Optimization  

PubMed Central

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

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

2013-01-01

97

Stochastic optimization algorithms for barrier dividend strategies  

NASA Astrophysics Data System (ADS)

This work focuses on finding optimal barrier policy for an insurance risk model when the dividends are paid to the share holders according to a barrier strategy. A new approach based on stochastic optimization methods is developed. Compared with the existing results in the literature, more general surplus processes are considered. Precise models of the surplus need not be known; only noise-corrupted observations of the dividends are used. Using barrier-type strategies, a class of stochastic optimization algorithms are developed. Convergence of the algorithm is analyzed; rate of convergence is also provided. Numerical results are reported to demonstrate the performance of the algorithm.

Yin, G.; Song, Q. S.; Yang, H.

2009-01-01

98

Market equilibrium via a primal--dual algorithm for a convex program  

Microsoft Academic Search

We give the first polynomial time algorithm for exactly computing an equilibrium for the linear util- ities case of the market model defined by Fisher. Our algorithm uses the primal-dual paradigm in the enhancedsetting of KKT conditionsand convexprograms. We pinpointthe added difficultyraised by this setting and the manner in which our algorithm circumvents it.

Nikhil R. Devanur; Christos H. Papadimitriou; Amin Saberi; Vijay V. Vazirani

2008-01-01

99

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

SciTech Connect

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

Lyashenko, O.I.

1995-12-10

100

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

101

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

102

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 SUIF (Stanford

Monica S. Lam

1991-01-01

103

Optimizing connected component labeling algorithms  

Microsoft Academic Search

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

Kesheng Wu; Ekow Otoo; Arie Shoshani

2005-01-01

104

Algorithmic Differentiation for Calculus-based Optimization  

NASA Astrophysics Data System (ADS)

For numerous applications, the computation and provision of exact derivative information plays an important role for optimizing the considered system but quite often also for its simulation. This presentation introduces the technique of Algorithmic Differentiation (AD), a method to compute derivatives of arbitrary order within working precision. Quite often an additional structure exploitation is indispensable for a successful coupling of these derivatives with state-of-the-art optimization algorithms. The talk will discuss two important situations where the problem-inherent structure allows a calculus-based optimization. Examples from aerodynamics and nano optics illustrate these advanced optimization approaches.

Walther, Andrea

2010-10-01

105

Analysis of a Path Following Method for Nonsmooth Convex Programs  

Microsoft Academic Search

Recently Gilbert, Gonzaga and Karas (7) constructed examples of ill-behaved central paths for convex programs. In this paper we show that under mild conditions the central path has sucient smoothness to allow construction of a path-following interior point algorithm for non-dierentiable convex programs. We show that starting from a point near the cen- ter of the first set an †-optimal

Sanjay Mehrotra

106

Quantum Algorithm for Continuous Global Optimization.  

National Technical Information Service (NTIS)

We investigate the entwined roles of information and quantum algorithms in reducing the complexity of the global optimization problem (GOP). We show that: (i) a modest amount of additional information is sufficient to map the general continuous GOP into t...

V. Protopopescu

2001-01-01

107

Algorithms for optimizing hydropower system operation  

Microsoft Academic Search

Successive liner 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 rates, plus the estimated value of water remaining in storage at the end of the 12-month planning period. The

Jan C. Grygier; Jery R. Stedinger

1985-01-01

108

LQR-based optimal linear consensus algorithms  

Microsoft Academic Search

Laplacian matrices play an important role in linear consensus algorithms. This paper studies linear-quadratic regulator (LQR) based optimal linear consensus algorithms for multi-vehicle systems with single-integrator kinematics in a continuous-time setting. We propose two global cost functions, namely, interaction-free and interaction-related cost functions. With the interaction-free cost function, we derive the optimal (nonsymmetric) Laplacian matrix. It is shown that the

Yongcan Cao; Wei Ren

2009-01-01

109

Optimal Sorting Algorithms for Parallel Computers  

Microsoft Academic Search

The problem of sorting a sequence of n elements on a parallel computer with k processors is considered. The algorithms we present can all be run on a single instruction stream multiple data stream computer. For large n, each achieves an asymptotic speed-up ratio of k with respect to the best sequential algorithm, which is optimal in the number of

Gérard M. Baudet; David Stevenson

1978-01-01

110

An Improved Algorithm for Hydropower Optimization  

Microsoft Academic Search

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.

K. K. Reznicek; S. P. Simonovic

1990-01-01

111

Global Optimality of the Successive Maxbet Algorithm.  

ERIC Educational Resources Information Center

It is known that the Maxbet algorithm, which is an alternative to the method of generalized canonical correlation analysis and Procrustes analysis, may converge to local maxima. Discusses an eigenvalue criterion that is sufficient, but not necessary, for global optimality of the successive Maxbet algorithm. (SLD)

Hanafi, Mohamed; ten Berge, Jos M. F.

2003-01-01

112

An Algorithm for Optimal Lambda Calculus Reduction  

Microsoft Academic Search

We present an algorithm for lambda expression reduction that avoids any copying that could later cause duplication of work. It is optimal in the sense defined by Lévy. The basis of the algorithm is a graphical representation of the kinds of commonality that can arise from substitutions; the idea can be adapted to represent other kinds of expressions besides lambda

John Lamping

1990-01-01

113

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

114

A General Optimal Video Smoothing Algorithm  

Microsoft Academic Search

Video smoothing is a promising technique for reducing the bandwidth variability of video in order to improve net- work eficiency. This paper presents a general optimal video smoothing algorithm based on the concept of dynamic pro- gramming. The algorithm generates the optimum transmis- sion schedule for difierent requirements by setting the con- straints and the cost function accordingly. It can

Zhimei Jiang; Leonard Kleinrock

1998-01-01

115

Evolutionary Algorithm for Optimal Vaccination Scheme  

NASA Astrophysics Data System (ADS)

The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease.

Parousis-Orthodoxou, K. J.; Vlachos, D. S.

2014-03-01

116

Optimizing Graph Algorithms for Improved Cache Performance  

Microsoft Academic Search

We develop algorithmic optimizations to improve the cache performance of four fundamental graph algorithms. We present a cache-oblivious implementation of the Floyd-Warshall algorithm for the fundamental graph problem of all-pairs shortest paths by relaxing some dependencies in the iterative version. We show that this implementation achieves the lower bound on processor-memory traffic of ?(N3\\/?C), where N and C are the

Joon-sang Park; Michael Penner; Viktor K. Prasanna

2004-01-01

117

A Long-Step, Cutting Plane Algorithm for Linear and Convex Programming  

Microsoft Academic Search

A cutting plane method for linear programming is described. This method is an extension of Atkinson and Vaidya's algorithm, and uses the central trajectory. The logarithmic barrier function is used explicitly, motivated partly by the successful implementation of such algorithms. This makes it possible to maintain primal and dual iterates, thus allowing termination at will, instead of having to solve

John E. Mitchell; Srinivasan Ramaswamy

2000-01-01

118

Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems  

NASA Astrophysics Data System (ADS)

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

Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao

119

Exact and Approximate Sizes of Convex Datacubes  

NASA Astrophysics Data System (ADS)

In various approaches, data cubes are pre-computed in order to efficiently answer Olap queries. The notion of data cube has been explored in various ways: iceberg cubes, range cubes, differential cubes or emerging cubes. Previously, we have introduced the concept of convex cube which generalizes all the quoted variants of cubes. More precisely, the convex cube captures all the tuples satisfying a monotone and/or antimonotone constraint combination. This paper is dedicated to a study of the convex cube size. Actually, knowing the size of such a cube even before computing it has various advantages. First of all, free space can be saved for its storage and the data warehouse administration can be improved. However the main interest of this size knowledge is to choose at best the constraints to apply in order to get a workable result. For an aided calibrating of constraints, we propose a sound characterization, based on inclusion-exclusion principle, of the exact size of convex cube as long as an upper bound which can be very quickly yielded. Moreover we adapt the nearly optimal algorithm HyperLogLog in order to provide a very good approximation of the exact size of convex cubes. Our analytical results are confirmed by experiments: the approximated size of convex cubes is really close to their exact size and can be computed quasi immediately.

Nedjar, Sébastien

120

Batting order optimization by genetic algorithm  

Microsoft Academic Search

Baseball has been widely studied in various ways, including math and statistics. In a baseball game, an optimized batting order helps the team achieves greater number of runs in a season. This paper introduces a method that combines a genetic algorithm with a statistical simulation to identify a non-optimal batting order. The biggest issue is how we evaluate a batting

Sen Han

2012-01-01

121

Algorithm optimization in molecular dynamics simulation  

NASA Astrophysics Data System (ADS)

Establishing the neighbor list to efficiently calculate the inter-atomic forces consumes the majority of computation time in molecular dynamics (MD) simulation. Several algorithms have been proposed to improve the computation efficiency for short-range interaction in recent years, although an optimized numerical algorithm has not been provided. Based on a rigorous definition of Verlet radius with respect to temperature and list-updating interval in MD simulation, this paper has successfully developed an estimation formula of the computation time for each MD algorithm calculation so as to find an optimized performance for each algorithm. With the formula proposed here, the best algorithm can be chosen based on different total number of atoms, system average density and system average temperature for the MD simulation. It has been shown that the Verlet Cell-linked List (VCL) algorithm is better than other algorithms for a system with a large number of atoms. Furthermore, a generalized VCL algorithm optimized with a list-updating interval and cell-dividing number is analyzed and has been verified to reduce the computation time by 30˜60% in a MD simulation for a two-dimensional lattice system. Due to similarity, the analysis in this study can be extended to other many-particle systems.

Wang, Di-Bao; Hsiao, Fei-Bin; Chuang, Cheng-Hsin; Lee, Yung-Chun

2007-10-01

122

Co-clustering of image segments using convex optimization applied to EM neuronal reconstruction  

Microsoft Academic Search

This paper addresses the problem of jointly clustering two segmentations of closely correlated images. We focus in particular on the application of reconstructing neuronal structures in over-segmented electron microscopy images. We formulate the problem of co-clustering as a quadratic semi-assignment problem and investigate convex relaxations using semidefinite and linear programming. We further introduce a linear programming method with manageable number

Shiv Naga Prasad Vitaladevuni; Ronen Basri

2010-01-01

123

Fixed, low-order controller design with time response specifications using non-convex optimization.  

PubMed

In this paper, we present a new algorithm for designing a fixed, low-order controller with time response specifications for a linear time invariant (LTI), single input single output (SISO) plant. For a two-parameter feedback configuration, the problem of finding a fixed or low-order controller to meet the desired time response specification is reduced to the least square estimation (LSE) in the sense of partial model matching (PMM), which minimizes a quadratic cost function. The closed-loop stability condition imposed on the controller parameters is formulated by the polynomial matrix inequality (PMI) constraint associated with the cost function. When the cascade feedback structure is considered, the zeros of the controller may be a substantial obstacle when designing a controller that has a good time response. This problem can also be formulated using polynomial constraints. Consequently, it is shown that the total problem here can be formulated as an optimization problem with a quadratic objective function and several polynomial constraints in the controller parameter space. We show that the SeDuMi with YALMIP interface [Löfberg J. YALMIP: A toolbox for modeling and optimization in MATLAB, in: Proceedings of the IEEE symposium on computer aided control systems design 2004. p. 284-9. http://control.ee.ethz.ch/~joloef/yalmip.php] can be used for solving this problem. Finally, several illustrative examples are given. PMID:18606409

Jin, Lihua; Kim, Young Chol

2008-10-01

124

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

125

Optimal approximation algorithms for digital filter design  

NASA Astrophysics Data System (ADS)

Several new algorithms are presented for the optimal approximation and design of various classes of digital filters. An iterative algorithm is developed for the efficient design of unconstrained and constrained infinite impulse response (IIR) digital filters. Both in the unconstrained and constrained cases, the numerator and denominator of the filter transfer function are designed iteratively by recourse to the Remez algorithm and to appropriate design parameters and criteria, at each iteration. This makes it possible for the algorithm to be implemented by means of a short main program which uses (at each iteration) the linear phase FIR filter design algorithm of McClellan et al. as a subroutine. The approach taken also permits the filter to be designed with a desired ripple ratio. Also, the algorithm determines automatically the minimum passband ripple corresponding to the prescribed orders and band edges of the filter. The filter is designed directly without guessing the passband ripple or stopband ripple.

Liang, J. K.

126

Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm.  

National Technical Information Service (NTIS)

A hybrid robust multi-objective optimization algorithm and accompanying software were developed that: 1) utilize several evolutionary optimization algorithms, a set of rules for automatic switching among these algorithms in order to accelerate the overall...

G. S. Dulikravich

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

Enhanced Fuel-Optimal Trajectory-Generation Algorithm for Planetary Pinpoint Landing  

NASA Technical Reports Server (NTRS)

An enhanced algorithm is developed that builds on a previous innovation of fuel-optimal powered-descent guidance (PDG) for planetary pinpoint landing. The PDG problem is to compute constrained, fuel-optimal trajectories to land a craft at a prescribed target on a planetary surface, starting from a parachute cut-off point and using a throttleable descent engine. The previous innovation showed the minimal-fuel PDG problem can be posed as a convex optimization problem, in particular, as a Second-Order Cone Program, which can be solved to global optimality with deterministic convergence properties, and hence is a candidate for onboard implementation. To increase the speed and robustness of this convex PDG algorithm for possible onboard implementation, the following enhancements are incorporated: 1) Fast detection of infeasibility (i.e., control authority is not sufficient for soft-landing) for subsequent fault response. 2) The use of a piecewise-linear control parameterization, providing smooth solution trajectories and increasing computational efficiency. 3) An enhanced line-search algorithm for optimal time-of-flight, providing quicker convergence and bounding the number of path-planning iterations needed. 4) An additional constraint that analytically guarantees inter-sample satisfaction of glide-slope and non-sub-surface flight constraints, allowing larger discretizations and, hence, faster optimization. 5) Explicit incorporation of Mars rotation rate into the trajectory computation for improved targeting accuracy. These enhancements allow faster convergence to the fuel-optimal solution and, more importantly, remove the need for a "human-in-the-loop," as constraints will be satisfied over the entire path-planning interval independent of step-size (as opposed to just at the discrete time points) and infeasible initial conditions are immediately detected. Finally, while the PDG stage is typically only a few minutes, ignoring the rotation rate of Mars can introduce 10s of meters of error. By incorporating it, the enhanced PDG algorithm becomes capable of pinpoint targeting.

Acikmese, Behcet; Blackmore, James C.; Scharf, Daniel P.

2011-01-01

129

Genetic algorithm optimization of phononic bandgap structures  

Microsoft Academic Search

This paper describes the use of genetic algorithms (GAs) for the optimal design of phononic bandgaps in periodic elastic two-phase media. In particular, we link a GA with a computational finite element method for solving the acoustic wave equation, and find optimal designs for both metal–matrix composite systems consisting of Ti\\/SiC, and H2O-filled porous ceramic media, by maximizing the relative

George A. Gazonas; Daniel S. Weile; Raymond Wildman; Anuraag Mohan

2006-01-01

130

An optimal extraction algorithm for CCD spectroscopy  

Microsoft Academic Search

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

K. Horne

1986-01-01

131

Combinatorial Multiobjective Optimization Using Genetic Algorithms  

NASA Technical Reports Server (NTRS)

The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

Crossley, William A.; Martin. Eric T.

2002-01-01

132

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

133

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

134

The particle swarm optimization algorithm in size and shape optimization  

Microsoft Academic Search

.   Shape and size optimization problems instructural design are addressed using the particle swarm optimization algorithm (PSOA).\\u000a In our implementation of the PSOA, the social behaviour of birds is mimicked. Individual birds exchange information about\\u000a their position, velocity and fitness, and the behaviour of the flock is then influenced to increase the probability of migration\\u000a to regions of high fitness.

P. C. Fourie; A. A. Groenwold

2002-01-01

135

Affine Buildings and Tropical Convexity  

Microsoft Academic Search

The notion of convexity in tropical geometry is closely related to notions of convexity in the theory of affine buildings. We explore this relationship from a combinatorial and computational perspective. Our results include a convex hull algorithm for the Bruhat--Tits building of SL$_d(K)$ and techniques for computing with apartments and membranes. While the original inspiration was the work of Dress

Michael Joswig; Bernd Sturmfels; Josephine Yu

2007-01-01

136

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

137

A combination of concave/convex surfaces for field-enhancement optimization: the indented nanocone.  

PubMed

We introduce a design strategy to maximize the Near Field (NF) enhancement near plasmonic antennas. We start by identifying and studying the basic electromagnetic effects that contribute to the electric near field enhancement. Next, we show how the concatenation of a convex and a concave surface allows merging all the effects on a single, continuous nanoantenna. As an example of this NF maximization strategy, we engineer a nanostructure, the indented nanocone. This structure, combines all the studied NF maximization effects with a synergistic boost provided by a Fano-like interference effect activated by the presence of the concave surface. As a result, the antenna exhibits a NF amplitude enhancement of ~ 800, which transforms into ~1600 when coupled to a perfect metallic surface. This strong enhancement makes the proposed structure a robust candidate to be used in field enhancement based technologies. Further elaborations of the concept may produce even larger and more effective enhancements. PMID:23187337

García-Etxarri, Aitzol; Apell, Peter; Käll, Mikael; Aizpurua, Javier

2012-11-01

138

Automated Segmentation of CBCT Image using Spiral CT Atlases and Convex Optimization  

PubMed Central

Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.

Wang, Li; Chen, Ken Chung; Shi, Feng; Liao, Shu; Li, Gang; Gao, Yaozong; Shen, Steve GF; Yan, Jin; Lee, Philip K.M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang

2013-01-01

139

Automated segmentation of CBCT image using spiral CT atlases and convex optimization.  

PubMed

Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations. PMID:24505768

Wang, Li; Chen, Ken Chung; Shi, Feng; Liao, Shu; Li, Gang; Gao, Yaozong; Shen, Steve G F; Yan, Jin; Lee, Philip K M; Chow, Ben; Liu, Nancy X; Xia, James J; Shen, Dinggang

2013-01-01

140

Active queue management based on particle swarm optimization PID algorithm  

Microsoft Academic Search

This paper proposes an algorithm for improving adaptation and coefficients adjustment of active queue management by particle swarm optimization PID algorithm. In this algorithm, swarm particle optimization is combined with PID algorithm, which can settle the coefficients adjustment online in PID and can adapt the variation of network traffic, so this algorithm can effectively fulfill active queue management. The simulation

Tao Wei; Shun-yi Zhang

2008-01-01

141

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.  

PubMed

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. PMID:21441012

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

142

Tropical Convexity  

Microsoft Academic Search

The notions of convexity and convex polytopes are introduced in the setting of tropical geometry. Combinatorial types of tropical polytopes are shown to be in bijection with regular triangulations of products of two simplices. Applications to phylogenetic trees are discussed. Theorem 29 and Corollary 30 in the paper, relating tropical polytopes to injective hulls, are incorrect. See the erratum at

Mike Develin; Bernd Sturmfels

2003-01-01

143

A reliable algorithm for optimal control synthesis  

NASA Technical Reports Server (NTRS)

In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.

Vansteenwyk, Brett; Ly, Uy-Loi

1992-01-01

144

® andStability for Additively Regularized LS-SVMs via Convex Optimization  

Microsoft Academic Search

This paper considers the design of an algorithm that maximizes explicitly its own stability. The stability criterion - as often used for the construction of bounds on the gene ralization error of a learn- ing algorithm - is proposed to compensate for overfitting. The primal-dual formulation characterizing Least Squares Support Vector Machines (LS-SVMs) and the additive regularization framework (13) are

K. Pelckmans; J. A. K. Suykens; B. De Moor

145

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

146

Optimized dynamical decoupling via genetic algorithms  

NASA Astrophysics Data System (ADS)

We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.

Quiroz, Gregory; Lidar, Daniel A.

2013-11-01

147

Pattern Search Algorithms for Mixed Variable General Constrained Optimization Problems.  

National Technical Information Service (NTIS)

A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented The Audet-Dennis Generalized Pattern Search (GPS) algorithm for bound constrained mixed variable optimization problems is extended to problems ...

M. A. Abramson

2002-01-01

148

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

149

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

150

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

151

Multidisciplinary design optimization using genetic algorithms  

NASA Technical Reports Server (NTRS)

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. The strengths and limitations of GA for launch vehicle design optimization is studied.

Unal, Resit

1994-01-01

152

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

153

Variable Acceleration Coefficient-based Particle Swarm Optimization for Non-Convex Economic load dispatch problem  

Microsoft Academic Search

This paper presents Variable Acceleration Coefficient-based Particle Swarm Optimization (VACPSO) method to solve the economic load dispatch for minimizing fuel cost while considering prohibited operating zones and valve point effect. The proposed VACPSO is a modified version of the conventional Particle Swarm Optimization (PSO). Three modifications are suggested in the proposed VACPSO. First, the cognitive behavior of particle is influenced

Vinay Kumar Jadoun; K. R. Niazi; Anil Swarnkar; Nikhil Gupta

2011-01-01

154

Fast Genetic Algorithms Used for PID Parameter Optimization  

Microsoft Academic Search

PID parameter optimization is an important problem in control field. This paper presents a kind of fast genetic algorithms, which have a lot of improvements about population, selection, crossover and mutation in comparison with simple genetic algorithms. These fast genetic algorithms are used in PID parameter optimization for common objective model to remedy flaws of simple genetic algorithms and accelerate

Xiangzhong Meng; Baoye Song

2007-01-01

155

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

156

Shuffled Frog Leaping Algorithm Based Optimal Reactive Power Flow  

Microsoft Academic Search

Abstract-A new approach to ORPF (optimal reactive power flow) based on SFLA (shuffled frog leaping algorithm) is proposed. The algorithm approaches to solving ORPF problem are given. By applying the algorithm to dealing with IEEE 30-bus system, compared with the particle swarm optimization (PSO) algorithm and SGA(simple genetic algorithm),the experimental results show that the algorithm is indeed capable of obtaining

Qingzheng Li

2009-01-01

157

Optimal array configuration search using genetic algorithm  

NASA Astrophysics Data System (ADS)

Radio astronomy interferometric arrays traditionally use Earth rotation aperture image synthesis. Existing radio telescopes consist of dozens antennas separated at hundreds and thousands wavelengths, and these arrays are very sparse comparing to the common radar & communications phased arrays. New projects of superlarge radio telescopes, Square Kilometer Array (SKA), Low Frequency Array (LOFAR), Atacama Large Millimeter Array (ALMA) presume both Earth rotation and snapshot imaging. Optimizing an array configuration is an important stage of the array design. Due to the sparseness of the radio interferometers, the following cost functions might be chosen during optimization process: sidelobe minimization, or, in more specific way, the maximum sidelobe amplitude or the baseline histogram. Genetic algorithm is proposed in this paper for solving the optimization problem. It provides the global maximum of a cost function in a multimodal task and admits easy implementation of different constrains: desirable angular resolution (maximal antenna spacing), sensitivity to extended image features (minimal spacing), topography limitations, etc. Several examples of array configuration optimization using genetic algorithms are given in the paper.

Fridman, Peter A.

2001-12-01

158

Bell-Curve Based Evolutionary Optimization Algorithm  

NASA Technical Reports Server (NTRS)

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

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

1998-01-01

159

Optimal Approximation Algorithms for Digital Filter Design.  

NASA Astrophysics Data System (ADS)

Several new algorithms are presented for the optimal approximation and design of various classes of digital filters. An iterative algorithm is developed for the efficient design of unconstrained and constrained infinite impulse response (IIR) digital filters. Both in the unconstrained and constrained cases, the numerator and denominator of the filter transfer function are designed iteratively by recourse to the Remez algorithm and to appropriate design parameters and criteria, at each iteration. This makes it possible for the algorithm to be implemented by means of a short main program which uses (at each iteration) the linear phase FIR filter design algorithm of McClellan et al. as a subroutine. The approach taken also permits the filter to be designed with a desired ripple ratio. Also, the algorithm determines automatically the minimum passband ripple corresponding to the prescribed orders and band edges of the filter. The filter is designed directly without guessing the passband ripple or stopband ripple. Another algorithm, based on similar principles, is developed for the design of a nonlinear phase finite impulse response (FIR) filter, whose transfer function optimally approximates a desired magnitude response, there being no constraints imposed on the phase response. A similar algorithm is presented for the design of two new classes of FIR digital filters, one linear phase and the other nonlinear phase. A filter of either class has significantly reduced number of multiplications compared to the one obtained by its conventional counterpart, with respect to a given frequency response. In the case of linear phase, by introducing the new class of digital filters into the design of multistage decimators and interpolators for narrow-band filter implementation, it is found that an efficient narrow-band filter requiring considerably lower multiplication rate than the conventional linear phase FIR design can be obtained. The amount of data storage required by the new class of nonlinear phase FIR filters is significantly less than its linear phase counterpart. Finally, the design of a (finite-impulse-response) FIR digital filter with some of the coefficients constrained to zero is formulated as a linear programming (LP) problem and the LP technique is then used to design this class of constrained FIR digital filters. . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of author.) UMI.

Liang, Junn-Kuen

160

A Hybrid Harmony Search Algorithm for Numerical Optimization  

Microsoft Academic Search

In the paper a novel harmony search (HS) algorithm based on opposition and differential evolution (ODHS) algorithm is proposed in order to solve high dimensional optimization problems. It provides a new architecture of hybrid algorithms, which organically merges the differential evolution (DE) into HS algorithm and the ODHS algorithm initializes the HM (harmony memory) using opposition based learning and uses

Peng-Jun Zhao

2010-01-01

161

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

162

Unification of algorithms for minimum mode optimization  

NASA Astrophysics Data System (ADS)

Minimum mode following algorithms are widely used for saddle point searching in chemical and material systems. Common to these algorithms is a component to find the minimum curvature mode of the second derivative, or Hessian matrix. Several methods, including Lanczos, dimer, Rayleigh-Ritz minimization, shifted power iteration, and locally optimal block preconditioned conjugate gradient, have been proposed for this purpose. Each of these methods finds the lowest curvature mode iteratively without calculating the Hessian matrix, since the full matrix calculation is prohibitively expensive in the high dimensional spaces of interest. Here we unify these iterative methods in the same theoretical framework using the concept of the Krylov subspace. The Lanczos method finds the lowest eigenvalue in a Krylov subspace of increasing size, while the other methods search in a smaller subspace spanned by the set of previous search directions. We show that these smaller subspaces are contained within the Krylov space for which the Lanczos method explicitly finds the lowest curvature mode, and hence the theoretical efficiency of the minimum mode finding methods are bounded by the Lanczos method. Numerical tests demonstrate that the dimer method combined with second-order optimizers approaches but does not exceed the efficiency of the Lanczos method for minimum mode optimization.

Zeng, Yi; Xiao, Penghao; Henkelman, Graeme

2014-01-01

163

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

164

Dual Schroedinger Equation as Global Optimization Algorithm  

SciTech Connect

The dual Schroedinger equation is defined as replacing the imaginary number i by -1 in the original one. This paper shows that the dual equation shares the same stationary states as the original one. Different from the original one, it explicitly defines a dynamic process for a system to evolve from any state to lower energy states and eventually to the lowest one. Its power as a global optimization algorithm might be used by nature for constructing atoms and molecules. It shall be interesting to verify its existence in nature.

Huang Xiaofei [School of Information Science and Technology, Tsinghua University, Beijing, 100084 (China); eGain Communications, Mountain View, CA 94043 (United States); Huang Xiaowu [Department of Physics, Anhui University, Hefei, 230039 (China)

2011-03-28

165

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

166

Online Routing in Convex Subdivisions  

Microsoft Academic Search

We consider online routing algorithms for nding paths between the vertices of plane graphs. We show (1) there exists a routing algorithm for arbitrary triangulations that has no memory and uses no randomization, (2) no equivalent result is possible for convex subdivisions, (3) there is no competitive online routing algorithm under the Euclidean distance metric in arbitrary triangulations, and (4)

Prosenjit Bose; Pat Morin; Andrej Brodnik; Svante Carlsson; Erik D. Demaine; Rudolf Fleischer; J. Ian Munro; Alejandro López-ortiz

2000-01-01

167

Lunar Habitat Optimization Using Genetic Algorithms  

NASA Technical Reports Server (NTRS)

Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.

SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.

2007-01-01

168

Optimization Algorithm for Designing Diffractive Optical Elements  

NASA Astrophysics Data System (ADS)

Diffractive Optical Elements (DOEs) are commonly used in many applications such as laser beam shaping, recording of micro reliefs, wave front analysis, metrology and many others where they can replace single or multiple conventional optical elements (diffractive or refractive). One of the most versatile way to produce them, is to use computer assisted techniques for their design and optimization, as well as optical or electron beam micro-lithography techniques for the final fabrication. The fundamental figures of merit involved in the optimization of such devices are both the diffraction efficiency and the signal to noise ratio evaluated in the reconstructed wave front at the image plane. A design and optimization algorithm based on the error-reduction method (Gerchberg and Saxton) is proposed to obtain binary discrete phase-only Fresnel DOEs that will be used to produce specific intensity patterns. Some experimental results were obtained using a spatial light modulator acting as a binary programmable diffractive phase element. Although the DOEs optimized here are discrete in phase, they present an acceptable signal noise relation and diffraction efficiency.

Agudelo, Viviana A.; Orozco, Ricardo Amézquita

2008-04-01

169

Instrument design and optimization using genetic algorithms  

SciTech Connect

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

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

2006-10-15

170

Hybrid evolutionary algorithm and application to structural optimization  

Microsoft Academic Search

This paper introduces a new evolutionary algorithm with a globally stochastic but locally heuristic search strategy. It is implemented by incorporating a modified micro-genetic algorithm with two local optimization operators. Performance tests using two benchmarking functions demonstrate that the new algorithm has excellent convergence performance when applied to multimodal optimization problems. The number of objective function evaluations required to obtain

Z. Fawaz; Y. G. Xu; K. Behdinan

2005-01-01

171

An optimal force cueing algorithm for dynamic seat  

Microsoft Academic Search

The force cueing algorithm is a primary source of flight simulation fidelity for dynamic seat. This paper presents a new optimal force cueing algorithm which incorporates a mathematical model of human body pressure system and otolith system. Linear perceptual models of the pilot in both dynamic seat and aircraft are built, then the cueing algorithm is derived by optimizing the

Hua Shao; Liwen Guan; Jinsong Wang; Liping Wang; Yang Fu

2009-01-01

172

An Optimizing Algorithm for 3D Object Surface Triangulation  

Microsoft Academic Search

In this paper, we present an optimizing algorithm for 3D object surface triangulation. The objective of our algorithm is to minimize the total volume error while preserving and presenting as many of the topological features or details as possible. We adopted a resource allocation methodology to guide the search for a near-optimal solution. The algorithmic procedure starts from a 3D

Tianyu Lu; Javed I. Khan; David Y. Y. Yun

173

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

2011-11-01

174

Optimal design of powder compaction processes via genetic algorithm technique  

Microsoft Academic Search

In this paper, an optimal design is performed for powder die-pressing process based on the genetic algorithm approach. It includes the shape optimization of powder component, the optimal design of punch movements, and the friction optimization of powder–tool interface. The genetic algorithm is employed to perform an optimal design based on a fixed-length vector of design variables. The technique is

A. R. Khoei; Sh. Keshavarz; S. O. R. Biabanaki

2010-01-01

175

AGC parameters optimization using real coded genetic algorithm  

Microsoft Academic Search

A real coded genetic algorithm (RCGA) for parameter optimization of multiarea automatic generating control (AGC) has been proposed. Instead of using a traditional analysis algorithm to obtain the controller parameters, GA optimization technology is introduced and the MATLAB Simulink model is designed as an AGC parameter optimization tool to deal with the interconnection of the AGC loops. Utilizing GA's parallel

Li Pingkang; Du Xiuxia; Liu Yulin

2002-01-01

176

Optimal Design of Discrete Structure with Directed Mutation Genetic Algorithms  

Microsoft Academic Search

One of the most important practical considerations in the optimization of discrete structures is that the structural members are generally to be selected from available profiles list. Genetic algorithm shows certain advantages over other classical optimization procedures in structural optimization of discrete variables. In this paper we introduce the idea of directed mutation into the simple genetic algorithms field and

Na Li; Feng Ye

2006-01-01

177

Optimal reactive power dispatch based on harmony search algorithm  

Microsoft Academic Search

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

A. H. Khazali; M. Kalantar

2011-01-01

178

A compiler algorithm for optimizing locality in loop nests  

Microsoft Academic Search

This paper describes an algorithm to optimize cache localityin scientific codes on uniprocessor and multiprocessor machines.A distinctive characteristic of our algorithm is thatit considers loop and data layout transformations in a unifiedframework. We illustrate through examples that ourapproach is very effective at reducing cache misses and tilesizesensitivity of blocked loop nests; and can optimize nestsfor which optimization techniques based on

Mahmut T. Kandemir; J. Ramanujam; Alok N. Choudhary

1997-01-01

179

Cubes convexes  

Microsoft Academic Search

In various approaches, data cubes are pre-computed in order to answer efficiently OLAP queries. The notion of data cube has been declined in various ways: iceberg cubes, range cubes or differential cubes. In this paper, we introduce the concept of convex cube which captures all the tuples of a datacube satisfying a constraint combination. It can be represented in a

Sebastien Nedjar; Alain Casali; Rosine Cicchetti; Lotfi Lakhal

2010-01-01

180

Study on Water Pollution Diffusion by Artificial Immunity Optimization Algorithm  

Microsoft Academic Search

Artificial immune algorithm is a new bionic algorithms, it becomes a hot spot. Artificial immune algorithm has self-adjustment ability and adaptive capacity of the environment and can deal with complex optimization problems in parallel. Immune algorithm takes concentration and affinity as standards. Thus low concentration, high-fit individuals have more breeding opportunities. As attention to the diversity of individuals of solution

Li Yu; Jia-quan Wang

2010-01-01

181

A cross-layer optimization algorithm for wireless sensor network  

NASA Astrophysics Data System (ADS)

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

Wang, Yan; Liu, Le Qing

2010-07-01

182

On the optimality of the neighbor-joining algorithm  

Microsoft Academic Search

The popular neighbor-joining (NJ) algorithm used in phylogenetics is a greedy algorithm for nding the balanced minimum evolution (BME) tree associated to a dissimilarity map. From this point of view, NJ is \\\\optimal\\

Kord Eickmeyer; Peter Huggins; Lior Pachter; Ruriko Yoshida

2008-01-01

183

Optimal Distributed Routing Algorithms for Datagram Communication Networks.  

National Technical Information Service (NTIS)

We consider the problem of routing in a communication network with datagram service. Relationships among four optimality conditions are presented. Two distributed algorithms are derived. The main idea underlying the construction of these algorithms is the...

J. R. Yee

1989-01-01

184

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

185

Optimal rank algorithm for the detection of optical signals  

NASA Astrophysics Data System (ADS)

Relationships are obtained for determining the threshold value of optimal rank statistics for the detection of faint optical signals. The operating characteristics of a detector based on the optimal rank algorithm (ORA) are presented. It is shown that the efficiency of the ORA is significantly higher than that of the quasi-optimal (rank sum) algorithm and is close to that of the classical (photoelectron counter) algorithm.

Stepin, A. P.; Borisov, E. V.

1984-12-01

186

Optimizing two-pass connected-component labeling algorithms  

Microsoft Academic Search

Wepresent two optimization strategiestoimprove connected component labeling algorithms. Taking together, they form an efficient two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy re- duces the number of neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms usedto trackequivalentlabels.Weshow that thefirst strategyreducestheaveragenumber of neighbors accessed by a

Kesheng Wu; Ekow J. Otoo; Kenji Suzuki

2009-01-01

187

PDE Nozzle Optimization Using a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

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

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

2000-01-01

188

Cubes convexes  

Microsoft Academic Search

In various approaches, data cubes are pre-computed in order to answer\\u000aefficiently OLAP queries. The notion of data cube has been declined in various\\u000aways: iceberg cubes, range cubes or differential cubes. In this paper, we\\u000aintroduce the concept of convex cube which captures all the tuples of a\\u000adatacube satisfying a constraint combination. It can be represented in a

Sébastien Nedjar; Alain Casali; Rosine Cicchetti; Lotfi Lakhal

2006-01-01

189

Comparison among five evolutionary-based optimization algorithms  

Microsoft Academic Search

Abstract Evolutionary algorithms (EAs) are stochastic search methods,that mimic,the natural biological evolution and\\/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical,techniques may,fail. This paper compares,the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping.

Emad Elbeltagi; Tarek Hegazy; Donald E. Grierson

2005-01-01

190

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

191

Analysis of an Optimized MLOS Tomographic Reconstruction Algorithm and Comparison to the MART Reconstruction Algorithm  

NASA Astrophysics Data System (ADS)

An optimally designed MLOS tomographic reconstruction algorithm for use in 3D PIV and PTV applications is analyzed. Using a set of optimized reconstruction parameters, the reconstructions produced by the MLOS algorithm are shown to be comparable to reconstructions produced by the MART algorithm for a range of camera geometries, camera numbers, and particle seeding densities. The resultant velocity field error calculated using PIV and PTV algorithms is further minimized by applying both pre and post processing to the reconstructed data sets.

La Foy, Roderick; Vlachos, Pavlos

2011-11-01

192

Optimal and Sublogarithmic Time Randomized Parallel Sorting Algorithms  

Microsoft Academic Search

We assume a parallel RAM model which allows both concurrent reads and concurrent writes of a global memory. Our main result is an optimal randomized parallel algorithm for INTE- GER SORT (i.e., for sorting n integers in the range (1 ,n )). Our algorithm costs only logarithmic time and is the first known that is optimal: the product of its

Sanguthevar Rajasekaran; John H. Reif

1989-01-01

193

Optimized Monte Carlo Path Generation using Genetic Algorithms  

Microsoft Academic Search

In this technical report we present a new method for optimizing the generation of paths in Monte Carlo global illumination rendering algorithms. Ray tracing, particle tracing, and bidirectional ray tracing all use random walks to estimate various fluxes in the scene. The probability density functions neces- sary to generate these random walks are optimized using a genetic algorithm, such that

F. Suykens; Y. D. Willems

194

Reentry trajectory planning optimization based on ant colony algorithm  

Microsoft Academic Search

An optimal trajectory is very important to reusable launch vehicle (RLV) which faces the critical heating and aero force when it comes back from outer space through the dense atmosphere. However, the trajectory planning is a sort of typically large scale and multi-constraint optimization problem. Ant colony algorithm is a new class of population algorithm which has the potential to

Zhang Qingzhen; Liu Cunjia; Yang Bo; Ren Zhang

2007-01-01

195

The Routing Optimization Based on Improved Artificial Fish Swarm Algorithm  

Microsoft Academic Search

A novel algorithm named artificial fish swarm algorithm (AFSA) is discussed to solve the problem of routing optimization in computer communication networks. An improved AFSA (IAFSA) with the taboo table and a new parameter is proposed to increase the global optimum capability and the neighborhood search ability of AFSA. A mathematical model of routing optimization based on the minimal time

Xiaojuan Shan; Mingyan Jiang; Jingpeng Li

2006-01-01

196

A novel numerical optimization algorithm inspired from weed colonization  

Microsoft Academic Search

This paper introduces a novel numerical stochastic optimization algorithm inspired from colonizing weeds. Weeds are plants whose vigorous, invasive habits of growth pose a serious threat to desirable, cultivated plants making them a threat for agriculture. Weeds have shown to be very robust and adaptive to change in environment. Thus, capturing their properties would lead to a powerful optimization algorithm.

Ali Reza Mehrabian; Caro Lucas

2006-01-01

197

An optimization approach to estimating stability regions using genetic algorithms  

Microsoft Academic Search

The problem of estimating regions of asymptotic stability for nonlinear dynamic systems is considered as an optimization problem. Genetic algorithms are then proposed to solve the resulting optimization problems. Three test systems are used to evaluate the performance of the proposed genetic algorithms. The test systems are 6th, 8th, and 17th order nonlinear power electronics systems. The performance of the

Benjamin P. Loop; Scott D. Sudhoff; S. H. Zak; Edwin L. Zivi

2005-01-01

198

Optimized neural net control using genetic algorithm for intermittent system  

Microsoft Academic Search

A PID-like Neural Net control algorithm is developed, and Genetic Algorithm is integrated into for optimizing the learning rates. The dynamic properties for an intermittent Heating Ventilation Air Conditioning (HVAC) system are analyzed, and MATLAB\\/Simulink model for Neural Net control algorithm is introduced. The simulation for large delay systems and the industrial intermittent HVAC system application is reported.

Li Pingkang; Du Xiuxia; Gan Xuejun

2002-01-01

199

Genetic Simulated Annealing Algorithm Used for PID Parameters Optimization  

Microsoft Academic Search

A type of genetic simulated annealing algorithms (GSAAs) is presented, which is used to optimize the parameters of proportional-integral-derivative (PID) controllers. This approach combines the merits of genetic algorithms (GAs) and simulated annealing algorithms (SAAs). By integrating the global search ability of GA with the local search ability of SAA, the search ability of GSAA is much stronger than GA's

Jiajia Wang; Guoqing Jin; Yaqun Wang; Xiaozhu Chen

2009-01-01

200

On the optimality of the neighbor-joining algorithm  

Microsoft Academic Search

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

Kord Eickmeyer; Peter Huggins; Lior Pachter; Ruriko Yoshida

2007-01-01

201

Optimal Motion Cueing Algorithm Using the Human Body Model  

Microsoft Academic Search

This paper introduces the development of a motion cueing algorithm which produces motion cues from vehicle motion signals. The simulator, a parallel manipulator, has the limitation on the workspace. In order to overcome this problem, the motion cueing algorithm is needed. Here, we suggest a new optimal motion cueing algorithm where the human body model (ISO 2631-1) as well as

Myung-Chul Han; Hyung-Sang Lee; Suk Lee; Man Hyung Lee

2002-01-01

202

Recurrent neural networks training with optimal bounded ellipsoid algorithm  

Microsoft Academic Search

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied traing the weights of recurrent neural

José de Jesús Rubio; Wen Yu

2007-01-01

203

Neural network training with optimal bounded ellipsoid algorithm  

Microsoft Academic Search

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter train- ing, the noise is not required to be Guassian. In this paper OBE algorithm is applied in training the weights of

José De Jesús Rubio; Wen Yu; Andrés Ferreyra

2009-01-01

204

A NEW HYBRID EVOLUTIONARY OPTIMIZATION ALGORITHM FOR DISTRIBUTION FEEDER RECONFIGURATION  

Microsoft Academic Search

This article extends a hybrid evolutionary algorithm to cope with the feeder reconfiguration problem in distribution networks. The proposed method combines the Self-Adaptive Modified Particle Swarm Optimization (SAMPSO) with Modified Shuffled Frog Leaping Algorithm (MSFLA) to proceed toward the global solution. As with other population-based algorithms, PSO has parameters which should be tuned to have a suitable performance. Thus, a

Taher Niknam; Mohsen Zare; Jamshid Aghaei; Ehsan Azad Farsani

2011-01-01

205

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 ...

206

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization  

NASA Technical Reports Server (NTRS)

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

Holst, Terry L.

2004-01-01

207

A new algorithm for L2 optimal model reduction  

NASA Technical Reports Server (NTRS)

In this paper the quadratically optimal model reduction problem for single-input, single-output systems is considered. The reduced order model is determined by minimizing the integral of the magnitude-squared of the transfer function error. It is shown that the numerator coefficients of the optimal approximant satisfy a weighted least squares problem and, on this basis, a two-step iterative algorithm is developed combining a least squares solver with a gradient minimizer. Convergence of the proposed algorithm to stationary values of the quadratic cost function is proved. The formulation is extended to handle the frequency-weighted optimal model reduction problem. Three examples demonstrate the optimization algorithm.

Spanos, J. T.; Milman, M. H.; Mingori, D. L.

1992-01-01

208

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

209

Transonic Wing Shape Optimization Using a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

2002-01-01

210

A work-optimal CGM algorithm for the LIS problem  

Microsoft Academic Search

This paper presents a work-optimal CGM algorithm that solves the Longest Increasing Subsequence Problem. It can be implemented in the CGM with P processors in O(N2 ÷P) time and O(P) communication steps. It is the first CGM algorithm for this problem and it is work-optimal since the sequential algorithm has a complexity of O(N2).

Thierry Garcia; Myoupo Jean-Frédéric; David Semé

2001-01-01

211

Optimal Randomized EREW PRAM Algorithms for Finding Spanning Forests  

Microsoft Academic Search

We present the first randomized O(logn) time and O(m+n) work EREW PRAM algorithm for finding a spanning forest of an undirected graph G=(V,E) with n vertices and m edges. Our algorithm is optimal with respect to time, work, and space. As a consequence we get optimal randomized EREW PRAM algorithms for other basic connectivity problems such as finding a bipartite

Shay Halperin; Uri Zwick

2001-01-01

212

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

PubMed

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

Celik, Yuksel; Ulker, Erkan

2013-01-01

213

Combining genetic algorithms with BESO for topology optimization  

Microsoft Academic Search

This paper proposes a new algorithm for topology optimization by combining the features of genetic algorithms (GAs) and bi-directional\\u000a evolutionary structural optimization (BESO). An efficient treatment of individuals and population for finite element models\\u000a is presented which is different from traditional GAs application in structural design. GAs operators of crossover and mutation\\u000a suitable for topology optimization problems are developed. The

Z. H. Zuo; Y. M. Xie; X. Huang

2009-01-01

214

Optimizing High Speed Flip-Flop Using Genetic Algorithm  

Microsoft Academic Search

In this paper, the optimization of power-delay-product (PDP) of a high-speed flip-flop via transistor sizing is presented. The optimization is performed using the genetic algorithm (GA). The flip-flop which is used in this optimization is called modified hybrid latch flip-flop (MHLFF). The genetic algorithm is implemented in MATLAB with the fitness function expressed in terms of the power and the

Fatemeh Aezinia; Ali Afzali-kusha; Caro Lucas

2006-01-01

215

Genetic-Algorithm Tool For Search And Optimization  

NASA Technical Reports Server (NTRS)

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

Wang, Lui; Bayer, Steven

1995-01-01

216

Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization  

PubMed Central

Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm’s global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms—a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units.

Schutte, Jaco F.; Koh, Byung; Reinbolt, Jeffrey A.; Haftka, Raphael T.; George, Alan D.; Fregly, Benjamin J.

2006-01-01

217

A Two-Level Genetic Algorithm for Electromagnetic Optimization  

Microsoft Academic Search

Optimizing complex engineering problems may demand large computational efforts because of the use of numerical models. Global optimization can be established through the use of evolutionary algorithms, but may demand a prohibitive amount of computational time. In order to reduce the computational time, we incorporate in the global optimization procedures a physics-based fast coarse model. This paper presents a two-level

Guillaume Crevecoeur; Peter Sergeant; Luc Dupre; Rik Van de Walle

2010-01-01

218

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

219

Optimal management of MicroGrid using Bacterial Foraging Algorithm  

Microsoft Academic Search

This paper focused on optimal operating strategy and cost optimization scheme for a MicroGrid by using Bacterial Foraging Algorithm. Prior to the optimization of the microgrid itself, the system model components from some real manufactural data are constructed. The proposed cost function takes into consideration the costs of the emissions NOx, SO2, and CO2 as well as the operation and

R. Noroozian; H. Vahedi

2010-01-01

220

Section optimization design of discrete structure with improved genetic algorithms  

Microsoft Academic Search

The structural members are generally to be selected from available profiles list is most important practical considerations in the optimization of discrete structures. Genetic algorithms show certain advantages over other classical optimization procedures in structural optimization of discrete variables. In order to overcoming the shortcoming of simple GA, we introduce the idea of directed mutation and present an active evolution

Li Na; Hou Huiying

2008-01-01

221

A Parallel Particle Swarm Optimization Algorithm with Communication Strategies  

Microsoft Academic Search

Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy

Jui-fang Chang; Shu-chuan Chu; John F. Roddick; Jeng-shyang Pan

2005-01-01

222

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization  

NASA Technical Reports Server (NTRS)

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

Holst, Terry L.

2005-01-01

223

A danger-theory-based immune network optimization algorithm.  

PubMed

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. PMID:23483853

Zhang, Ruirui; Li, Tao; Xiao, Xin; Shi, Yuanquan

2013-01-01

224

Analysis, estimation and controller design of parameter-dependent systems using convex optimization based on linear matrix inequalities  

NASA Astrophysics Data System (ADS)

In this thesis, we outline three contributions in robust control. The first is the efficient computation of a lower bound on the robust stability margin (RSM) of uncertain systems. A lower bound on the RSM can be derived using the framework of integral quadratic constraints (IQCs). Current techniques for numerically computing this lower bound use a bisection scheme. We show how this bisection can be avoided altogether by reformulating the lower bound computation problem as a single generalized eigenvalue minimization problem, which can be solved very efficiently using standard algorithms. For the second contribution, we focus on linear systems affected by parametric uncertainties. For these systems, we present sufficient conditions for robust stability. We also derive conditions for the existence of a robustly stabilizing gain-scheduled controller when the system has time-varying parametric uncertainties that can be measured in real time. Our approach is proven to be in general less conservative than existing methods. Our third contribution is on the robust estimation of systems having parametric uncertainties. For systems with mixed deterministic and stochastic uncertainties, we design two optimized steady state filters: (i) the first filter minimizes an upper bound on the worst-case gain in the mean energy between the noise affecting the system and the estimation error; (ii) the second filter minimizes an upper bound on the worst-case asymptotic mean square estimation error when the plant is driven by a white noise process. For time-varying systems with stochastic uncertainties, we derive a robust adaptive Kalman filtering algorithm. This algorithm offers considerable improvement in performance when compared to the standard Kalman filtering techniques. We demonstrate the performance of these robust filters on numerical examples consisting of the design of equalizers for communication channels.

Wang, Fan

225

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

226

A parallel Jacobson-Oksman optimization algorithm. [parallel processing (computers)  

NASA Technical Reports Server (NTRS)

A gradient-dependent optimization technique which exploits the vector-streaming or parallel-computing capabilities of some modern computers is presented. The algorithm, derived by assuming that the function to be minimized is homogeneous, is a modification of the Jacobson-Oksman serial minimization method. In addition to describing the algorithm, conditions insuring the convergence of the iterates of the algorithm and the results of numerical experiments on a group of sample test functions are presented. The results of these experiments indicate that this algorithm will solve optimization problems in less computing time than conventional serial methods on machines having vector-streaming or parallel-computing capabilities.

Straeter, T. A.; Markos, A. T.

1975-01-01

227

A majorization-minimization algorithm for (multiple) hyperparameter learning  

Microsoft Academic Search

We present a general Bayesian framework for hyperparameter tuning in L2-regularized super- vised learning models. Paradoxically, our al- gorithm works by first analytically integrating out the hyperparameters from the model. We find a local optimum of the resulting non- convex optimization problem efficiently using a majorization-minimization (MM) algorithm, in which the non-convex problem is reduced to a series of convex

Chuan-sheng Foo; Chuong B. Do; Andrew Y. Ng

2009-01-01

228

Convex hull realizations of the multiplihedra  

Microsoft Academic Search

We present a simple algorithm for determining the extremal points in Euclidean space whose convex hull is the nth polytope in the sequence known as the multiplihedra. This answers the open question of whether the multiplihedra could be realized as convex polytopes. We use this realization to unite the approach to An-maps of Iwase and Mimura to that of Boardman

Stefan Forcey

2008-01-01

229

Convex Drawings of Hierarchical Plane Graphs  

Microsoft Academic Search

This paper proves that every internally triconnected hierarchical plane graph with the outer facial cycle drawn as a convex polygon admits a convex drawing. We present an algorithm which constructs such a drawing. This extends the previous known result that every hierarchical plane graph admits a straight-line drawing.

Seok-Hee Hong; Hiroshi Nagamochi

230

Genetic algorithms - What fitness scaling is optimal?  

NASA Technical Reports Server (NTRS)

A problem of choosing the best scaling function as a mathematical optimization problem is formulated and solved under different optimality criteria. A list of functions which are optimal under different criteria is presented which includes both the best functions empirically proved and new functions that may be worth trying.

Kreinovich, Vladik; Quintana, Chris; Fuentes, Olac

1993-01-01

231

A Decomposition-based Multi-objective Particle Swarm Optimization Algorithm for Continuous Optimization Problems  

Microsoft Academic Search

Particle swarm optimization (PSO) is a heuristic optimization technique that uses previous personal best experience and global best experience to search global optimal solutions. This paper studies the application of PSO techniques to multi-objective optimization using decomposition methods. A new decomposition-based multi-objective PSO algorithm is proposed, called MOPSO\\/D. It integrates PSO into a multiobjective evolutionary algorithm based on decomposition (MOEA\\/D).

Wei Peng; Qingfu Zhang

2008-01-01

232

An alternating split Bregman algorithm for multi-region segmentation  

Microsoft Academic Search

Multi-region image segmentation aims at partitioning an image into several “meaningful” regions. The associated optimization problem is non-convex and generally difficult to solve. Finding the global optimum, or good approximations of it, hence is a problem of first interest in computer vision. We propose an alternating split Bregman algorithm for a large class of convex relaxations of the continuous Potts

Gregory Paul; Janick Cardinale; Ivo F. Sbalzarini

2011-01-01

233

Optimal Motion Cueing Algorithm Using the Human Body Model  

NASA Astrophysics Data System (ADS)

This paper introduces the development of a motion cueing algorithm which produces motion cues from vehicle motion signals. The simulator, a parallel manipulator, has the limitation on the workspace. In order to overcome this problem, the motion cueing algorithm is needed. Here, we suggest a new optimal motion cueing algorithm where the human body model (ISO 2631-1) as well as the human perception model are incorporated. And we show the performance of this algorithm using the computer simulation. In simulation, we produce motion cues from vehicle motion signals through the proposed algorithm and show sensation errors which the human feel between vehicle signals and the simulator motions subjected to motion cues.

Han, Myung-Chul; Lee, Hyung-Sang; Lee, Suk; Lee, Man Hyung

234

PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm.  

PubMed

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

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

2014-01-01

235

PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm  

PubMed Central

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

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

2014-01-01

236

Genome rearrangements: a correct algorithm for optimal capping  

Microsoft Academic Search

In this Letter we propose two results in application to rearrangements of multichromosomal genomes: first, a literature synthesis providing a classification for breakpoint graph components; and second, a new algorithm for optimal capping with a proof of its correction.

Géraldine Jean; Macha Nikolski

2007-01-01

237

Standard Harmony Search Algorithm for Structural Design Optimization  

Microsoft Academic Search

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

Kang Seok Lee

238

Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos  

Microsoft Academic Search

Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity\\u000a and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence,\\u000a slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm\\u000a (CGA)

Chun-Tian Cheng; Wen-Chuan Wang; Dong-Mei Xu; K. W. Chau

2008-01-01

239

Optimal Linear-Consensus Algorithms: An LQR Perspective  

Microsoft Academic Search

Laplacian matrices play an important role in linear-consensus algorithms. This paper studies optimal linear-consensus algorithms for multivehicle systems with single-integrator dynamics in both continuous-time and discrete-time settings. We propose two global cost functions, namely, interaction-free and interaction-related cost functions. With the interaction-free cost function, we derive the optimal (nonsymmetric) Laplacian matrix by using a linear-quadratic-regulator-based method in both continuous-time and

Yongcan Cao; Wei Ren

2010-01-01

240

Optimal Concurrent Dimensional and Geometrical Tolerancing based on Evolutionary Algorithms  

Microsoft Academic Search

A general new methodology using evolutionary algorithm viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for obtaining optimal tolerance allocation and alternative process selection for mechanical assembly is presented. The problem has a multi-criterion character in which 3 objective functions, 6 constraints and 11 variables are considered. The average fitness membership function method is

K. Sivakumar; C. Balamurugan; S. Ramabalan; S. B. Venkata Raman

2009-01-01

241

Progress in design optimization using evolutionary algorithms for aerodynamic problems  

NASA Astrophysics Data System (ADS)

Evolutionary algorithms (EAs) are useful tools in design optimization. Due to their simplicity, ease of use, and suitability for multi-objective design optimization problems, EAs have been applied to design optimization problems from various areas. In this paper we review the recent progress in design optimization using evolutionary algorithms to solve real-world aerodynamic problems. Examples are given in the design of turbo pump, compressor, and micro-air vehicles. The paper covers the following topics that are deemed important to solve a large optimization problem from a practical viewpoint: (1) hybridized approaches to speed up the convergence rate of EAs; (2) the use of surrogate model to reduce the computational cost stemmed from EAs; (3) reliability based design optimization using EAs; and (4) data mining of Pareto-optimal solutions.

Lian, Yongsheng; Oyama, Akira; Liou, Meng-Sing

2010-07-01

242

A parallel variable metric optimization algorithm  

NASA Technical Reports Server (NTRS)

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

Straeter, T. A.

1973-01-01

243

Optimization of an offset reflector antenna using genetic algorithms  

Microsoft Academic Search

This paper presents the application of genetic algorithms in the optimization of an offset reflector antenna. The antenna shape is designed in order to obtain a uniform radiation pattern on the Brazilian territory. Modified genetic operators are proposed with the aim to increase the efficiency of the real coded genetic algorithms used here.

S. L. Avila; J. A. Vasconcelos

2004-01-01

244

On Timonov's algorithm for global optimization of univariate Lipschitz functions  

Microsoft Academic Search

Timonov proposes an algorithm for global maximization of univariate Lipschitz functions in which successive evaluation points are chosen in order to ensure at each iteration a maximal expected reduction of the “region of indeterminacy”, which contains all globally optimal points. It is shown that such an algorithm does not necessarily converge to a global optimum.

Pierre Hansen; Brigitte Jaumard; Shi-Hui Lu

1991-01-01

245

Parallel optimization algorithms and their implementation in VLSI design  

NASA Technical Reports Server (NTRS)

Two new parallel optimization algorithms based on the simplex method are described. They may be executed by a SIMD parallel processor architecture and be implemented in VLSI design. Several VLSI design implementations are introduced. An application example is reported to demonstrate that the algorithms are effective.

Lee, G.; Feeley, J. J.

1991-01-01

246

A turbo codes optimization method using particle swarm algorithm  

Microsoft Academic Search

Turbo Codes present a new direction for the channel encoding, especially since they were adopted for multiple norms of telecommunications, such as deeper communication, etc. To obtain an excellent performance, it is necessary to design robust turbo code interleaver and decoding algorithms. In this paper, we are investigating particle swarm algorithm as a promising optimization method to find good interleaver

An Jing; Qi Kang; Lei Wang; Qidi Wu

2008-01-01

247

Optimal Algorithm for Shape from Shading and Path Planning  

Microsoft Academic Search

. An optimal algorithm for the reconstruction of a surface from its shadingimage is presented. The algorithm solves the 3D reconstruction from a single shadingimage problem. The shading image is treated as a penalty function and the heightof the reconstructed surface is a weighted distance. A consistent numerical schemebased on Sethian's fast marching method is used to compute the reconstructedsurface.

Ron Kimmel; James A. Sethian

2001-01-01

248

An alternative differential evolution algorithm for global optimization  

Microsoft Academic Search

The purpose of this paper is to present a new and an alternative differential evolution (ADE) algorithm for solving unconstrained global optimization problems. In the new algorithm, a new directed mutation rule is introduced based on the weighted difference vector between the best and the worst individuals of a particular generation. The mutation rule is combined with the basic mutation

Ali W. Mohamed; Hegazy Z. Sabry; Motaz Khorshid

249

Optimal automatic hardware synthesis for signal processing algorithms  

Microsoft Academic Search

This paper presents a complete methodology for the automatic synthesis of VLSI architectures used in digital signal processing. Most signal processing algorithms have the form of an n-dimensional nested loop with unit uniform loop carried dependencies. We model such algorithms with generalized UET grids. We calculate the optimal makespan for the generalized UET grids and then we establish the minimum

Nectarios Koziris; George Economakos; Theodore Andronikos; George Papakonstantinou; Panayotis Tsanakas

1997-01-01

250

An SFL-Based Multicast Routing Optimization Algorithm  

Microsoft Academic Search

The problem of computing the bandwidth-delay-constrained least-cost QoS multicast routing is a NP-complete problem. A novel shuffled frog leaping (SFL) algorithm is proposed to deal with the Qos multicast routing problem effectively and efficiently in this paper. The experimental results show that the proposed algorithm can find optimal solution quickly and has a good scalability.

Xia Sun; Ziqiang Wang

2009-01-01

251

Genetic algorithm optimization applied to electromagnetics: a review  

Microsoft Academic Search

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

Daniel S. Weile; Eric Michielssen

1997-01-01

252

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

253

Designing Stochastic Optimization Algorithms for Real-world Applications  

NASA Astrophysics Data System (ADS)

This article presents a review of recent advances in stochastic optimization algorithms. Novel algorithms achieving highly adaptive and efficient searches, theoretical analyses to deepen our understanding of search behavior, successful implementation on parallel computers, attempts to build benchmark suites for industrial use, and techniques applied to real-world problems are included. A list of resources is provided.

Someya, Hiroshi; Handa, Hisashi; Koakutsu, Seiichi

254

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

255

Neural Networks Training with Optimal Bounded Ellipsoid Algorithm  

Microsoft Academic Search

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some\\u000a better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages\\u000a over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied traing the weights\\u000a of recurrent neural

José De Jesús Rubio; Wen Yu

2007-01-01

256

An optimal parallel algorithm for graph planarity  

Microsoft Academic Search

The authors present a parallel algorithm based on open ear decomposition which, given a graph G on n vertices, constructs an embedding of G onto the plane or reports that G is nonplanar. This parallel algorithm runs on a concurrent-read, concurrent-write parallel random-access machine (CRCW PRAM) in O(log n) time with the same processor bound as graph connectivity

Vijaya Ramachandran; J. Reif

1989-01-01

257

A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain  

Microsoft Academic Search

For estimating the states or outputs of a Markov process, the symbol-by-symbol MAP algorithm is optimal. However, this algorithm, even in its recursive form, poses technical difficulties because of numerical representation problems, the necessity of nonlinear functions and a high number of additions and multiplications. MAP like algorithms operating in the logarithmic domain presented in the past solve the numerical

P. Robertson; E. Villebrun; P. Hoeher

1995-01-01

258

Modeling TSP with Particle Swarm Optimization and Genetic Algorithm  

Microsoft Academic Search

Traveling Salesman Problem (TSP) is a classical problem of optimization for researchers and its modeling is of great interest for Engineering, Operations Research and Computer Science. For solving TSP, many methods have been proposed, including heuristic ones. Our work extends the hybrid model, based on Particle Swarm Optimization, Genetic Algorithms and Fast Local Search, for the symmetric blind travelling salesman

Shaukat Ali Khan; S. Asghar; S. Fong

2010-01-01

259

Optimization of Bacterial Strains with Variable-Sized Evolutionary Algorithms  

Microsoft Academic Search

óIn metabolic engineering it is difcult to identify which set of genetic manipulations will result in a microbial strain that achieves a desired production goal, due to the complexity of the metabolic and regulatory cellular networks and to the lack of appropriate modeling and optimization tools. In this work, Evolutionary Algorithms (EAs) are proposed for the optimization of the set

Miguel Rocha; José P. Pinto; Isabel Rocha; Eugénio C. Ferreira

2007-01-01

260

A design algorithm for the optimization of laminated composite structures  

Microsoft Academic Search

This paper is devoted to the optimal design of laminated composite structures. The goal of the study is to assess the quality and the performance of an algorithm based on the directional derivative method. Particular attention is paid to the one-dimensional search, a critical step of the process, performed by cubic splines approximation. The optimization problem is formulated as weight

Roberto Spallino; Giuseppe Giambanco; Santi Rizzo

1999-01-01

261

Optimal distributed algorithm for minimum spanning trees revisited  

Microsoft Academic Search

In an earlier paper, Awerbuch presented an innovative distributedalgorithm for solving minimum spanning tree (MST)problems that achieved optimal time and message complexitythrough the introduction of several advanced features.In this paper, we show that there are some cases where hisalgorithm can create cycles or fail to achieve optimal timecomplexity. We then show how to modify the algorithm toavoid these problems, and

Michalis Faloutsos; Mart Molle

1995-01-01

262

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

263

Genetic-algorithm-based reliability optimization for computer network expansion  

Microsoft Academic Search

This paper explains the development and implementation of a new methodology for expanding existing computer networks. Expansion is achieved by adding new communication links and computer nodes such that reliability measures of the network are optimized within specified constraints. A genetic algorithm-based computer network expansion methodology (GANE) is developed to optimize a specified objective function (reliability measure) under a given

Anup Kumar; Rakesh M. Pathak; Yash P. Gupta

1995-01-01

264

OPTIMIZING BUS TRANSIT NETWORK WITH PARALLEL ANT COLONY ALGORITHM  

Microsoft Academic Search

This study develop an optimization model for bus transit network based on road network and zonal OD. The model aims at achieving minimum transfers and maximum passenger flow per unit length with line length and non-linear rate as constraints. The coarse-grain parallel ant colony algorithm (CPACA) is used to solve the problem. To effectively search the global optimal solution, we

Zhongzhen Yang; Chuntian Cheng; Chong Liu

2005-01-01

265

Utilizing genetic algorithms for the optimal design of electromagnetic devices  

Microsoft Academic Search

A new technique for the design optimization of electromagnetic devices that adopts the genetic algorithms (GAs) as the search method is presented. The method is applied to the optimization of the shape of a pole face in an electric motor. The electromagnetic analysis of the devices implemented is performed using 2D finite elements. The results show an excellent promise and

G. Fuat Uler; Osama A. Mohammed; Chang-Seop Koh

1994-01-01

266

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

267

Comparative Assessment of Algorithms and Software for Global Optimization  

Microsoft Academic Search

The thorough evaluation of optimization algorithms and software demands devotion, time, (code development and hardware) resources, in addition to professional objectivity. This general remark is particularly valid with respect to global optimization (GO) software since GO literally encompasses “all” mathematical programming models. It is easy not only to fabricate very challenging test problems, but also to find realistic GO problems

Charoenchai Khompatraporn; János D. Pintér; Zelda B. Zabinsky

2005-01-01

268

Genetic Algorithms Based Methodologies for Optimization Designs of RC Structures  

Microsoft Academic Search

In the paper, an optimization method based on genetic algorithm was proposed. The objective of the optimization procedure is to minimize the material and construction costs of reinforced concrete structural elements subjected to serviceability and strength requirements described by the code for design of concrete structures Code. Different constraints conditions according to the code for design of concrete structures were

Lu Mingqi; Liao Xing

2010-01-01

269

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

270

Algorithmic and architectural optimizations for computationally efficient particle filtering.  

PubMed

In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speed-up of the algorithm using the methodology proposed in the paper. PMID:18390378

Sankaranarayanan, Aswin C; Srivastava, Ankur; Chellappa, Rama

2008-05-01

271

Dual Scheduling Algorithm in a Generalized Switch: Asymptotic Optimality and Throughput Optimality  

Microsoft Academic Search

In this article, we consider the dual scheduling algorithm for a generalized switch. For a saturated system, we prove the\\u000a asymptotic optimality of the dual scheduling algorithm and thus establish its fairness properties. For a system with exogenous\\u000a arrivals, we propose a modified dual scheduling algorithm, which is throughput-optimal while providing some weighted fairness\\u000a among the users at the level

Lijun Chen; Steven H. Low; John C. Doyle

272

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

273

PCNN document segmentation method based on bacterial foraging optimization algorithm  

NASA Astrophysics Data System (ADS)

Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.

Liao, Yanping; Zhang, Peng; Guo, Qiang; Wan, Jian

2014-04-01

274

Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm  

NASA Astrophysics Data System (ADS)

A multidisciplinary optimization scheme of airborne radome is proposed. The optimization procedure takes into account the structural and the electromagnetic responses simultaneously. The structural analysis is performed with the finite element method using Patran/Nastran, while the electromagnetic analysis is carried out using the Plane Wave Spectrum and Surface Integration technique. The genetic algorithm is employed for the multidisciplinary optimization process. The thicknesses of multilayer radome wall are optimized to maximize the overall transmission coefficient of the antenna-radome system under the constraint of the structural failure criteria. The proposed scheme and the optimization approach are successfully assessed with an illustrative numerical example.

Tang, Xinggang; Zhang, Weihong; Zhu, Jihong

275

Global Optimization Algorithm Using Stochastic Differential Equations.  

National Technical Information Service (NTIS)

SIGMA is a set of FORTRAN subprograms for solving the global optimization problem, which implement a method founded on the numerical solution of a Cauchy problem for stochastic differential equations inspired by quantum physics. This paper gives a detaile...

F. Aluffi-Pentini V. Parisi F. Zirilli

1985-01-01

276

Air data system optimization using a genetic algorithm  

NASA Technical Reports Server (NTRS)

An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.

Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III

1992-01-01

277

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

278

A simple checkerboard suppression algorithm for evolutionary structural optimization  

Microsoft Academic Search

.   Checkerboard patterns are quite common in various fixed grid finite element based structural optimization methods. In the\\u000a evolutionary structural optimization procedure, such checkerboard patterns have been observed under various design criteria.\\u000a The presence of checkerboard patterns makes the interpretation of optimal material distribution and subsequent geometric extraction\\u000a for manufacturing difficult. To prevent checkerboarding, an effective smoothing algorithm in terms

Q. Li; G. P. Steven; Y. M. Xie

2001-01-01

279

Robot Trajectory Planning Using Multi-objective Genetic Algorithm Optimization  

Microsoft Academic Search

\\u000a Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non trivial optimization problem.\\u000a In this paper a multi-objective genetic algorithm is proposed to address this problem. Multiple criteria are optimized up\\u000a to five simultaneous objectives. Simulations results are presented for robots with two and three degrees of freedom, considering\\u000a two and five objectives optimization. A subsequent analysis of

Eduardo José Solteiro Pires; José António Tenreiro Machado; Paulo B. De Moura Oliveira

2004-01-01

280

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

281

Optimal multigrid algorithms for calculating thermodynamic limits  

Microsoft Academic Search

Beyond eliminating the critical slowing down, multigrid algorithms can also eliminate the need to produce many independent fine-grid configurations for averaging out their statistical deviations, by averaging over the many samples produced in coarse grids during the multigrid cycle. Thermodynamic limits can be calculated to accuracy ? in justO(?-2) computer operations. Examples described in detail and with results of numerical

A. Brandt; M. Galun; D. Ron

1994-01-01

282

A superlinear interior points algorithm for engineering design optimization  

NASA Technical Reports Server (NTRS)

We present a quasi-Newton interior points algorithm for nonlinear constrained optimization. It is based on a general approach consisting of the iterative solution in the primal and dual spaces of the equalities in Karush-Kuhn-Tucker optimality conditions. This is done in such a way to have primal and dual feasibility at each iteration, which ensures satisfaction of those optimality conditions at the limit points. This approach is very strong and efficient, since at each iteration it only requires the solution of two linear systems with the same matrix, instead of quadratic programming subproblems. It is also particularly appropriate for engineering design optimization inasmuch at each iteration a feasible design is obtained. The present algorithm uses a quasi-Newton approximation of the second derivative of the Lagrangian function in order to have superlinear asymptotic convergence. We discuss theoretical aspects of the algorithm and its computer implementation.

Herskovits, J.; Asquier, J.

1990-01-01

283

A Hybrid Ant Colony Algorithm for Loading Pattern Optimization  

NASA Astrophysics Data System (ADS)

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

Hoareau, F.

2014-06-01

284

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

2014-01-01

285

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

286

Pattern search algorithms for mixed variable general constrained optimization problems  

NASA Astrophysics Data System (ADS)

A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. The Audet-Dennis Generalized Pattern Search (GPS) algorithm for bound constrained mixed variable optimization problems is extended to problems with general nonlinear constraints by incorporating a filter, in which new iterates are accepted whenever they decrease the incumbent objective function value or constraint violation function value. Additionally, the algorithm can exploit any available derivative information (or rough approximation thereof) to speed convergence without sacrificing the flexibility often employed by GPS methods to find better local optima. In generalizing existing GPS algorithms, the new theoretical convergence results presented here reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are made, a hierarchy of theoretical convergence results is given, in which the assumptions dictate what can be proved about certain limit points of the algorithm. A new Matlab(c) software package was developed to implement these algorithms. Numerical results are provided for several nonlinear optimization problems from the CUTE test set, as well as a difficult nonlinearly constrained mixed variable optimization problem in the design of a load-bearing thermal insulation system used in cryogenic applications.

Abramson, Mark Aaron

287

Optimal design of engine mount using an artificial life algorithm  

NASA Astrophysics Data System (ADS)

When designing fluid mounts, design parameters can be varied in order to obtain a desired notch frequency and notch depth. The notch frequency is a function of the mount parameters and is typically selected by the designer to occur at the vibration disturbance frequency. Since the process of choosing these parameters can involve some trial and error, it seems to be a great application for obtaining optimal performance of the mount. Many combinations of parameters are possible to give us the desired notch frequency, but the question is which combination provides the lowest depth? Therefore, an automatic optimal technique is needed to optimize the fluid mount. In this study, the enhanced artificial life algorithm (EALA) is applied to minimizing transmissibility of a fluid mount at the desired notch frequency, and at the notch and resonant frequencies. The present hybrid algorithm is the synthesis of a conventional artificial life algorithm with the random tabu search (R-tabu) method and then, the time for searching optimal solution could be reduced from the conventional artificial life algorithm and its solution accuracy became better. The results show that the performance of the optimized mount by using the hybrid algorithm has been better than that of the conventional fluid mount.

Ahn, Young Kong; Song, Jin Dae; Yang, Bo-Suk

2003-03-01

288

A Solution Quality Assessment Method for Swarm Intelligence Optimization Algorithms  

PubMed Central

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

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

2014-01-01

289

Optimal Design of Structure Using Relative Diffrence Quotient Algorithm and Improved Genetic Agorithm  

Microsoft Academic Search

The optimization design of structure with discrete variables is generally a combinatorial optimization problem. Being simple genetic algorithm has the defects of premature phenomenon, slow convergence speed and poor stability, a hybrid genetic algorithm is proposed to deal with structure optimization based on relative difference quotient method and improved genetic algorithm. The advantages of genetic algorithm in global optimization and

Sun Guofu; Ge Yanhui

2010-01-01

290

Optimization Algorithm for Designing Diffractive Optical Elements  

Microsoft Academic Search

Diffractive Optical Elements (DOEs) are commonly used in many applications such as laser beam shaping, recording of micro reliefs, wave front analysis, metrology and many others where they can replace single or multiple conventional optical elements (diffractive or refractive). One of the most versatile way to produce them, is to use computer assisted techniques for their design and optimization, as

Viviana A. Agudelo; Ricardo Ame´zquita Orozco

2008-01-01

291

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

292

Linearithmic time sparse and convex maximum margin clustering.  

PubMed

Recently, a new clustering method called maximum margin clustering (MMC) was proposed and has shown promising performances. It was originally formulated as a difficult nonconvex integer problem. To make the MMC problem practical, the researchers either relaxed the original MMC problem to inefficient convex optimization problems or reformulated it to nonconvex optimization problems, which sacrifice the convexity for efficiency. However, no approaches can both hold the convexity and be efficient. In this paper, a new linearithmic time sparse and convex MMC algorithm, called support-vector-regression-based MMC (SVR-MMC), is proposed. Generally, it first uses the SVR as the core of the MMC. Then, it is relaxed as a convex optimization problem, which is iteratively solved by the cutting-plane algorithm. Each cutting-plane subproblem is further decomposed to a serial supervised SVR problem by a new global extended-level method (GELM). Finally, each supervised SVR problem is solved in a linear time complexity by a new sparse-kernel SVR (SKSVR) algorithm. We further extend the SVR-MMC algorithm to the multiple-kernel clustering (MKC) problem and the multiclass MMC (M3C) problem, which are denoted as SVR-MKC and SVR-M3C, respectively. One key point of the algorithms is the utilization of the SVR. It can prevent the MMC and its extensions meeting an integer matrix programming problem. Another key point is the new SKSVR. It provides a linear time interface to the nonlinear kernel scenarios, so that the SVR-MMC and its extensions can keep a linearthmic time complexity in nonlinear kernel scenarios. Our experimental results on various real-world data sets demonstrate the effectiveness and the efficiency of the SVR-MMC and its two extensions. Moreover, the unsupervised application of the SVR-MKC to the voice activity detection (VAD) shows that the SVR-MKC can achieve good performances that are close to its supervised counterpart, meet the real-time demand of the VAD, and need no labeling for model training. PMID:22645273

Zhang, Xiao-Lei; Wu, Ji

2012-12-01

293

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

294

Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications  

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

295

Performance Trend of Different Algorithms for Structural Design Optimization  

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

296

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

297

Hybrid Solution of Stochastic Optimal Control Problems Using Gauss Pseudospectral Method and Generalized Polynomial Chaos Algorithms.  

National Technical Information Service (NTIS)

Two numerical methods, Gauss Pseudospectral Method and Generalized Polynomial Chaos Algorithm, were combined to form a hybrid algorithm for solving nonlinear optimal control and optimal path planning problems with uncertain parameters. The algorithm was a...

G. C. Cottrill

2012-01-01

298

Application of coevolutionary genetic algorithms for multiobjective optimization  

NASA Astrophysics Data System (ADS)

Multiobjective optimization is clearly one of the most important classes of problems in science and engineering. The solution of real problem involved in multiobjective optimization must satisfy all optimization objectives simultaneously, and in general the solution is a set of indeterminacy points. The task of multiobjective optimization is to estimate the distribution of this solution set, then to find the satisfying solution in it. Many methods solving multiobjective optimization using genetic algorithm have been proposed in recent twenty years. But these approaches tend to work negatively, causing that the population converges to small number of solutions due to the random genetic drift. To avoid this phenomenon, a multiobjective coevolutionary genetic algorithm (MoCGA) for multiobjective optimization is proposed. The primary design goal of the proposed approach is to produce a reasonably good approximation of the true Pareto front of a problem. In the algorithms, each objective corresponds to a population. At each generation, these populations compete among themselves. An ecological population density competition equation is used for reference to describe the relation between multiple objectives and to direct the adjustment over the relation at individual and population levels. The proposed approach store the Pareto optimal point obtained along the evolutionary process into external set. The proposed approach is validated using Schaffer's test function f2 and it is compared with the Niched Pareto GA (nPGA). Simulation experiments prove that the algorithm has a better performance in finding the Pareto solutions, and the MoCGA can have advantages over the other algorithms under consideration in convergence to the Pareto-optimal front.

Liu, Jian-guo; Li, Zu-shu; Wu, Wei-ping

2007-12-01

299

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

PubMed Central

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

Zhu, Zhouquan

2013-01-01

300

Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces  

NASA Technical Reports Server (NTRS)

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

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

2006-01-01

301

Improved Clonal Selection Algorithm Combined with Ant Colony Optimization  

NASA Astrophysics Data System (ADS)

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.

Gao, Shangce; Wang, Wei; Dai, Hongwei; Li, Fangjia; Tang, Zheng

302

Genetic Algorithm Optimizes Q-LAW Control Parameters  

NASA Technical Reports Server (NTRS)

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

303

Hybrid methods using genetic algorithms for global optimization  

Microsoft Academic Search

This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and genetic algorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combining principles from genetic algorithms and “hill-climbing” methods in order to

Jean-Michel Renders; StCphane P. Flasse

1996-01-01

304

A limited-memory algorithm for bound-constrained optimization  

SciTech Connect

An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based on the gradient projection method and uses a limited-memory BFGS matrix to approximate the Hessian of the objective function. We show how to take advantage of the form of the limited-memory approximation to implement the algorithm efficiently. The results of numerical tests on a set of large problems are reported.

Byrd, R.H. [Univ. of Colorado, Boulder, CO (United States). Computer Science Dept.] [Univ. of Colorado, Boulder, CO (United States). Computer Science Dept.; Peihuang, L. [Northwestern Univ., Evanston, IL (United States). Dept. of Electrical Engineering and Computer Science] [Northwestern Univ., Evanston, IL (United States). Dept. of Electrical Engineering and Computer Science; Nocedal, J. [Northwestern Univ., Evanston, IL (United States). Dept. of Electrical Engineering and Computer Science] [Northwestern Univ., Evanston, IL (United States). Dept. of Electrical Engineering and Computer Science; [Argonne National Lab., IL (United States)

1996-03-01

305

Optimal control of trading algorithms: a general impulse control approach  

Microsoft Academic Search

We propose a general framework for intraday trading based on the control of trading algorithms. Given a set of generic parameterized algorithms (which have to be specified by the controller ex-ante), our aim is to optimize the dates \\\\$(\\\\tau\\\\_i)\\\\_i\\\\$ at which they are launched, the length \\\\$(\\\\delta\\\\_i)\\\\_i\\\\$ of the trading period, and the value of the parameters \\\\$({\\\\cal E}\\\\_i)\\\\_i\\\\$ kept

Bruno Bouchard; Ngoc-Minh Dang; Charles-Albert Lehalle

2011-01-01

306

A parallel genetic algorithm for optimal designing of frame structure  

Microsoft Academic Search

A new evolutionary optimal designing method is presented for the designing of steel structures. We combine finite element structural analysis codes with genetic algorithm to minimize the total weight of the structure subject to external loads and constraints. The proposed algorithm is implemented using the Message Passing Interface(MPI) library over High Performance Computing Cluster-Dawning 4000. A master-slave paradigm is used

Ru Zhongliang; Zhao Hongbo; Zhu Chuanrui

2010-01-01

307

Feature selection for facial expression recognition based on optimization algorithm  

Microsoft Academic Search

This paper presents a wrapper approach to feature selection from image sequences and applies it to the facial expression classification problem. The pre-processing phase automatically scans image sequences and detects frames with maximum intensity of facial expression. The features are generated using the log-Gabor filters. A global optimization algorithm genetic algorithm (GA) is adopted to select a sub-set of features

Seyed Mehdi Lajevardi; Zahir M. Hussain

2009-01-01

308

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

309

PLC Implementation of a Genetic Algorithm for Controller Optimization  

Microsoft Academic Search

A common practice is to design a controller by plant observations (i.e., experiments) and to optimize some of its parameters by trial-and-error. This paper proposes a genetic algorithm for the automation of the search procedure. A chemical process is introduced to explain the proposed approach. The process is controlled by a programmable logic controller (PLC). A genetic algorithm was implemented

Paolo Dadone; Hugh F. VanLandingham

1998-01-01

310

Parameter Optimization of Ultrasonic Machining Process Using Nontraditional Optimization Algorithms  

Microsoft Academic Search

The optimum selection of process parameters is essential for advanced machining processes, as these processes incur high initial investment, tooling cost, and operating and maintenance costs. This article presents optimization aspects of an important advanced machining process known as ultrasonic machining (USM). The objective considered is maximization of material removal rate (MRR) subjected to the constraint of surface roughness. The

Ravipudi Venkata Rao; P. J. Pawar; J. P. Davim

2010-01-01

311

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations  

NASA Technical Reports Server (NTRS)

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 tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw

2005-01-01

312

A New Algorithm for Bi-Directional Evolutionary Structural Optimization  

NASA Astrophysics Data System (ADS)

In this paper, a new algorithm for bi-directional evolutionary structural optimization (BESO) is proposed. In the new BESO method, the adding and removing of material is controlled by a single parameter, i.e. the removal ratio of volume (or weight). The convergence of the iteration is determined by a performance index of the structure. It is found that the new BESO algorithm has many advantages over existing ESO and BESO methods in terms of efficiency and robustness. Several 2D and 3D examples of stiffness optimization problems are presented and discussed.

Huang, Xiaodong; Xie, Yi Min; Burry, Mark Cameron

313

Optimization of media by evolutionary algorithms for production of polyols.  

PubMed

Biotransformation of sucrose-based medium to polyols has been reported for the first time using osmophilic yeast, Hansenula anomala. A new, real coded evolutionary algorithm was developed for optimization of fermentation medium in parallel shake-flask experiments. By iteratively employing the nature-inspired techniques of selection, crossover, and mutation for a fixed number of generations, the algorithm obtains the optimal values of important process variables, namely, inoculum size and sugar, yeast extract, urea, and MgSO4 concentrations. Maximum polyols yield of 76.43% has been achieved. The method is useful for reducing the overall development time to obtain an efficient fermentation process. PMID:12396116

Patil, S V; Jayaraman, V K; Kulkarni, B D

2002-01-01

314

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

PubMed

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

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

2008-11-01

315

A separable linear algorithm for hydropower optimization  

SciTech Connect

A deterministic, separable, linear algorithm is presented for maximizing aggregate hydropower production. The method is iterative and amenable to solution using standard LP software. The utility of the technique is demonstrated using several test applications involving a hypothetical single-purpose hydropower reservoir and a monthly increment 20-year flow record from the Gunpowder River in Maryland. The separable linearized forms solved quickly using MPSX on a variety of IBM hardware: 3090-400 VF, 3084 QX, dual processor 4381-3, and an AT/370 personal computer. For comparison purposes, the original nonlinear nonseparable version of the model was also solved using MINOS. This yielded a value of aggregate hydropower marginally higher than that using MPSX. The separable, linearized methodology proved to be a useful and an efficient means of generating good starting points for MINOS. The use of these warm starts effected substantial reductions in MINOS execution times.

Ellis, J.H.; ReVelle, C.S. (Johns Hopkins Univ., Baltimore, MD (USA))

1988-04-01

316

Optimized Mapping Modes and Algorithms for ALFA  

NASA Astrophysics Data System (ADS)

The task of mapping the Galactic Arecibo sky with ALFA presents some interesting and formidable challenges. As with all radio telescopes, the issues of variable gain and system temperature make creating viable, systematic-free maps difficult. In addition to these typical problems, each of the seven ALFA beams are non-gaussian and have strong first sidelobes that vary dramatically from beam to beam in orientation. I will describe the optimal observing methods for reducing systematics, and what techniques can be used to robustly transform the acquired time-ordered data into an optimum map.

Goldston, J. E.

2004-12-01

317

Optimized approximation algorithm in neural networks without overfitting.  

PubMed

In this paper, an optimized approximation algorithm (OAA) is proposed to address the overfitting problem in function approximation using neural networks (NNs). The optimized approximation algorithm avoids overfitting by means of a novel and effective stopping criterion based on the estimation of the signal-to-noise-ratio figure (SNRF). Using SNRF, which checks the goodness-of-fit in the approximation, overfitting can be automatically detected from the training error only without use of a separate validation set. The algorithm has been applied to problems of optimizing the number of hidden neurons in a multilayer perceptron (MLP) and optimizing the number of learning epochs in MLP's backpropagation training using both synthetic and benchmark data sets. The OAA algorithm can also be utilized in the optimization of other parameters of NNs. In addition, it can be applied to the problem of function approximation using any kind of basis functions, or to the problem of learning model selection when overfitting needs to be considered. PMID:18541499

Liu, Yinyin; Starzyk, Janusz A; Zhu, Zhen

2008-06-01

318

An efficient cuckoo search algorithm for numerical function optimization  

NASA Astrophysics Data System (ADS)

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

Ong, Pauline; Zainuddin, Zarita

2013-04-01

319

An Interactive Branch-and-Bound Algorithm for Multiple Criteria Optimization.  

National Technical Information Service (NTIS)

The report presents a new interactive algorithm for multiple criteria optimization. The algorithm is of the branch-and-bound type, and differs from previous interactive algorithms in several ways. First, the field of application of the algorithm is wider ...

O. Marcotte R. M. Soland

1981-01-01

320

Control optimization, stabilization and computer algorithms for aircraft applications  

NASA Technical Reports Server (NTRS)

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

1975-01-01

321

Three modified versions of differential evolution algorithm for continuous optimization  

Microsoft Academic Search

Differential evolution (DE) is one simple and effective evolutionary algorithm (EA) for global optimization. In this paper,\\u000a three modified versions of the DE to improve its performance, to repair its defect in accurate converging to individual optimal\\u000a point and to compensate the limited amount of search moves of original DE are proposed. In the first modified version called\\u000a bidirectional differential

Morteza Alinia Ahandani; Naser Pourqorban Shirjoposh; Reza Banimahd

2010-01-01

322

Optimization of heat pump using fuzzy logic and genetic algorithm  

Microsoft Academic Search

Heat pumps offer economical alternatives of recovering heat from different sources for use in various industrial, commercial\\u000a and residential applications. In this study, single-stage air-source vapor compression heat pump system has been optimized\\u000a using genetic algorithm (GA) and fuzzy logic (FL). The necessary thermodynamic properties for optimization were calculated\\u000a by FL. Thermodynamic properties obtained with FL were compared with actual

Arzu ?encan ?ahin; Bayram K?l?ç; Ula? K?l?ç

323

Optimal Configuration of a Square Array Group Testing Algorithm  

Microsoft Academic Search

We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of

Michael G. Hudgens; Hae-Young Kim

2011-01-01

324

An Alternative Approach to Inverse Planning Optimization: Applying the Projection Theorem to Concave and Convex PTVs for VMAT Delivery  

Microsoft Academic Search

We present an alternative technique for inverse planning optimization and apply it to volumetric modulated arc therapy (VMAT)\\u000a delivery in one rotation with a prior knowledge about the type of leaf motions. The optimization is based on the Projection\\u000a Theorem in inner product spaces. MLC motion is directly considered in the optimization, thus avoiding leaf segmentation. In\\u000a this work we

W. Hoegele; R. Loeschel; P. Zygmanski

325

Global optimization of an accelerator lattice using multiobjective genetic algorithms  

NASA Astrophysics Data System (ADS)

Storage ring lattice design is a highly constrained multiobjective optimization problem. The objectives can include lattice functions or derived quantities like emittance, brightness, or luminosity while simultaneously fulfilling constraints such as linear stability of the lattice. In this paper we explore the use of multiobjective genetic algorithms (MOGA) to find globally optimized lattice settings in a storage ring. Using the Advanced Light Source (ALS) for illustration, three examples of MOGA are shown and analyzed—(i) using three fit parameters to optimize the straight section betatron function and the natural emittance, (ii) using three fit parameters to optimize the photon brightness of bending magnet and insertion device source points in the lattice and (iii) a six parameter fit creating alternating high and low horizontal betatron functions in subsequent straight sections while still minimizing the natural emittance. Making use of one of the main benefits of MOGA, we also study the trade-offs in the optimization objectives between sets of optimal solutions.

Yang, Lingyun; Robin, David; Sannibale, Fernando; Steier, Christoph; Wan, Weishi

2009-10-01

326

Optimal FLD algorithm for facial feature extraction  

NASA Astrophysics Data System (ADS)

In this paper, we try to extend Fisher linear discriminant analysis (FLD) to the singular cases. Firstly, PCA is used to reduce the dimension of feature space to N-1 (N denotes the number of training samples). Then, the transformed space is divided into two subspaces: the null space of within- class scatter matrix and its orthogonal complement, from which two cases of optimal discriminant vectors are selected respectively. Finally, we test our method on ORL face database, and achieve a recognition rate of 97% with a minimum distance classifier or a nearest neighbor classifier. The experimental results indicate that our approach is better than classical Eigenfaces and Fisherfaces with respect to recognition performance.

Yang, Jian; Yang, Jingyu

2001-10-01

327

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

328

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

329

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

330

Harmonic optimization of multilevel converters using genetic algorithms  

Microsoft Academic Search

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

Burak Ozpineci; Leon M. Tolbert; John N. Chiasson

2005-01-01

331

Harmonic optimization of multilevel converters using genetic algorithms  

Microsoft Academic Search

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

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

2004-01-01

332

A New Algorithm for BiDirectional Evolutionary Structural Optimization  

Microsoft Academic Search

In this paper, a new algorithm for bi-directional evolutionary structural optimization (BESO) is proposed. In the new BESO method, the adding and removing of material is controlled by a single parameter, i.e. the removal ratio of volume (or weight). The convergence of the iteration is determined by a performance index of the structure. It is found that the new BESO

Xiaodong Huang; Yi Min Xie; Mark Cameron Burry

2006-01-01

333

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

334

Attitude determination using vector observations: A fast optimal matrix algorithm  

NASA Technical Reports Server (NTRS)

The attitude matrix minimizing Wahba's loss function is computed directly by a method that is competitive with the fastest known algorithm for finding this optimal estimate. The method also provides an estimate of the attitude error covariance matrix. Analysis of the special case of two vector observations identifies those cases for which the TRIAD or algebraic method minimizes Wahba's loss function.

Markley, F. Landis

1993-01-01

335

Numerical Optimization Algorithms and Software for Systems Biology  

SciTech Connect

The basic aims of this work are: to develop reliable algorithms for solving optimization problems involving large stoi- chiometric matrices; to investigate cyclic dependency between metabolic and macromolecular biosynthetic networks; and to quantify the significance of thermodynamic constraints on prokaryotic metabolism.

Saunders, Michael

2013-02-02

336

Optimization of composite panels using neural networks and genetic algorithms  

Microsoft Academic Search

The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using Genetic Algorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a wide scope of discrete and continuous design variables that can be

W. Ruijter; R. Spallino; L. Warneta; A. de Boera

2003-01-01

337

Optimization flow control—I: basic algorithm and convergence  

Microsoft Academic Search

We propose an optimization approach to o w control where the objective is to maximize the aggregate source utility over their transmission rates. We view net- work links and sources as processors of a distributed com- putation system to solve the dual problem using gradient projection algorithm. In this system sources select trans- mission rates that maximize their own benets,

Steven H. Low; David E. Lapsley

1999-01-01

338

The evolution of optimal linear polyfractal arrays using genetic algorithms  

Microsoft Academic Search

Recently, in order to successfully combine the positive attributes of both periodic and random arrays into one design, a novel class of arrays, known as fractal-random arrays, has been introduced. In addition, several researchers have successfully used genetic algorithms, robust global optimization techniques based on natural selection, to find solutions to complex array layout problems. This paper introduces a type

Joshua S. Petko; Douglas H. Werner

2005-01-01

339

Improved shuffled frog leaping algorithm for continuous optimization problem  

Microsoft Academic Search

Shuffled frog leaping algorithm (SFLA) is mainly used for the discrete space optimization. For SFLA, the population is divided into several memeplexes, several frogs of each memeplex are selected to compose a submemeplex for local evolvement, according to the mechanism that the worst frog learns from the best frog in submemeplex or the best frog in population, and the memeplexes

Ziyang Zhen; DaoBo Wang; Yuanyuan Liu

2009-01-01

340

Comparison between Genetic Algorithms and Particle Swarm Optimization  

Microsoft Academic Search

This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from

Russell C. Eberhart; Yuhui Shi

1998-01-01

341

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

342

Optimal algorithms theory for robust estimation and prediction  

Microsoft Academic Search

This paper deals with the theory of optimal algorithms for problems which cannot be solved exactly. The lheory developed allows for the derivation of new and interesting results in parameter estimation and in time series prediction in situations where no reliable statistical hypothesis can be made on the functions and modeling errors involved, but only a bound on them is

MARIO MILANESE; ROBERTO TEMPO

1985-01-01

343

An algorithm of global optimization for solving layout problems  

Microsoft Academic Search

The two-dimensional layout problem is known to be NP-complete, and the current research work is basically in the heuristic way. In this paper, we mainly discuss the methods for solving layout problem about the artificial satellite module by virtue of graph theory and group theory. Also, an algorithm of global optimization is presented first time. The method given here can

Enmin Feng; Xilu Wang; Xiumei Wang; Hongfei Teng

1999-01-01

344

Methods of optimization of milling parameters based on genetic algorithm  

Microsoft Academic Search

In this paper, the genetic algorithm is used to optimize the milling parameters in the milling process so that the tool life can be enhanced and processing costs can be reduced. LABVIEW is used as software development platform to program, monitor the tool wear and determine the tool life. Through the method of orthogonal experiment to design experiment and then

Chengqiang Zhang; Jie Chen

2009-01-01

345

Slotting optimization of warehouse based on hybrid genetic algorithm  

Microsoft Academic Search

Traditional warehouse operation management always relies on experience to arrange inventory goods to available space once they arrived, resulting in the inefficient warehouse work. This paper considers goods' turnover rate and shelves' stability as principles to construct a multiobjective optimization mathematical model. By setting up random goal weight to improve traditional genetic algorithm, and based on MATLAB software platform to

Qiaohong Zu; Mengmeng Cao; Fang Guo; Yeqing Mu

2011-01-01

346

Genetic algorithm based optimization of an agent based queuing system  

Microsoft Academic Search

Queuing research and its applications have been studied extensively by concentrating mainly on design, performance and running of the service facility under study. In this paper we show how a simple behavioral queuing system can be modeled using a Cellular Automata; and then we show how a Genetic Algorithm can be used to optimize the behavioral properties of this agent

K. Sankaranarayanan; E. R. Larsen; A. van Ackere; C. A. Delgado

2010-01-01

347

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

348

Environmental Optimization Using the WAste Reduction Algorithm (WAR)  

EPA Science Inventory

Traditionally chemical process designs were optimized using purely economic measures such as rate of return. EPA scientists developed the WAste Reduction algorithm (WAR) so that environmental impacts of designs could easily be evaluated. The goal of WAR is to reduce environme...

349

Identification of the optimal emotion recognition algorithm using physiological signals  

Microsoft Academic Search

In human-computer interaction researches, emotion recognition systems based on physiological signals have introduced. This study was to identify the optimal emotion recognition algorithm for classification of seven emotional states (happiness, sadness, anger, fear, disgust, surprise, and stress) using physiological signals. 12 college students participated in this experiment over 10 times. To induce each emotion, 10 emotional stimuli sets which had

E.-H. Jang; B.-J. Park; S.-H. Kim; Y. Eum; J.-H. Sohn

2011-01-01

350

An optimal estimation algorithm for multiuser chaotic communications systems  

Microsoft Academic Search

In a multiuser chaotic communications scheme, each user's information is modulated with a different chaotic system and then transmitted independently. However, the receiver sees the superposition of these signals with additional noise. In this paper, a novel optimal estimation algorithm for such multiuser chaotic communications systems is presented. The goal is to estimate the chaotic signal being sent by each

M. Cifici; Douglas B. Williams

2002-01-01

351

The quantum adiabatic optimization algorithm and local minima  

Microsoft Academic Search

The quantum adiabatic optimization algorithm uses the adiabatic theorem from quantum physics to minimize a function by interpolation between two Hamiltonians. The quantum wave function can sometimes tunnel through significant obstacles. However it can also sometimes get stuck in local minima, even for fairly simple problems. An initial Hamiltonian which insufficiently mixes computational basis states is analogous to a poorly

Ben W. Reichardt

2004-01-01

352

Optimization of local control of chaos by an evolutionary algorithm  

Microsoft Academic Search

An evolutionary algorithm for optimizing local control of chaos is presented. Based on a Lyapunov approach, a linear control law and the state-space region in which this control law is activated are determined. In addition, we study a relation between certain adjustable design parameters and a particular measure of the uncontrolled chaotic attractor in the state-space region of control (SSRC).

Hendrik Richter; Kurt J. Reinschke

2000-01-01

353

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

354

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm  

NASA Astrophysics Data System (ADS)

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

Burger, Christoph

355

Research reactor loading pattern optimization using estimation of distribution algorithms  

SciTech Connect

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

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

2006-07-01

356

A scatter learning particle swarm optimization algorithm for multimodal problems.  

PubMed

Particle swarm optimization (PSO) has been proved to be an effective tool for function optimization. Its performance depends heavily on the characteristics of the employed exemplars. This necessitates considering both the fitness and the distribution of exemplars in designing PSO algorithms. Following this idea, we propose a novel PSO variant, called scatter learning PSO algorithm (SLPSOA) for multimodal problems. SLPSOA contains some new algorithmic features while following the basic framework of PSO. It constructs an exemplar pool (EP) that is composed of a certain number of relatively high-quality solutions scattered in the solution space, and requires particles to select their exemplars from EP using the roulette wheel rule. By this means, more promising solution regions can be found. In addition, SLPSOA employs Solis and Wets' algorithm as a local searcher to enhance its fine search ability in the newfound solution regions. To verify the efficiency of the proposed algorithm, we test it on a set of 16 benchmark functions and compare it with six existing typical PSO algorithms. Computational results demonstrate that SLPSOA can prevent premature convergence and produce competitive solutions. PMID:24108491

Ren, Zhigang; Zhang, Aimin; Wen, Changyun; Feng, Zuren

2014-07-01

357

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

358

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

359

Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms  

Microsoft Academic Search

This paper analyses extensions of No-Free-Lunch (NFL) theorems to countably infinite and uncountable infinite domains and investigates the design of optimal optimization algorithms. The original NFL theorem due to Wolpert and Macready states that, for finite search domains, all search heuristics have the same performance when averaged over the uniform distribution over all possible functions. For infinite domains the extension

Anne Auger; Olivier Teytaud

2010-01-01

360

Optimal Placement and Sizing of Distributed Generator Units using Genetic Optimization Algorithms  

Microsoft Academic Search

In this article the authors describe how genetic optimization algorithms can be used to find the optimal size and location of distributed generation units in a residential distri- bution grid. Power losses are minimized while the voltage profile is kept at an acceptable level. The method is applied on a system based on an existing grid topology with pro- duction

Edwin Haesen; Marcelo Espinoza; Bert Pluymers; Ivan Goethals; Vu Van Thong; Johan Driesen; Ronnie Belmans; Bart De Moor

361

Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm  

NASA Technical Reports Server (NTRS)

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

Oyama, Akira; Liou, Meng-Sing

2001-01-01

362

Research on Optimization of Encoding Algorithm of PDF417 Barcodes  

NASA Astrophysics Data System (ADS)

The purpose of this research is to develop software to optimize the data compression of a PDF417 barcode using VC++6.0. According to the different compression mode and the particularities of Chinese, the relevant approaches which optimize the encoding algorithm of data compression such as spillage and the Chinese characters encoding are proposed, a simple approach to compute complex polynomial is introduced. After the whole data compression is finished, the number of the codeword is reduced and then the encoding algorithm is optimized. The developed encoding system of PDF 417 barcodes will be applied in the logistics management of fruits, therefore also will promote the fast development of the two-dimensional bar codes.

Sun, Ming; Fu, Longsheng; Han, Shuqing

363

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

364

A numerical algorithm for singular optimal LQ control systems  

NASA Astrophysics Data System (ADS)

A numerical algorithm to obtain the consistent conditions satisfied by singular arcs for singular linear-quadratic optimal control problems is presented. The algorithm is based on the Presymplectic Constraint Algorithm (PCA) by Gotay-Nester (Gotay et al., J Math Phys 19:2388-2399, 1978; Volckaert and Aeyels 1999) that allows to solve presymplectic Hamiltonian systems and that provides a geometrical framework to the Dirac-Bergmann theory of constraints for singular Lagrangian systems (Dirac, Can J Math 2:129-148, 1950). The numerical implementation of the algorithm is based on the singular value decomposition that, on each step, allows to construct a semi-explicit system. Several examples and experiments are discussed, among them a family of arbitrary large singular LQ systems with index 2 and a family of examples of arbitrary large index, all of them exhibiting stable behaviour.

Delgado-Téllez, Marina; Ibort, Alberto

2009-08-01

365

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

366

Optimization of image processing algorithms on mobile platforms  

NASA Astrophysics Data System (ADS)

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

Poudel, Pramod; Shirvaikar, Mukul

2011-02-01

367

Threshold optimization of adaptive template filtering for MRI based on intelligent optimization algorithm.  

PubMed

Intelligent Optimization Algorithm (IOA) mainly includes Immune Algorithm (IA) and Genetic Algorithm (GA). One of the most important characteristics of MRI is the complicated changes of gray level. Traditional filtering algorithms are not fit for MRI. Adaptive Template Filtering Method (ATFM) is an appropriate denoising method for MRI. However, selecting threshold for ATFM is a complicated problem which directly affects the denoising result. Threshold selection has been based on experience. Thus, it was lack of solid theoretical foundation. In this paper, 2 kinds of IOA are proposed for threshold optimization respectively. As our experiment demonstrates, they can effectively solve the problem of threshold selection and perfect ATFM. Through algorithm analysis, the performance of IA surpasses the performance of GA. As a new kind of IOA, IA exhibits its great potential in image processing. PMID:17945854

Guo, Lei; Wu, Youxi; Liu, Xuena; Li, Ying; Xu, Guizhi; Yan, Weili

2006-01-01

368

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm  

NASA Technical Reports Server (NTRS)

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

2005-01-01

369

Optimization of warfarin dose by population-specific pharmacogenomic algorithm.  

PubMed

To optimize the warfarin dose, a population-specific pharmacogenomic algorithm was developed using multiple linear regression model with vitamin K intake and cytochrome P450 IIC polypeptide9 (CYP2C9(*)2 and (*)3), vitamin K epoxide reductase complex 1 (VKORC1(*)3, (*)4, D36Y and -1639 G>A) polymorphism profile of subjects who attained therapeutic international normalized ratio as predictors. New algorithm was validated by correlating with Wadelius, International Warfarin Pharmacogenetics Consortium and Gage algorithms; and with the therapeutic dose (r=0.64, P<0.0001). New algorithm was more accurate (Overall: 0.89 vs 0.51, warfarin resistant: 0.96 vs 0.77 and warfarin sensitive: 0.80 vs 0.24), more sensitive (0.87 vs 0.52) and specific (0.93 vs 0.50) compared with clinical data. It has significantly reduced the rate of overestimation (0.06 vs 0.50) and underestimation (0.13 vs 0.48). To conclude, this population-specific algorithm has greater clinical utility in optimizing the warfarin dose, thereby decreasing the adverse effects of suboptimal dose. PMID:21358752

Pavani, A; Naushad, S M; Rupasree, Y; Kumar, T R; Malempati, A R; Pinjala, R K; Mishra, R C; Kutala, V K

2012-08-01

370

Leveraging off genetic algorithms for optimizing AGRIN lenses  

NASA Astrophysics Data System (ADS)

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

Manhart, Paul K.; Sparrold, Scott W.

2000-10-01

371

Optimization of an Antenna Array Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

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

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

2014-06-01

372

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

373

Hierarchical artificial bee colony algorithm for RFID network planning optimization.  

PubMed

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

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

2014-01-01

374

Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization  

PubMed Central

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

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

2014-01-01

375

Using Heuristic Algorithms to Optimize Observing Target Sequences  

NASA Astrophysics Data System (ADS)

The preparation of observations is normally carried out at the telescope by the visiting observer. In order to help the observer, we propose several algorithms to automatically optimize the sequence of targets. The optimization consists of assuring that all the chosen targets are observable within the given time interval, and to find their best execution order in terms of the observation quality and the shortest telescope displacement time. Since an exhaustive search is too expensive in time, we researched heuristic algorithms, specifically: Min-Conflict, Non-Sorting Genetic Algorithms and Simulated Annealing. Multiple metaheuristics are used in parallel to swiftly give an approximation of the best solution, with all the constraints satis�ed and the total execution time minimized. The optimization process has a duration on the order of tens of seconds, allowing for quick re-adaptation in case of changing atmospheric conditions. The graphical user interface allows the user to control the parameters of the optimization process. Therefore, the search can be adjusted in real time. The module was coded in a way to allow easily the addition of new constraints, and thus ensure its compatibility with different instruments. For now, the application runs as a plug-in to the observation preparation tool called New Short Term Scheduler, which is used on three spectrographs dedicated to the exoplanets search: HARPS at the La Silla observatory, HARPS North at the La Palma observatory and SOPHIE at the Observatoire de Haute-Provence.

Sosnowska, D.; Ouadahi, A.; Buchschacher, N.; Weber, L.; Pepe, F.

2014-05-01

376

Algorithms for optimal sequencing of dynamic multileaf collimators  

NASA Astrophysics Data System (ADS)

Dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is used to deliver intensity modulated beams using a multileaf collimator (MLC), with the leaves in motion. DMLC-IMRT requires the conversion of a radiation intensity map into a leaf sequence file that controls the movement of the MLC while the beam is on. It is imperative that the intensity map delivered using the leaf sequence file be as close as possible to the intensity map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf-sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf-sequencing algorithms for dynamic multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under the most common leaf movement constraints that include leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bi-directional movement of the MLC leaves.

Kamath, Srijit; Sahni, Sartaj; Palta, Jatinder; Ranka, Sanjay

2004-01-01

377

Optimization of multicast optical networks with genetic algorithm  

NASA Astrophysics Data System (ADS)

In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

2007-12-01

378

Water distribution network optimization using a modified genetic algorithm  

NASA Astrophysics Data System (ADS)

A modified genetic algorithm (GA) is proposed for water distribution network optimization. Several changes are introduced in the selection and mutation processes of a simple GA. In each generation a constant number of solutions is eliminated, the selected ones are ranked for crossover, and the new solutions are allowed to undergo at most one mutation. All these modifications greatly increase the algorithm convergence. The modified GA is tested on the New York City water supply expansion problem. It obtains the lowest-cost feasible solution reported in the literature in far fewer generations than any previous GA.

Montesinos, Pilar; Garcia-Guzman, Adela; Ayuso, Jose Luis

1999-11-01

379

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 3.87 s 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

2013-12-01

380

Solving complex economic load dispatch problems using biogeography-based optimization  

Microsoft Academic Search

This paper presents an algorithm, biogeography-based optimization (BBO) to solve both convex and non-convex economic load dispatch (ELD) problems of thermal generators of a power system. The Proposed methodology easily takes care of solving non-convex economic dispatch problems considering different constraints such as transmission losses, ramp rate limits, multi-fuel options and prohibited operating zones. Biogeography deals with the geographical distribution

Aniruddha Bhattacharya; P. K. Chattopadhyay

2010-01-01

381

Automatic Treatment Planning with Convex Imputing  

NASA Astrophysics Data System (ADS)

Current inverse optimization-based treatment planning for radiotherapy requires a set of complex DVH objectives to be simultaneously minimized. This process, known as multi-objective optimization, is challenging due to non-convexity in individual objectives and insufficient knowledge in the tradeoffs among the objective set. As such, clinical practice involves numerous iterations of human intervention that is costly and often inconsistent. In this work, we propose to address treatment planning with convex imputing, a new-data mining technique that explores the existence of a latent convex objective whose optimizer reflects the DVH and dose-shaping properties of previously optimized cases. Using ten clinical prostate cases as the basis for comparison, we imputed a simple least-squares problem from the optimized solutions of the prostate cases, and show that the imputed plans are more consistent than their clinical counterparts in achieving planning goals.

Sayre, G. A.; Ruan, D.

2014-03-01

382

Parallel Algorithms for Graph Optimization using Tree Decompositions  

SciTech Connect

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

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

2012-06-01

383

Implementation and Optimization of Image Processing Algorithms on Embedded GPU  

NASA Astrophysics Data System (ADS)

In this paper, we analyze the key factors underlying the implementation, evaluation, and optimization of image processing and computer vision algorithms on embedded GPU using OpenGL ES 2.0 shader model. First, we present the characteristics of the embedded GPU and its inherent advantage when compared to embedded CPU. Additionally, we propose techniques to achieve increased performance with optimized shader design. To show the effectiveness of the proposed techniques, we employ cartoon-style non-photorealistic rendering (NPR), speeded-up robust feature (SURF) detection, and stereo matching as our example algorithms. Performance is evaluated in terms of the execution time and speed-up achieved in comparison with the implementation on embedded CPU.

Singhal, Nitin; Yoo, Jin Woo; Choi, Ho Yeol; Park, In Kyu

384

Multi-Grid Genetic Algorithms For Optimal Radiation Shield Design  

NASA Astrophysics Data System (ADS)

Genetic Algorithms (GA) are a powerful search and optimization technique that can be applied to numerous problems. Unfortunately. GA relies on large numbers of fitness evaluations to determine the relative merits of various solutions to a problem. For problems requiring computationally intensive fitness evaluations this can make GA too expensive to use. We describe a hierarchical technique that we have created called Multi-Grid Genetic Algorithms (MGGA). MGGA leverages the geometry of a problem space to build a hierarchy of increasingly smaller problem spaces. Optimizations over these smaller spaces are used to seed a population of solutions in a larger space. We explore how MGGA can be applied to several radiation shielding problems.

Asbury, Stephen T.

385

Genetic algorithm application in optimization of wireless sensor networks.  

PubMed

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

Norouzi, Ali; Zaim, A Halim

2014-01-01

386

Mixed Models for the Analysis of Optimization Algorithms  

NASA Astrophysics Data System (ADS)

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

Chiarandini, Marco; Goegebeur, Yuri

387

Optimized confidence weights for localization algorithms with scarce information  

Microsoft Academic Search

A method to derive weights to be used in distance-based multi-dimensional scaling (MDS) source localization algorithms under scarce information is discussed. In particular, a family of weighing function is derived with basis on small-scale statistics and the parameter that drives the choice of a particular weighing function out of such a family is optimized with basis on an information-theoretical criterion.

Giuseppe Destino; G. T. F. de Abreu

2008-01-01

388

Robustness in multi-objective optimization using evolutionary algorithms  

Microsoft Academic Search

This work discusses robustness assessment during multi-objective optimization with a Multi-Objective Evolutionary Algorithm\\u000a (MOEA) using a combination of two types of robustness measures. Expectation quantifies simultaneously fitness and robustness,\\u000a while variance assesses the deviation of the original fitness in the neighborhood of the solution. Possible equations for\\u000a each type are assessed via application to several benchmark problems and the selection

A. Gaspar-Cunha; J. A. Covas

2008-01-01

389

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

390

Boundary Search for Constrained Numerical Optimization Problems in ACO Algorithms  

Microsoft Academic Search

This paper presents a novel boundary approach which is included as a constraint-handling technique in an ant colony algorithm.\\u000a The necessity of approaching the boundary between the feasible and infeasible search space for many constrained optimization\\u000a problems is a paramount challenge for every constraint-handling technique. Our proposed technique precisely focuses the search\\u000a on the boundary region and can be either

Guillermo Leguizamón; Carlos A. Coello Coello

2006-01-01

391

A handover optimization algorithm with mobility robustness for LTE systems  

Microsoft Academic Search

A large number of cells will be deployed to provide high speed services in any places using the Long-Term Evolution (LTE) system. The management of such a large number of cells increases the operating expenditure (OPEX). Self-organizing network (SON) attracts attention as an effective way to reduce OPEX. This paper proposes a self-optimization algorithm for handover (HO) parameters. In conventional

Koichiro Kitagawa; Toshihiko Komine; Toshiaki Yamamoto; Satoshi Konishi

2011-01-01

392

Stochastic search in structural optimization - Genetic algorithms and simulated annealing  

NASA Technical Reports Server (NTRS)

An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

Hajela, Prabhat

1993-01-01

393

CONVEX mini manual  

NASA Technical Reports Server (NTRS)

The use of the CONVEX computers that are an integral part of the Supercomputing Network Subsystems (SNS) of the Central Scientific Computing Complex of LaRC is briefly described. Features of the CONVEX computers that are significantly different than the CRAY supercomputers are covered, including: FORTRAN, C, architecture of the CONVEX computers, the CONVEX environment, batch job submittal, debugging, performance analysis, utilities unique to CONVEX, and documentation. This revision reflects the addition of the Applications Compiler and X-based debugger, CXdb. The document id intended for all CONVEX users as a ready reference to frequently asked questions and to more detailed information contained with the vendor manuals. It is appropriate for both the novice and the experienced user.

Tennille, Geoffrey M.; Howser, Lona M.

1993-01-01

394

Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework  

SciTech Connect

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

Alicia Hofler, Pavel Evtushenko, Frank Marhauser

2009-09-01

395

Multilevel image thresholding by using the shuffled frog-leaping optimization algorithm  

Microsoft Academic Search

In this paper, a new multilevel MCET algorithm using the shuffled frog-leaping optimization (SFLO) algorithm is proposed. The proposed image thresholding algorithm is called SFLO-based MCET algorithm. Three different methods including the exhaustive search, the honey bee mating optimization (HBMO) and the particle swarm optimization (PSO) algorithms are also implemented for comparison. The experimental results demonstrate that the proposed SFLO-based

Ming-Huwi Horng

2011-01-01

396

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

397

Advanced metaheuristic algorithms for laser optimization in optical accelerator technologies  

NASA Astrophysics Data System (ADS)

Lasers are among the most important experimental tools for user facilities, including synchrotron radiation and free electron lasers (FEL). In the synchrotron radiation field, lasers are widely used for experiments with Pump-Probe techniques. Especially for X-ray-FELs, lasers play important roles as seed light sources or photocathode-illuminating light sources to generate a high-brightness electron bunch. For future accelerators, laser-based techonologies such as electro-optic (EO) sampling to measure ultra-short electron bunches and optical-fiber-based femtosecond timing systems have been intensively developed in the last decade. Therefore, controls and optimizations of laser pulse characteristics are strongly required for many kinds of experiments and improvement of accelerator systems. However, people believe that lasers should be tuned and customized for each requirement manually by experts. This makes it difficult for laser systems to be part of the common accelerator infrastructure. Automatic laser tuning requires sophisticated algorithms, and the metaheuristic algorithm is one of the best solutions. The metaheuristic laser tuning system is expected to reduce the human effort and time required for laser preparations. I have shown some successful results on a metaheuristic algorithm based on a genetic algorithm to optimize spatial (transverse) laser profiles, and a hill-climbing method extended with a fuzzy set theory to choose one of the best laser alignments automatically for each machine requirement.

Tomizawa, Hiromitsu

2011-10-01

398

Optimized design of embedded DSP system hardware supporting complex algorithms  

NASA Astrophysics Data System (ADS)

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 communication. Because of employing a high performance-price ratio DSP TMS320C6712 and a large FLASH in the design, this system permits loading and performing complex algorithms with little algorithm optimization and code reduction. The CPLD provides flexible logic control for the whole DSP board, especially in input channel, and allows convenient interface between different sensors and DSP system. The transceiver circuit can transfer data between DSP and host computer. In the paper, some key technologies are also introduced which make the whole system work efficiently. Because of the characters referred above, the hardware is a perfect flat for multi-channel data collection, image processing, and other signal processing with high performance and adaptability. The application section of this paper presents how this hardware is adapted for the biometric identification system with high identification precision. The result reveals that this hardware is easy to interface with a CMOS imager and is capable of carrying out complex biometric identification algorithms, which require real-time process.

Li, Yanhua; Wang, Xiangjun; Zhou, Xinling

2003-09-01

399

Neural networks for convex hull computation.  

PubMed

Computing convex hull is one of the central problems in various applications of computational geometry. In this paper, a convex hull computing neural network (CHCNN) is developed to solve the related problems in the N-dimensional spaces. The algorithm is based on a two-layered neural network, topologically similar to ART, with a newly developed adaptive training strategy called excited learning. The CHCNN provides a parallel online and real-time processing of data which, after training, yields two closely related approximations, one from within and one from outside, of the desired convex hull. It is shown that accuracy of the approximate convex hulls obtained is around O[K(-1)(N-1/)], where K is the number of neurons in the output layer of the CHCNN. When K is taken to be sufficiently large, the CHCNN can generate any accurate approximate convex hull. We also show that an upper bound exists such that the CHCNN will yield the precise convex hull when K is larger than or equal to this bound. A series of simulations and applications is provided to demonstrate the feasibility, effectiveness, and high efficiency of the proposed algorithm. PMID:18255663

Leung, Y; Zhang, J S; Xu, Z B

1997-01-01

400

GQSD: The new algorithm for optimizing the quality of service heterogeneous possessing sources at distributed systems  

Microsoft Academic Search

In this paper we proposed GQSD algorithm that has created new technical method to decrease the maximum number of repetitions at generations also we will optimize the processing sources schedule at offered algorithm rather than previous optimization algorithms. GQSD is a novel algorithm that by defining new parameters and metrics, has decreased the delay and also response time of job.

Arash Ghorbannia Delavar; M. Rahmany; M. Nejadkheirallah; Kobra Darvish

2010-01-01

401

Application of Genetic Algorithm in the Optimization of Water Pollution Control Scheme  

Microsoft Academic Search

Genetic Algorithm (Genetic Algorithm Chine write for the GA) is a kind of hunting Algorithm bionic global optimization imitating the Darwinian biological evolution theories, is advancing front of complex nonlinear science and artificial intelligence science. In the basic of introducing the GA basic principle and optimization Algorithm, this text leads the GA into the domain of the water pollution control

Rui-Ming Zhao; Dong-Ping Qian

2007-01-01

402

[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

403

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm  

PubMed Central

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

Svecko, Rajko

2014-01-01

404

Multi-parameter optimization of electrostatic micro-generators using design optimization algorithms  

NASA Astrophysics Data System (ADS)

In this paper, the design of an electrostatic micro-generator with an in-plane area-overlap architecture is optimized in a six-dimensional parameter space using multi-parameter optimization algorithms. A parametric model is presented including four geometric and two electrical parameters. The constraints of the design parameters are discussed. The design optimization is carried out in modeFRONTIER using a genetic algorithm. The results show that the displacement limit and the number of electrode elements are essential parameters, which require optimization in the design process. The other parameters take values at the upper or lower bound of their design space. The results also demonstrate that a maximized power output will not be achieved by maximizing the capacitance change per unit displacement.

Hoffmann, Daniel; Folkmer, Bernd; Manoli, Yiannos

2010-11-01

405

Quantum-based algorithm for optimizing artificial neural networks.  

PubMed

This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms. PMID:24808566

Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang

2013-08-01

406

A novel Retinex algorithm based on alternating direction optimization  

NASA Astrophysics Data System (ADS)

The goal of the Retinex theory is to removed the effects of illumination from the observed images. To address this typical ill-posed inverse problem, many existing Retinex algorithms obtain an enhanced image by using different assumptions either on the illumination or on the reflectance. One significant limitation of these Retinex algorithms is that if the assumption is false, the result is unsatisfactory. In this paper, we firstly build a Retinex model which includes two variables: the illumination and the reflectance. We propose an efficient and effective algorithm based on alternating direction optimization to solve this problem where FFT (Fast Fourier Transform) is used to speed up the computation. Comparing with most existing Retinex algorithms, the proposed method solve the illumination image and reflectance image without converting images to the logarithmic domain. One of the advantages in this paper is that, unlike other traditional Retinex algorithms, our method can simultaneously estimate the illumination image and the reflectance image, the later of which is the ideal image without the illumination effect. Since our method can directly separate the illumination and the reflectance, and the two variables constrain each other mutually in the computing process, the result is robust to some degree. Another advantage is that our method has less computational cost and can be applied to real-time processing.

Fu, Xueyang; Lin, Qin; Guo, Wei; Huang, Yue; Zeng, Delu; Ding, Xinghao

2013-10-01

407

Microwave-based medical diagnosis using particle swarm optimization algorithm  

NASA Astrophysics Data System (ADS)

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

Modiri, Arezoo

408

A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications.  

PubMed

Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy. PMID:20713084

Raghunathan, Shriram; Gupta, Sumeet K; Markandeya, Himanshu S; Roy, Kaushik; Irazoqui, Pedro P

2010-10-30

409

Efficiency Improvements to the Displacement Based Multilevel Structural Optimization Algorithm  

NASA Technical Reports Server (NTRS)

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

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

2001-01-01

410

An optimized hybrid encode based compression algorithm for hyperspectral image  

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

411

Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization  

NASA Technical Reports Server (NTRS)

Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *

Shaykhian, Gholam Ali; Sen, S. K.

2007-01-01

412

Optimized Algorithms for Prediction Within Robotic Tele-Operative Interfaces  

NASA Technical Reports Server (NTRS)

Robonaut, the humanoid robot developed at the Dexterous Robotics Labo ratory at NASA Johnson Space Center serves as a testbed for human-rob ot collaboration research and development efforts. One of the recent efforts investigates how adjustable autonomy can provide for a safe a nd more effective completion of manipulation-based tasks. A predictiv e algorithm developed in previous work was deployed as part of a soft ware interface that can be used for long-distance tele-operation. In this work, Hidden Markov Models (HMM?s) were trained on data recorded during tele-operation of basic tasks. In this paper we provide the d etails of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmi c approach. We show that all of the algorithms presented can be optim ized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. 1

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

2010-01-01

413

A modified ant colony optimization algorithm based on differential evolution for chaotic synchronization  

Microsoft Academic Search

Optimization algorithms inspired by the ants’ foraging behavior have been initially used for solving combinatorial optimization problems. Since the emergence of ant algorithms as an optimization tool, some attempts were also made to use them for tackling continuous optimization problems. In recent years, the investigation of synchronization and control problem for discrete chaotic systems has attracted much attention, and many

Leandro dos Santos Coelho; Diego Luis de Andrade Bernert

2010-01-01

414

Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna  

Microsoft Academic Search

Genetic algorithms (GA) have proven to be a useful method of optimization for difficult and discontinuous multidimensional engineering problems. A new method of optimization, particle swarm optimization (PSO), is able to accomplish the same goal as GA optimization in a new and faster way. The purpose of this paper is to investigate the foundations and performance of the two algorithms

Jacob Robinson; Seelig Sinton; Yahya Rahmat-Samii

2002-01-01

415

Convex Graph Invariants.  

National Technical Information Service (NTIS)

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

A. S. Willsky P. A. Parrilo V. Chandrasekaran

2010-01-01

416

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade  

NASA Astrophysics Data System (ADS)

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

Huang, Xiaobiao; Safranek, James

2014-09-01

417

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

418

Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow  

NASA Astrophysics Data System (ADS)

A multiobjective genetic algorithm is used to optimize a water conjunctive use problem.Water production, cost, and energy production are optimized simultaneously.Optimized simultaneously are significant nonlinear groundwater and surface water flow.

Peralta, Richard C.; Forghani, Ali; Fayad, Hala

2014-04-01

419

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

NASA Astrophysics Data System (ADS)

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

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

2012-07-01

420

Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization  

SciTech Connect

The authors describe a convergence theory for evolutionary pattern search algorithms (EPSAs) on a broad class of unconstrained and linearly constrained problems. EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. The analysis significantly extends the previous convergence theory for EPSAs. The analysis applies to a broader class of EPSAs,and it applies to problems that are nonsmooth, have unbounded objective functions, and which are linearly constrained. Further, they describe a modest change to the algorithmic framework of EPSAs for which a non-probabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSAs.

HART,WILLIAM E.

2000-06-01

421

An optimal algorithm for computing all subtree repeats in trees  

PubMed Central

Given a labelled tree T, our goal is to group repeating subtrees of T into equivalence classes with respect to their topologies and the node labels. We present an explicit, simple and time-optimal algorithm for solving this problem for unrooted unordered labelled trees and show that the running time of our method is linear with respect to the size of T. By unordered, we mean that the order of the adjacent nodes (children/neighbours) of any node of T is irrelevant. An unrooted tree T does not have a node that is designated as root and can also be referred to as an undirected tree. We show how the presented algorithm can easily be modified to operate on trees that do not satisfy some or any of the aforementioned assumptions on the tree structure; for instance, how it can be applied to rooted, ordered or unlabelled trees.

Flouri, T.; Kobert, K.; Pissis, S. P.; Stamatakis, A.

2014-01-01

422

Robust Optimization Design Algorithm for High-Frequency TWTs  

NASA Technical Reports Server (NTRS)

Traveling-wave tubes (TWTs), such as the Ka-band (26-GHz) model recently developed for the Lunar Reconnaissance Orbiter, are essential as communication amplifiers in spacecraft for virtually all near- and deep-space missions. This innovation is a computational design algorithm that, for the first time, optimizes the efficiency and output power of a TWT while taking into account the effects of dimensional tolerance variations. Because they are primary power consumers and power generation is very expensive in space, much effort has been exerted over the last 30 years to increase the power efficiency of TWTs. However, at frequencies higher than about 60 GHz, efficiencies of TWTs are still quite low. A major reason is that at higher frequencies, dimensional tolerance variations from conventional micromachining techniques become relatively large with respect to the circuit dimensions. When this is the case, conventional design- optimization procedures, which ignore dimensional variations, provide inaccurate designs for which the actual amplifier performance substantially under-performs that of the design. Thus, this new, robust TWT optimization design algorithm was created to take account of and ameliorate the deleterious effects of dimensional variations and to increase efficiency, power, and yield of high-frequency TWTs. This design algorithm can help extend the use of TWTs into the terahertz frequency regime of 300-3000 GHz. Currently, these frequencies are under-utilized because of the lack of efficient amplifiers, thus this regime is known as the "terahertz gap." The development of an efficient terahertz TWT amplifier could enable breakthrough applications in space science molecular spectroscopy, remote sensing, nondestructive testing, high-resolution "through-the-wall" imaging, biomedical imaging, and detection of explosives and toxic biochemical agents.

Wilson, Jeffrey D.; Chevalier, Christine T.

2010-01-01

423

A Novel Memetic Algorithm for Global Optimization Based on PSO and SFLA  

Microsoft Academic Search

Memetic algorithms (MAs) which mimic culture evolution are population based heuristic searching approaches for the optimization\\u000a problems. This paper presents a new memetic algorithm called shuffled particle swarm optimization (SPSO), which combines the\\u000a learning strategy of particle swarm optimization (PSO) and the shuffle strategy of shuffled frog leaping algorithm (SFLA).\\u000a In the proposed algorithm, the population is partitioned into several

Ziyang Zhen; Zhisheng Wang; Zhou Gu; Yuanyuan Liu

2007-01-01

424

Convex Hull in Medical Simulations: A New Hybrid Approach  

Microsoft Academic Search

Nowadays, virtual reality techniques have become widely used in different fields such as medical and architecture. Since a real object does not have a deterministic shape, it is impossible to define a geometric equation to model it. Thus, alternative approaches are the convex hull algorithms to form the convex envelopes of any object and to mimic realistic environment with exact

Fadi Yaacoub; Y. Haman; Antoine Abche; Charbel Fares

2006-01-01

425

Computing Convex Hulls on Beckenbach and Drandell Geometries  

Microsoft Academic Search

We take Beckenbach and Drandell geometries as con- crete models within the framework of complete ordered incidence geometries and show that it is possible to ex- tend Kirkpatrick and Seidel's convex hull algorithm to these two models within essentially the same O(nlogh) time complexity. 1 A historical note and summary Peixoto's seminal work (5) in generalizing the notion of convex

Pedro J. de Rezende

426

Control optimization, stabilization and computer algorithms for aircraft applications  

NASA Technical Reports Server (NTRS)

The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory.

Athans, M. (editor); Willsky, A. S. (editor)

1982-01-01

427

Order from Chaos: A Sampling of Stochastic Optimization Algorithms  

NSDL National Science Digital Library

This teaching module introduces stochastic approaches to finding optimum solutions for adequately defined systems. Because these approaches are intrinsically random, large numbers of random samples are typically required to find robust optima. This results in either long single simulation runs, or the need for multiple replicated simulations considered as an ensemble, or both. Monte Carlo, simulated annealing and genetic algorithm approaches to optimization are introduced in this module and applied to a few example problems, and parallelization strategies and their resulting performance gains are assessed.

Joiner, David

428

Optimality of the Neighbor Joining Algorithm and Faces of the Balanced Minimum Evolution Polytope  

Microsoft Academic Search

Balanced minimum evolution (BME) is a statistically consistent distance-based method to reconstruct a phylogenetic tree from\\u000a an alignment of molecular data. In 2000, Pauplin showed that the BME method is equivalent to optimizing a linear functional\\u000a over the BME polytope, the convex hull of the BME vectors obtained from Pauplin’s formula applied to all binary trees. The\\u000a BME method is

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

2010-01-01

429

Generalized Particle Swarm Algorithm for HCR Gearing Geometry Optimization  

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

430

Optimization of activated sludge designs using genetic algorithms.  

PubMed

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

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

2002-01-01

431

Population Induced Instabilities in Genetic Algorithms for Constrained Optimization  

NASA Astrophysics Data System (ADS)

Evolutionary computation techniques, like genetic algorithms, have received a lot of attention as optimization techniques but, although they exhibit a very promising potential in curing the problem, they have not produced a significant breakthrough in the area of systematic treatment of constraints. There are two mainly ways of handling the constraints: the first is to produce an infeasibility measure and add it to the general cost function (the well known penalty methods) and the other is to modify the mutation and crossover operation in a way that they only produce feasible members. Both methods have their drawbacks and are strongly correlated to the problem that they are applied. In this work, we propose a different treatment of the constraints: we induce instabilities in the evolving population, in a way that infeasible solution cannot survive as they are. Preliminary results are presented in a set of well known from the literature constrained optimization problems.

Vlachos, D. S.; Parousis-Orthodoxou, K. J.

2013-02-01

432

Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling  

NASA Astrophysics Data System (ADS)

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

Abrahart, R. J.

2004-05-01

433

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

434

Design of Optimal Microfluidic Components Using a Genetic Algorithm Search  

NASA Astrophysics Data System (ADS)

Mott et al. [1] describe the automatic design of optimal microfluidic components based on performance criteria. The approach constructs a complex component by adding geometric features, such a grooves of various shapes, to a microchannel. The net transport produced by each of these features in isolation was pre-computed and stored as an ``advection map'' for that feature, and the complex flow through a composite geometry that combines these basic features was calculated rapidly by applying the corresponding maps in sequence. An exhaustive search of feature combinations produced optimized mixer designs of moderate size and complexity. In the current work, a genetic algorithm replaces the exhaustive search of Ref. [1], enabling the optimization of much more complex components with far more degrees of freedom. New metrics for characterizing surface delivery and sample dispersion (i.e., the spreading of a sample plug within the pressure-driven flow) are developed, and the software is applied to design new components that optimize surface delivery and that minimize sample dispersion. [1]. Mott, D.R., Howell, P.B, Golden, J.P., Kaplan, C.R., Ligler, F.S., and Oran, E.S., Lab on a Chip, Vol. 6, No. 4, 2006, pp. 540-549.

Mott, David; Obenschain, Keith; Howell, Peter; Golden, Joel

2007-11-01

435

GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS  

SciTech Connect

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

Rogers, Adam; Fiege, Jason D. [Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3T-2N2 (Canada)

2011-02-01

436

LPS Auto-Calibration Algorithm with Predetermination of Optimal Zones  

PubMed Central

Accurate coordinates for active beacons placed in the environment are required in Local Positioning Systems (LPS). These coordinates and the distances (or differences of distances) measured between the beacons and the mobile node to be localized are inputs to most trilateration algorithms. As a first approximation, such coordinates are obtained by means of manual measurements (a time-consuming and non-flexible method), or by using a calibration algorithm (i.e., automatic determination of beacon coordinates from ad hoc measurements). This paper presents a method to calibrate the beacons’ positions in a LPS using a mobile receiver. The method has been developed for both, spherical and hyperbolic trilateration. The location of only three test points must be known a priori, while the position of the other test points can be unknown. Furthermore, the paper describes a procedure to estimate the optimal positions, or approximate areas in the coverage zone, where the test-points necessary to calibrate the ultrasonic LPS should be placed. Simulation and experimental results show the improvement achieved when these optimal test-points are used instead of randomly selected ones.

Ruiz, Francisco Daniel; Urena, Jesus; Garcia, Juan C.; Jimenez, Ana; Hernandez, Alvaro; Garcia, Juan J.

2011-01-01

437

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

PubMed Central

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

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

2013-01-01

438

An improved chaos optimization algorithm and its application in the economic load dispatch problem  

Microsoft Academic Search

This paper reforms the Mutative Scale Chaos Optimization Algorithm (MSCOA). Numerical simulation demonstrates that the efficiency and performance of the algorithm are improved. An analysis of the Improved Mutative Scale Chaos Optimization Algorithm (IMSCOA) is also given. IMSCOA is applied to examples of economic load dispatch, considering both the valve point effect and transmission loss. The results are compared with

Fang Han; Qi-shao Lu

2008-01-01

439

Optimization design based on improved ant colony algorithm for PID parameters of BP neural network  

Microsoft Academic Search

Aiming at manually carry through optimization of experiment way adopted for traditional PID controller parameter, an optimization method based on improved ant colony algorithm for PID parameters of BP neural network is presented. The improved ant colony algorithm and BP neural is organically combined by this method. Which not only overcomes effectively the shortcoming of BP algorithm on some degree

Yan Zhao; Zhongjun Xiao; Jiayu Kang

2010-01-01

440

Optimal tuning of multi-machine Power System Stabilizer parameters using Genetic-Algorithm  

Microsoft Academic Search

Optimal tuning of Power System Stabilizers (PSSs) parameters using genetic algorithm is presented in this paper. Selecting the parameters of power system stabilizers which simultaneously stabilize system oscillations is converted to a simple optimization problem which is solved by a genetic algorithm. The advantage of Genetic Algorithm (GA) technique for tuning the PSS parameters is that it is independent of

O. Abedinia; M. S. Naderi; A. Jalili; B. Khamenehpour

2010-01-01

441

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

442

The application of genetic algorithm based on matlab in function optimization  

Microsoft Academic Search

This paper introduces the theory of genetic algorithm. The specific operation flow of genetic algorithm is described. The application of genetic algorithm in function optimization has been achieved by the using of matlab programming language. The process of programming shows that it is very easy, flexible and efficient to optimize and compute with matlab language, and the effectiveness of genetic

Guangya Liu; Jingli Chen

2011-01-01

443

Optimization of the Titania Humidity Sensor Equivalent Circuit Model Based on Genetic Algorithms  

Microsoft Academic Search

Taking the parameters of the Titania humidity sensor equivalent circuit model as an optimizing object, this paper proposes an optimization model to improve equivalent circuits based on genetic algorithms. Elitist strategy is added into the selection option of the algorithms, and fitness function is suitably adjusted. Thus, the operating efficiency and accuracy of the algorithms are enhanced, and a fitting

Gang Liu; Xingcheng Wang; Kai Zheng; Ming Yang

2011-01-01

444

Optimal Design of Structures with Discrete Variables Based on Improved Genetic Algorithms  

Microsoft Academic Search

In order to overcome premature phenomenon of simple genetic algorithms and inability to optimize algorithms with complex constraints, an improved genetic algorithms based on some improved methods is presented in this paper and is applied in optimization design of frame structure by adopting adaptive crossover rate and mutation rate, adjusting population size, fitness and penalty function and elitist strategy in

Sun Guofu; Ge Yanhui

2010-01-01

445

Thermodynamic Pareto optimization of turbojet engines using multi-objective genetic algorithms  

Microsoft Academic Search

Multi-objective genetic algorithms (GAs) are used for Pareto approach optimization of thermodynamic cycle of ideal turbojet engines. On this behalf, a new diversity preserving algorithm is proposed to enhance the performance of multi-objective evolutionary algorithms (MOEAs) in optimization problems with more than two objective functions. The important conflicting thermodynamic objectives that have been considered in this work are, namely, specific

K. Atashkari; N. Nariman-Zadeh; A. Pilechia; A. Jamalia

446

Quantum-inspired immune clonal algorithm for global optimization.  

PubMed

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

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

2008-10-01

447

Optimal algorithm for detecting two-dimensional images  

NASA Astrophysics Data System (ADS)

In this paper, we present a new two-dimensional (2D) edge detection algorithm. The algorithm detects edges in 2D images by a curve segment based edge detection functional that uses the zero crossing contours of the Laplacian of Gaussian (LOG) as initial conditions to approach the true edge locations. We prove that the proposed edge detection functional is optimal in terms of signal-to-noise ratio and edge localization accuracy for detecting general 2D edges. In addition, the detected edge candidates preserve the nice scaling behavior that is held uniquely by the LOG zero crossing contours in scale space. The algorithm also provides: (1) an edge regularization procedure that enhances the continuity and smoothness of the detected edges; (2) an adaptive edge thresholding procedure that is based on a robust global noise estimation approach and two physiologically originated criteria to help generate edge detection results similar to those perceived by human visual systems; and (3) a scale space combination procedure that reliably combines edge candidates detected from different scales.

Qian, Richard J.; Huang, Thomas S.

1996-02-01

448

A dynamic convexized method for the TSP  

Microsoft Academic Search

This paper describes a dynamic convexized method for solving the symmetric traveling salesman problem (TSP). We construct an auxiliary function and design an algorithm based on this function. The possibility of sinking into a previous local minimizer can be reduced by adjusting the value of the parameter in the auxiliary function. We have verified the correctness of this approach both

Miaoling Wu; Wenxing Zhu

2010-01-01

449

The CrIMSS EDR Algorithm: Characterization, Optimization, and Validation  

NASA Astrophysics Data System (ADS)

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

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

2014-04-01

450

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

451

Runtime Analysis of an Ant Colony Optimization Algorithm for TSP Instances  

Microsoft Academic Search

Ant colony optimization (ACO) is a relatively new random heuristic approach for solving optimization problems. The main application of the ACO algorithm lies in the field of combinatorial optimization, and the traveling salesman problem (TSP) is the first benchmark problem to which the ACO algorithm has been applied. However, relatively few results on the runtime analysis of the ACO on

Yuren Zhou

2009-01-01

452

The Application of a Differential Evolution Algorithm on Products Structure Optimization in Coal Preparation Plants  

Microsoft Academic Search

The problem of product structure optimization always appears in the coal preparation plants, mines, and mining areas three ranks. The Differential Evolution Algorithm is applied to optimize the product structure in coal preparation plants. A model optimizing the coal product structure and maximizing economic benefit of coal preparation plants is developed, with the use of a Differential Evolution Algorithm toolbox

Ji Li; Kuang Yali; Wang Zhangguo; Du Bo; Li Yunyu; Cao Bing

2012-01-01

453

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

Microsoft Academic Search

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

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

2008-01-01

454

Simulation study of structure optimization by combining fuzzy neural network (FNN) with genetic algorithm (GA)  

Microsoft Academic Search

The method of using genetic algorithm to optimize a fuzzy neural network (FNN) has been introduced in many papers, but most of these articles adopt different methods. This paper identifies the parameters of the controlled plant using a neural network; then it modifies the parameters with the genetic algorithm for optimization. Finally, it modulates and optimizes the membership function and

Yahui Wang; Xuejun Hao; Zhixin Chen; Wen Zhang; Tongtong Liu; Linlin Zhang

2002-01-01

455

HVAC system optimization with CO 2 concentration control using genetic algorithms  

Microsoft Academic Search

This study describes the use of genetic algorithms (GAs) for operating standard HVAC systems (HVAC—heating, ventilation and air conditioning) in order to optimize performance, primarily with regard to power saving. Genetic algorithms were introduced as an instrument for solving optimization problems. Analytic optimization procedures are widely used in other fields of engineering, but they are difficult to operate within HVAC

Velimir Congradac; Filip Kulic

2009-01-01

456

OPTIMIZATION OF PROCESS PLANNING PARAMETERS FOR ROTATIONAL COMPONENTS BY GENETIC ALGORITHMS  

Microsoft Academic Search

In CAPP systems process parameter optimization is one of the key areas for research and development. Traditional techniques have very limited scope because of the complexity of the optimization problem. Due to the rapid development of computer technology Genetic Algorithms (GAs), which are robust search algorithm, have been found to be suitable and efficient tools for optimization in such cases.

Nafis Ahmad; Anwarul Haque

2001-01-01

457

Speed Control Based Particle Swarm Optimizing Clonal Algorithm for AC Induction Motor  

Microsoft Academic Search

An intelligent optimizing algorithm, particle swarm optimizing clonal algorithm (PSOCA) was introduced in this paper, which combined the clonal selection mechanism of the immune system with the evolution equation of particle swarm optimization. It had the ability of global searching. The PSOCA improves the diversity of antibody population and its convergence speed, by using effectively the past information of the

Wang Qiang; Chen Jun; Xiao Jianxiu; Sun Jian

2010-01-01

458

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

459

Difference of convex functions optimization algorithms (DCA) for globally minimizing nonconvex quadratic forms on Euclidean balls and spheres  

Microsoft Academic Search

We present DCA for globally minimizing quadratic forms on Euclidean balls and spheres. Since these problems admit at most one local-nonglobal minimizer, DCA converges in general to a solution for these problems. Numerical simulations show robustness, stability and efficiency of DCA with respect to related standard methods.

Pham Dinh Tao; Le Thi Hoai An

1996-01-01

460

Computational and statistical tradeoffs via convex relaxation  

PubMed Central

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

Chandrasekaran, Venkat; Jordan, Michael I.

2013-01-01

461

Integrated network design and scheduling problems : optimization algorithms and applications.  

SciTech Connect

We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.

Nurre, Sarah G.; Carlson, Jeffrey J.

2014-01-01

462

Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization  

Microsoft Academic Search

The theoretical analysis of evolutionary algorithms is believed to be very important for understanding