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1

A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization

the prob- lem of solving one single-criteria convex-quadratic optimization problem by an interior-point method used for this problem. 1 Introduction Multicriteria optimization problems are a class of difficult The Interior-Point Algorithm 2.1 The Problem Let there be given a primal quadratic optimization problem (PQP

Fliege, JÃ¶rg

2

An Optimal Algorithm for Intersecting Three-Dimensional Convex Polyhedra

This paper describes a linear-time algorithm for computing the intersection of two convex polyhedra in 3-space. Applications of this result to computing intersections, convex hulls, and Voronoi diagrams are also given.

Bernard Chazelle

1992-01-01

3

Optimal Stochastic Approximation Algorithms for Strongly Convex ...

Jul 1, 2010 ... imate solutions 10 ? 40 times faster than an SAA based algorithm while keeping similar solution quality. ...... e,1 are in the same order of magnitude, i.e., O(1/. ?. N ). ...... Inference, and Prediction, Second Edition. Springer ...

2012-06-18

4

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. PMID:22538474

Sidky, Emil Y.; Jørgensen, Jakob H.; Pan, Xiaochuan

2012-01-01

5

Adapted Convex Optimization Algorithm for Wavelet-Based Dynamic PET Reconstruction

1 Adapted Convex Optimization Algorithm for Wavelet-Based Dynamic PET Reconstruction Nelly Abstract--This work deals with Dynamic Positron Emission Tomography (PET) data reconstruction, considering. The effectiveness of this approach is shown with simulated dynamic PET data. Comparative results are also provided

Paris-Sud XI, UniversitÃ© de

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

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

Gálvez, Akemi; Iglesias, Andrés

2013-01-01

8

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

Gálvez, Akemi; Iglesias, Andrés

2013-01-01

9

An algorithm for linearizing convex extremal problems

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

10

Solution to Non-convex Electric Power Dispatch Problem Using Seeker Optimization Algorithm

NASA Astrophysics Data System (ADS)

This paper presents the application of Seeker Optimization Algorithm (SOA) to constrained economic load dispatch problem. Independent simulations were performed over separate systems with different number of generating units having constraints like prohibited operating zones and ramp rate limits. The performance is also compared with other existing similar approaches. The proposed methodology was found to be robust, fast converging and more proficient over other existing techniques.

Krishnanand, K. R.; Rout, P. K.; Panigrahi, B. K.; Mohapatra, Ankita

11

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

12

Design of different types of digital FIR filter is of paramount significance in various Digital Signal Processing (DSP) applications. Different optimization techniques can judiciously be utilized to determine the impulse response coefficients of such a filter. These optimization techniques may include some conventional processes such as Convex or Non-convex optimization methods or some evolutionary algorithms such as Genetic Algorithm (GA),

S. Chattopadhyay; S. K. Sanyal; A. Chandra

2010-01-01

13

Kernel regression for travel time estimation via convex optimization

Kernel regression for travel time estimation via convex optimization Sébastien Blandin , Laurent El Ghaoui and Alexandre Bayen Abstract--We develop an algorithm aimed at estimating travel time on segments of a road network using a convex optimiza- tion framework. Sampled travel time from probe vehicles

14

A nonisolated optimal solution for special reverse convex programming problems

NASA Astrophysics Data System (ADS)

In this paper, an efficient algorithm is proposed for globally solving special reverse convex programming problems with more than one reverse convex constraints. The proposed algorithm provides a nonisolated global optimal solution which is also stable under small perturbations of the constraints, and it turns out that such an optimal solution is adequately guaranteed to be feasible and to be close to the actual optimal solution. Convergence of the algorithm is shown and the numerical experiment is given to illustrate the feasibility of the presented algorithm.

Shen, Pei-Ping; Chen, Yong-Qiang; Ma, Yuan

2009-02-01

15

On Equilibrium Pricing as Convex Optimization Jiawei Zhang

optimization by an interior-point algorithm in polynomial time. 1 Introduction The study of competitive economy problem, and it could be computed by the Ellipsoid method or by efficient interior-point methods of these constraints yield equilibrium prices. Thus, finding a Fisher equilibrium becomes solving a convex optimization

Ye, Yinyu

16

NASA Astrophysics Data System (ADS)

In this paper, a new nonmonotone MBFGS algorithm for unconstrained optimization will be proposed. Under some suitable assumptions, the global and superlinear convergence of the new nonmonotone MBFGS algorithm on convex objective functions will be established. Some numerical experiments show that this new nonmonotone MBFGS algorithm is competitive to the MBFGS algorithm and the nonmonotone BFGS algorithm.

Liu, Liying; Yao, Shengwei; Wei, Zengxin

2008-10-01

17

Sparse recovery via convex optimization

NASA Astrophysics Data System (ADS)

This thesis considers the problem of estimating a sparse signal from a few (possibly noisy) linear measurements. In other words, we have y = Ax + z where A is a measurement matrix with more columns than rows, x is a sparse signal to be estimated, z is a noise vector, and y is a vector of measurements. This setup arises frequently in many problems ranging from MRI imaging to genomics to compressed sensing.We begin by relating our setup to an error correction problem over the reals, where a received encoded message is corrupted by a few arbitrary errors, as well as smaller dense errors. We show that under suitable conditions on the encoding matrix and on the number of arbitrary errors, one is able to accurately recover the message.We next show that we are able to achieve oracle optimality for x, up to a log factor and a factor of sqrt{s}, when we require the matrix A to obey an incoherence property. The incoherence property is novel in that it allows the coherence of A to be as large as O(1/ log n) and still allows sparsities as large as O(m/log n). This is in contrast to other existing results involving coherence where the coherence can only be as large as O(1/sqrt{m}) to allow sparsities as large as O(sqrt{m}). We also do not make the common assumption that the matrix A obeys a restricted eigenvalue condition.We then show that we can recover a (non-sparse) signal from a few linear measurements when the signal has an exactly sparse representation in an overcomplete dictionary. We again only require that the dictionary obey an incoherence property.Finally, we introduce the method of l_1 analysis and show that it is guaranteed to give good recovery of a signal from a few measurements, when the signal can be well represented in a dictionary. We require that the combined measurement/dictionary matrix satisfies a uniform uncertainty principle and we compare our results with the more standard l_1 synthesis approach.All our methods involve solving an l_1 minimization program which can be written as either a linear program or a second-order cone program, and the well-established machinery of convex optimization used to solve it rapidly.

Randall, Paige Alicia

18

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

19

Conditional Gradient Sliding for Convex Optimization

Oct 17, 2014 ... and strongly convex problems, while still maintaining the optimal O(1/?) bound on the ...... One possible remedy to this issue is to incorporate the randomized smoothing ...... Hence, we employed a trial-and-error method to fine tune the .... SIAM Journal on Control and Optimization, 18(5):473487, 1980.

2014-10-17

20

An Efficient Interior-Point Method for Convex Multicriteria Optimization Problems

In multicriteria optimization, several objective functions have to be minimized simultaneously. We propose a new efficient method for approximating the solution set of a multicriteria optimization problem, where the objective functions involved are arbitrary convex functions and the set of feasible points is convex. The method is based on generating warm-start points for an efficient interior-point algorithm, while the approximation

Jörg Fliege

2006-01-01

21

August 2000 (Convex Optimization ) JP Goux The mega title ...

A new class of proximal interior-point methods for optimization under positivity constraints (Convex Optimization ) ..... Roman Polyak Lagrangian ... Stephen Vavasis Convex relaxation for finding planted influential nodes in a social network

22

We extend Clarkson's randomized algorithm for linear programming to a general scheme for solving convex optimization problems. The scheme can be used to speed up existing algorithms on problems which have many more constraints than variables. In particular, we give a randomized algorithm for solving convex quadratic and linear programs, which uses that scheme together with a variant of Karmarkar's

Ilan Adler; Ron Shamir

1993-01-01

23

Efficient parallel coordinate descent algorithm for convex ...

Keywords: Coordinate descent optimization, parallel algorithm, (sub)linear convergence .... vector on these sets and can be done numerically very efficient. For exam- ... quadratic problems are solved with an interior point solver. From (6)-

2012-12-21

24

First-order convex feasibility algorithms for x-ray CT

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

25

First-order convex feasibility algorithms for x-ray CT

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°. 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. PMID:23464295

Sidky, Emil Y.; Jørgensen, Jakob S.; Pan, Xiaochuan

2013-01-01

26

Generalization of Primal-Dual Interior-Point Methods to Convex Optimization Problems in Conic Form

. We generalize primal—dual interior-point methods for linear programming (LP) problems to the convex optimization problems\\u000a in conic form. Previously, the most comprehensive theory of symmetric primal—dual interior-point algorithms was given by Nesterov\\u000a and Todd for feasible regions expressed as the intersection of a symmetric cone with an affine subspace. In our setting, we\\u000a allow an arbitrary convex cone

Levent Tunçel

2001-01-01

27

On Projection Algorithms for Solving Convex Feasibility Problems

Due to their extraordinary utility and broad applicability in many areasof classical mathematics and modern physical sciences (most notably,computerized tomography), algorithms for solving convex feasibilityproblems continue to receive great attention. To unify, generalize, andreview some of these algorithms, a very broad and flexible frameworkis investigated . Several crucial new concepts which allow a systematicdiscussion of questions on behaviour in general

Heinz H. Bauschke; Jonathan M. Borwein

1996-01-01

28

A Predictorcorrector algorithm with multiple corrections for convex quadratic programming

Recently an infeasible interior-point algorithm for linear programming (LP) was presented by Liu and Sun. By using similar\\u000a predictor steps, we give a (feasible) predictor-corrector algorithm for convex quadratic programming (QP). We introduce a\\u000a (scaled) proximity measure and a dynamical forcing factor (centering parameter). The latter is used to force the duality gap\\u000a to decrease. The algorithm can decrease the

Zhongyi Liu; Yue Chen; Wenyu Sun; Zhihui Wei

2012-01-01

29

6.253 Convex Analysis and Optimization, Spring 2010

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

Bertsekas, Dimitri

30

For my project I will investigate the equivalence between convex programming relaxations and message passing algorithms on hard, combinatorial optimization problems. It has been shown that when the LP relaxations of such problems as weighted b-matchings [1], max weight matchings [2], and max-weight independent set [3] [4] are tight, then the max-product

Matt Kraning

2011-01-01

31

Stochastic Convex Optimization Shai Shalev-Shwartz

- ent is strong convexity and regularization. Our results demonstrate that the celebrated theo- rem prediction setting where z = (x, y) is an instance-label pair, W is a subset of a Hilbert space, and f(w; x

Srebro, Nathan

32

Support set expansion sensitivity analysis in convex quadratic optimization

In support set expansion sensitivity analysis, one concerns to find the range of parameter variation where the perturbed problem has an optimal solution with the support set that includes the support set of the given optimal solution of the unperturbed problem. In this article, we consider the perturbed convex quadratic optimization problem and present a method to identify the support

Kamal Mirnia; Alireza Ghaffari Hadigheh

2007-01-01

33

Interior point decoding for linear vector channels based on convex optimization

In the present paper, a novel decoding algorithm for low-density parity-check (LDPC) codes based on convex optimization is presented. The decoding algorithm, which is referred to hereinafter as interior point decoding, is designed for linear vector channels. The linear vector channels include several practically important channels, such as inter-symbol interference channels and partial response (PR) channels. It is shown that

Tadashi Wadayama

2010-01-01

34

Lossless Convexification of a Class of Non-Convex Optimal Control Problems for Linear Systems

Lossless Convexification of a Class of Non-Convex Optimal Control Problems for Linear Systems BehcÂ¸et AcÂ¸ikmesÂ¸e and Lars Blackmore Abstract-- We consider a class of finite time horizon optimal control-convex control constraints. We propose a convex relaxation of the non-convex control constraints, and prove

Williams, Brian C.

35

A convex interior-point method for optimal OFDM PAR reduction

The main disadvantage of OFDM is the high time-domain peak-to-average power ratio (PAR) that limits transmitter power efficiency. This paper presents a convex optimization algorithm for minimizing the PAR of an OFDM signal subject to constraints on the constellation error vector magnitude (EVM). The derivation reduces computational complexity by exploiting known features of the optimization problem, such as OFDM's FFT

Alok Aggarwal; Teresa H. Meng

2005-01-01

36

Convex Optimization, Game Theory, and Variational Inequality Theory

In this article, we have provided a unified view of some basic theoretical foundations and main techniques in convex optimization, game theory, and VI theory. We put special emphasis on the generality of the VI framework, showing how it allows to tackle several interesting problems in nonlinear analysis, classical optimization, and equilibrium programming. In particular, we showed the relevance of

Gesualdo Scutari; Daniel P. Palomar; Francisco Facchinei; Jong-Shi Pang

2010-01-01

37

Large-Scale Convex Optimization via Saddle Point Computation

This article proposes large-scale convex optimization problems to be solved via saddlepoints of the standard Lagrangian. A recent approach for saddle point computation isspecialized, by way of a specific perturbation technique and unique scaling method, toconvex optimization problems with differentiable objective and constraint functions. Ineach iteration the update directions for primal and dual variables are determined by gradientsof the Lagrangian.

Markku Kallio; Charles H. Rosa

1994-01-01

38

Block clustering based on difference of convex functions (DC) programming and DC algorithms.

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

39

A Sequential Convex Semidefinite Programming Algorithm for ...

In free material optimization (FMO), the design variable is the elastic stiffness tensor E which ... (see [12]). This makes the approach unpractical for 3D problems with more than ...... Mathematical Theory of Elastic and Elasto-Plastic. Bodies: An ...

2007-08-11

40

IMPROVED ALGORITHMS FOR CONVEX MINIMIZATION IN ...

fastest of our algorithms produces a solution within relative error O(1/k) of the ..... doing well and manages to decrease the objective value by a constant fraction, the ..... E2 = E?2 = R2m, Q2 is the unit simplex in R2m and A is the 2m × n matrix.

2009-01-18

41

Formulating Cyber-Security as Convex Optimization Problems

to defend, a problem often referred to as cyber situation aware- ness. Situation awareness [3] is a commonFormulating Cyber-Security as Convex Optimization Problems Kyriakos G. Vamvoudakis, Jo~ao P. Mission-centric cyber-security analysts require a complete overview and understanding of the state

Hespanha, JoÃ£o Pedro

42

Formulating Cyber-Security as Convex Optimization Problems

aware- ness. Situation awareness [3] is a common feature of many cyber-security solu- tions but most the biggest threat and prioritize which services to defend, a problem often referred to as cyber situationFormulating Cyber-Security as Convex Optimization ProblemsÃ? Kyriakos G. Vamvoudakis1 , Jo~ao P

Vigna, Giovanni

43

Optimal average cost manufacturing flow controllers: convexity and differentiability

The authors consider the control of a production facility consisting of a single workstation with multiple failure modes and part types using a continuous flow control model. Technical issues concerning the convexity and differentiability of the differential cost function are investigated. It is proven that under an optimal control policy the differential cost is C1 on attractive control switching boundaries

Michael H. Veatch; Michael C. Caramanis

1999-01-01

44

Convex Formulations of Aggregate Network Air Traffic Flow Optimization Problems

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

45

A tutorial on convex optimization II: duality and interior point methods

In recent years, convex optimization has become a computational tool of central importance in engineering, thanks to its ability to solve very large, practical engineering problems reliably and efficiently. The goal of this tutorial is to continue the overview of modern convex optimization from where our ACC2004 Tutorial on Convex Optimization left off, to cover important topics that were omitted

Haitham Hindi

2006-01-01

46

In this paper, we study a class of sample dependent convex optimization problems, and derive a general sequential approximation\\u000a bound for their solutions. This analysis is closely related to the regret bound framework in online learning. However we apply\\u000a it to batch learning algorithms instead of online stochastic gradient decent methods. Applications of this analysis in some\\u000a classification and regression

Tong Zhang

2001-01-01

47

First and second order convex approximation strategies in structural optimization

NASA Technical Reports Server (NTRS)

In this paper, various methods based on convex approximation schemes are discussed that have demonstrated strong potential for efficient solution of structural optimization problems. First, the convex linearization method (Conlin) is briefly described, as well as one of its recent generalizations, the method of moving asymptotes (MMA). Both Conlin and MMA can be interpreted as first-order convex approximation methods that attempt to estimate the curvature of the problem functions on the basis of semiempirical rules. Attention is next directed toward methods that use diagonal second derivatives in order to provide a sound basis for building up high-quality explicit approximations of the behavior constraints. In particular, it is shown how second-order information can be effectively used without demanding a prohibitive computational cost. Various first-order and second-order approaches are compared by applying them to simple problems that have a closed form solution.

Fleury, C.

1989-01-01

48

MODERN CONVEX OPTIMIZATION Aharon Ben-Tal

optimization programs one can solve well ("efficiently solv- able" programs) and when such a possibility is, not well posed!) "what are generic optimization programs we can solve well": #12;3 (!) As far as numerical.isye.gatech.edu/faculty-staff/profile.php?entry=an63 Fall Semester 2013 #12;2 Preface Mathematical Programming deals with optimization programs

Nemirovski, Arkadi

49

Further Development on the Interior Algorithm for Convex Quadratic Programming

Â tion on interior algorithms for quadratic programming (QP) or linear complementarity problem (LCP. This algorithm creates a sequence of primal and dual interior feasible points converging to the optimal solution, which create a sequence of primal and dual interior feasible points converging to the optimal solution

Ye, Yinyu

50

Near-optimal deterministic algorithms for volume computation via M-ellipsoids

We give a deterministic algorithm for computing an M-ellipsoid of a convex body, matching a known lower bound. This leads to a nearly optimal deterministic algorithm for estimating the volume of a convex body and improved deterministic algorithms for fundamental lattice problems under general norms.

Dadush, Daniel; Vempala, Santosh S.

2013-01-01

51

Solving convex programs by random walks

Minimizing a convex function over a convex set in n-dimensional space is a basic, general problem with many interesting special cases. Here, we present a simple new algorithm for convex optimization based on sampling by a random walk. It extends naturally to minimizing quasi-convex functions and to other generalizations.

Dimitris Bertsimas; Santosh Vempala

2004-01-01

52

On the Complexity of Optimization Problems for 3ÂDimensional Convex Polyhedra and Decision Trees@cs.jhu.edu Abstract We show that several wellÂknown optimization problems involving 3Âdimensional convex polyhedra. Key words: Convex polyhedra, approximation, Steinitz's theorem, planar graphs, art gallery theorems

Goodrich, Michael T.

53

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

54

Ordered subsets convex algorithm for 3D terahertz transmission tomography.

We investigate in this paper a new reconstruction method in order to perform 3D Terahertz (THz) tomography using a continuous wave acquisition setup in transmission mode. This method is based on the Maximum Likelihood for TRansmission tomography (ML-TR) first developed for X-ray imaging. We optimize the Ordered Subsets Convex (OSC) implementation of the ML-TR by including the Gaussian propagation model of THz waves and take into account the intensity distributions of both blank calibration scan and dark-field measured on THz detectors. THz ML-TR reconstruction quality and accuracy are discussed and compared to other tomographic reconstructions. PMID:25321798

Recur, B; Balacey, H; Bou Sleiman, J; Perraud, J B; Guillet, J-P; Kingston, A; Mounaix, P

2014-09-22

55

Sparse representations and convex optimization as tools for LOFAR radio interferometric imaging

Compressed sensing theory is slowly making its way to solve more and more astronomical inverse problems. We address here the application of sparse representations, convex optimization and proximal theory to radio interferometric imaging. First, we expose the theory behind interferometric imaging, sparse representations and convex optimization, and second, we illustrate their application with numerical tests with SASIR, an implementation of the FISTA, a Forward-Backward splitting algorithm hosted in a LOFAR imager. Various tests have been conducted in Garsden et al., 2015. The main results are: i) an improved angular resolution (super resolution of a factor ~2) with point sources as compared to CLEAN on the same data, ii) correct photometry measurements on a field of point sources at high dynamic range and iii) the imaging of extended sources with improved fidelity. SASIR provides better reconstructions (five time less residuals) of the extended emissions as compared to CLEAN. With the advent of large radiotel...

Girard, Julien N; Starck, Jean Luc; Corbel, Stéphane; Woiselle, Arnaud; Tasse, Cyril; McKean, John P; Bobin, Jérôme

2015-01-01

56

Studies integrating geometry, probability, and optimization under convexity

Convexity has played a major role in a variety of fields over the past decades. Nevertheless, the convexity assumption continues to reveal new theoretical paradigms and applications. This dissertation explores convexity ...

Nogueira, Alexandre Belloni

2006-01-01

57

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

A Genetic Algorithm for Packing ThreeDimensional NonConvex Objects Having Cavities and Holes with non convex parts having holes and cavities. Parts can be arranged in any orientation and lo cation to an STL file format which is an industrystandard interface between a 3D model and a rapid prototyping

Coello, Carlos A. Coello

58

An algorithm for finding input-output constrained convex sets in an acyclic digraph

An algorithm for finding input-output constrained convex sets in an acyclic digraph G. Gutin, A all input-output constrained convex (IOCC) sets in an acyclic digraph, which uses several novel ideas the worst case and computational experiments. IOCC sets of acyclic digraphs are of interest in the area

Gutin, Gregory

59

An algorithm for solving control constrained optimal control problems

An algorithm, with an approach similar to the Han-Powell method in finite-dimensional optimization, is devised to solve continuous-time optimal control problems where the control variables are constrained. The algorithm is based on a second-order approximation to the change of the cost functional due to a change in the control. Further approximation of that summation produces a simple convex functional. It

Baoming Ma; W. S. Levine

1993-01-01

60

Fireworks Algorithm for Optimization

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

Ying Tan; Yuanchun Zhu

2010-01-01

61

Efficient Market Making via Convex Optimization, and a Connection to Online Learning

12 Efficient Market Making via Convex Optimization, and a Connection to Online Learning JACOB framework, we illustrate the mathematical parallels between cost-function-based markets and online learning. Efficient market making via convex optimization, and a connection to online learning. ACM Trans. Econ. Comp

Chen, Yiling

62

Analysis of Robustness for Convex Optimization Applied to Array Antenna Pattern Synthesis

This study presents an analysis of the convex optimization applied to the synthesis of the radiation pattern for linear antenna arrays. This study emphasizes the application of the convex optimization for the array pattern synthesis considering the simultaneous elimination of several zones interferences, reduction of the level of power in two space zones densely populated by interferences, as well as

Richard Torrealba; David H. Covarrubias; Marco Panduro

2008-01-01

63

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

for the degree of MASTER OF SCIENCE December 1999 Major Subject: Computer Science VERIQUICKHULL: FAST SEQUENTIAL AND PARALLEL ALGORITHMS FOR COMPUTING THE PLANAR CONVEX HULL A Thesis by MASHILAMANI SAMBASIVAM Submitted to Texas ASM University in partial... Major Subject: Computer Science ABSTRACT VeriQuickhull: Fast Sequential and Parallel Algorithms for Computing the Planar Convex Hull. (December 1999) Mashilamani Sambasivam, B. E. , Sri Venkateswara College of Engineering Chair of Advisory...

Sambasivam, Mashilamani

1999-01-01

64

, bevel-tip medical needles, planning curvature-constrained channels in 3D printed implants for targeted vehicles (UAVs). In this work, we present a motion planning technique using sequential convex optimization applications. Our experiments indicate that our approach can compute high-quality plans for medical needle

Abbeel, Pieter

65

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

We present the software library libCreme which we have previously used to successfully calculate convex-roof entanglement measures of mixed quantum states appearing in realistic physical systems. Evaluating the amount of entanglement in such states is in general a non-trivial task requiring to solve a highly non-linear complex optimization problem. The algorithms provided here are able to achieve to do this for a large and important class of entanglement measures. The library is mostly written in the Matlab programming language, but is fully compatible to the free and open-source Octave platform. Some inefficient subroutines are written in C/C++ for better performance. This manuscript discusses the most important theoretical concepts and workings of the algorithms, focussing on the actual implementation and usage within the library. Detailed examples in the end should make it easy for the user to apply libCreme to specific problems.

Beat Röthlisberger; Jörg Lehmann; Daniel Loss

2011-07-22

66

Efficient Market Making via Convex Optimization, and a Connection to Online Learning

X Efficient Market Making via Convex Optimization, and a Connection to Online Learning Jacob illustrate the mathematical paral- lels between cost function based markets and online learning and establish design, securities market, prediction market, automated market maker, convex analysis, online linear

Abernethy, Jake

67

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

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

Balan, Radu V.

68

Level Bundle Methods for Constrained Convex Optimization with ...

May 23, 2013 ... nonsmooth optimization problems whose objective and constraint functions ... The last few years have seen the occurrence of a new generation of ... We compare the proposed approaches with some algorithms ...... See [1] for such a Unit-Commitment decomposition ..... to hydro-thermal unit-commitment.

2013-05-23

69

Ultrafast Quantum Process Tomography via Continuous Measurement and Convex Optimization

NASA Astrophysics Data System (ADS)

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

Baldwin, Charles; Riofrio, Carlos; Deutsch, Ivan

2013-03-01

70

A random polynomial time algorithm for approximating the volume of convex bodies

We present a randomised polynomial time algorithm for approximating the volume of a convex body K in n-dimensional Euclidean space. The proof of correctness of the algorithm relies on recent theory of rapidly mixing Markov chains and isoperimetric inequalities to show that a certain random walk can be used to sample nearly uniformly from within K.

Martin E. Dyer; Alan M. Frieze; Ravi Kannan

1989-01-01

71

Two Algorithms for Determining Volumes of Convex Polyhedra

Determining volumes of convex n-dimensional polyhedra defined by a linear system of inequalities is useful in program analysis Two methods for computing these volumes are proposed (1) summing the volumes of stmphces which form the polyhedron, and (2) summing the volumes of (increasingly smaller) paralleleplpeds which can be fit into the polyhedron Assuming that roundoff errors are small, the first

Jacques Cohen; Timothy J. Hickey

1979-01-01

72

AOAB -- Automated Optimization Algorithm Benchmarking

In this paper we present AOAB, the Automated Optimization Algorithm Benchmarking system. AOAB can be used to automatically conduct experiments with numerical optimization algorithms by applying them to different benchmarks with different parameter settings. Based on the results, AOAB can automatically perform comparisons between different algorithms and settings. It can aid the researcher to identify trends for good parameter settings

Thomas Weise; Li Niu; Ke Tang

2010-01-01

73

A Pivoting Algorithm for Convex Hulls and Vertex Enumeration of Arrangements and Polyhedra

We present a new pivot-based algorithm which can be used with minor modification for the enumeration of the facets of the\\u000a convex hull of a set of points, or for the enumeration of the vertices of an arrangement or of a convex polyhedron, in arbitrary\\u000a dimension. The algorithm has the following properties:\\u000a \\u000a \\u000a (a) \\u000a \\u000a Virtually no additional storage is required beyond

David Avis; Komei Fukuda

1992-01-01

74

A Comparison of Two Fast Algorithms for Computing the Distance between Convex Polyhedra

The problem of tracking the distance betweentwo convex polyhedra is finding applications in many areasof robotics. The algorithm of Lin and Canny is a well-knownfast solution to this problem, but by recasting the algorithmsinto configuration space we show that a minor modificationto the earlier algorithm of Gilbert, Johnson and Keerthi alsogives this algorithm the same expected cost.I. IntroductionMethods for computing

Stephen Cameron

1996-01-01

75

Global optimization algorithm for heat exchanger networks

This paper deals with the global optimization of heat exchanger networks with fixed topology. It is shown that if linear area cost functions are assumed, as well as arithmetic mean driving force temperature differences in networks with isothermal mixing, the corresponding nonlinear programming (NLP) optimization problem involves linear constraints and a sum of linear fractional functions in the objective which are nonconvex. A rigorous algorithm is proposed that is based on a convex NLP underestimator that involves linear and nonlinear estimators for fractional and bilinear terms which provide a tight lower bound to the global optimum. This NLP problem is used within a spatial branch and bound method for which branching rules are given. Basic properties of the proposed method are presented, and its application is illustrated with several example problems. The results show that the proposed method only requires few nodes in the branch and bound search.

Quesada, I.; Grossmann, I.E. (Carnegie Mellon Univ., Pittsburgh, PA (United States))

1993-03-01

76

Scalable 2d Convex Hull and Trianglulation Algorithms for Coarse Grained Multieomputers

Scalable 2d Convex Hull and Trianglulation Algorithms for Coarse Grained Multieomputers Afonso for the coarse rained multicomputer model: p processors with O(gf >> O(1) local memory each, connected to som,e arbitrary interconnection network (e.g. mesh,, hypercube, omega). They require time O(T s e q y n t i a i +T

Rau-Chaplin, Andrew

77

Robust fixed-order Hinfinity controller design for spectral models by convex optimization

A new approach for robust fixed-order H1 con- troller design by convex optimization is proposed. Linear time- invariant single-input single-output systems represented by a finite set of complex values in the frequency domain are consi d- ered. It is shown that the H1 robust performance condition can be approximated by a set of linear or convex constraints with respect to

Alireza Karimi; Gorka Galdos; Roland Longchamp

2008-01-01

78

Non-euclidean restricted memory level method for large-scale convex optimization

. We propose a new subgradient-type method for minimizing extremely large-scale nonsmooth convex functions over simple domains. The characteristic features of the method are (a) the possibility to adjust the scheme to the geometry of the feasible set, thus allowing to get (nearly) dimension-independent (and nearly optimal in the large-scale case) rate-of-convergence results for minimization of a convex Lipschitz continuous function

Aharon Ben-tal; Arkadi Nemirovski

2005-01-01

79

In this article, quadratic programming problems with strict convex objective functions f and linear constraints are considered. Based on a nonlinear separation Theorem, a complete characterization of constraints that are superfluous in an optimal point is given. It allows to derive sufficient conditions for deletion of restrictions. The corresponding conditions can easily be checked if upper bounds on the objective

P. Recht; P. H. Schade

2007-01-01

80

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

NASA Astrophysics Data System (ADS)

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

Röthlisberger, Beat; Lehmann, Jörg; Loss, Daniel

2012-01-01

81

A scalable projective scaling algorithm for l(p) loss with convex penalizations.

This paper presents an accurate, efficient, and scalable algorithm for minimizing a special family of convex functions, which have a lp loss function as an additive component. For this problem, well-known learning algorithms often have well-established results on accuracy and efficiency, but there exists rarely any report on explicit linear scalability with respect to the problem size. The proposed approach starts with developing a second-order learning procedure with iterative descent for general convex penalization functions, and then builds efficient algorithms for a restricted family of functions, which satisfy the Karmarkar's projective scaling condition. Under this condition, a light weight, scalable message passing algorithm (MPA) is further developed by constructing a series of simpler equivalent problems. The proposed MPA is intrinsically scalable because it only involves matrix-vector multiplication and avoids matrix inversion operations. The MPA is proven to be globally convergent for convex formulations; for nonconvex situations, it converges to a stationary point. The accuracy, efficiency, scalability, and applicability of the proposed method are verified through extensive experiments on sparse signal recovery, face image classification, and over-complete dictionary learning problems. PMID:25608289

Zhou, Hongbo; Cheng, Qiang

2015-02-01

82

FIR Filter Design via Spectral Factorization and Convex Optimization

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

Lieven Vandenberghe; Shao-po Wu; Stephen Boyd

1997-01-01

83

Parameterless Hierarchical Bayesian Optimization Algorithm

the parameterless populationsizing scheme can be incorporated into other estimation of distribution algorithms]. That is why the dream of having a fully parameterless optimizer for the entire class of nearly decomposable

Peinke, Joachim

84

Fairness in optimal routing algorithms

FAIRNESS IN OPTIMAL ROUTING ALGORITHMS A Thesis by JEFFREY ALAN GOOS Submitted to the Office of Graduate Studies of Texas AkM University in partial fulfiHment of the requirements for the degree of MASTER OF SCIENCE December 1988 Major... Subject: Electrical Engineering FAIRNESS IN OPTIMAL ROUTING ALGORITHMS A Thesis by JEFFREY ALAN GOOS Approved as to style and content by: Wei K. Tsai (Co-Chairman of Committee) C Pierce E. Cantrell (Co-Chairman of Committee) Jer D. Gibson...

Goos, Jeffrey Alan

1988-01-01

85

Ant Algorithms for Discrete Optimization

Ant Algorithms for Discrete Optimization Marco Dorigo Gianni Di Caro IRIDIA CP 194/6 Universit@iridia.ulb.ac.be Luca M. Gambardella IDSIA Corso Elvezia 36 CH-6900 Lugano Switzerland luca@idsia.ch Keywords ant algorithms, ant colony optimiza- tion, swarm intelligence, metaheuris- tics, natural computation Abstract

Hutter, Frank

86

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

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

2014-11-01

87

Par reduction in OFDM through convex programming

The high peak-to-average power ratio (PAR) encountered in OFDM system has been a major obstacle in the implementation of power efficient transmitter. In this paper, we present a new active constellation extension (ACE) based convex optimization algorithm which reduces PAR through convex programming. In comparison with previous convex programming method, our method greatly reduces the complexity and keeps the bit-error-rate

Chao Wang; Shu Hung Leung

2008-01-01

88

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

NASA Astrophysics Data System (ADS)

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

Adhikari, Sam

2007-11-01

89

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

90

A Primal-Dual Decomposition Algorithm for Multistage Stochastic Convex Programming

This paper presents a new and high performance solution method for multistage stochastic convex programming. Stochastic programming is a quantitative tool developed in the field of optimization to cope with the problem of decision-making under uncertainty. Among others, stochastic programming has found many applications in finance, such as asset-liability and bond-portfolio management. However, many stochastic programming applications still remain computationally

Arjan Berkelaar; Joaquim A. S. Gromicho; Roy Kouwenberg; Shuzhong Zhang

2005-01-01

91

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

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

Sapatnekar, Sachin

92

A Study of Near-Field Direct Antenna Modulation Systems Using Convex Optimization

) systems. The modulation is carried out in a NFDAM system by means of a control unit that switches amongA Study of Near-Field Direct Antenna Modulation Systems Using Convex Optimization Javad Lavaei de- sign for a class of communication systems known as near-field direct antenna modulation (NFDAM

Hajimiri, Ali

93

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

's method, the formal Newton method began to evolve from Isaac Newton (1669) for finding roots10-725: Convex Optimization Fall 2013 Lecture 9: Newton Method Lecturer: Barnabas Poczos.1 Motivation Newton method is originally developed for finding a root of a function. It is also known as Newton

Tibshirani, Ryan

94

Convex optimization approach to the fusion of identity information

NASA Astrophysics Data System (ADS)

We consider the problem of identity fusion for a multi- sensor target tracking system whereby sensors generate reports on the target identities. Since the sensor reports are typically fuzzy, 'incomplete' and inconsistent, the fusion approach based on the minimization of inconsistencies between the sensor reports by using a convex Quadratic Programming (QP) and linear programming (LP) formulation. In contrast to the Dempster-Shafer's evidential reasoning approach which suffers from exponentially growing completely, our approach is highly efficient. Moreover, our approach is capable of fusing 'ratio type' sensor reports, thus it is more general than the evidential reasoning theory. When the sensor reports are consistent, the solution generated by the new fusion method can be shown to converge to the true probability distribution. Simulation work shows that our method generates reasonable fusion results, and when only 'Subset type' sensor reports are presented, it produces fusion results similar to that obtained via the evidential reasoning theory.

Li, Lingjie; Luo, Zhi-Quan; Wong, Kon M.; Bosse, Eloi

1999-03-01

95

Coordination and Control of Multiple Spacecraft using Convex Optimization Techniques

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

96

Constrained Multiobjective Biogeography Optimization Algorithm

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

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

2014-01-01

97

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders. PMID:25462697

Daducci, Alessandro; Canales-Rodríguez, Erick J; Zhang, Hui; Dyrby, Tim B; Alexander, Daniel C; Thiran, Jean-Philippe

2015-01-15

98

NASA Astrophysics Data System (ADS)

Radiation therapy is an important modality in treating various cancers. Various treatment planning and delivery technologies have emerged to support intensity modulated radiation therapy (IMRT), creating significant opportunities to advance this type of treatment. However, one of the fundamental questions in treatment planning and optimization, 'can we produce better treatment plans relying on the existing delivery technology?' still remains unanswered, in large part due to the underlying computational complexity of the problem, which, in turn, often stems from the optimization model being non-convex. We investigate the possibility of including the dose prescription, specified by the dose-volume histogram (DVH), within the convex optimization framework for inverse radiotherapy treatment planning. Specifically, we study the quality of approximating a given DVH with a superset of generalized equivalent uniform dose (gEUD)-based constraints, the so-called generalized moment constraints (GMCs). As a bi-product, we establish an analytic relationship between a DVH and a sequence of gEUD values. The newly proposed approach is promising as demonstrated by the computational study where the rectum DVH is considered. Unlike the precise partial-volume constraints formulation, which is commonly based on the mixed-integer model and necessitates the use of expensive computing resources to be solved to global optimality, our convex optimization approach is expected to be feasible for implementation on a conventional treatment planning station.

Zinchenko, Y.; Craig, T.; Keller, H.; Terlaky, T.; Sharpe, M.

2008-06-01

99

The Nelson--Oppen Algorithm (Deterministic Version for Convex Theories) Unsat: F 1 , F 2 # if ##x F models, then the deterministic Nelson--Oppen algorithm is terminating, sound and complete for deciding Nelson--Oppen algorithm for convex theories requires at most O(n 3 ) calls to the individual decision

Waldmann, Uwe

100

Alternating direction methods for non convex optimization with ...

Feb 11, 2015 ... motivation arises from applications in risk parity portfolio selection. ...... Algorithm (ISTA and FISTA) by Beck and Teboulle [4], Alternating Linearization ...... Transactions of the American mathematical Society, 82(2):421–.

Xi Bai

2015-02-11

101

Tensor completion and low-n-rank tensor recovery via convex optimization

NASA Astrophysics Data System (ADS)

In this paper we consider sparsity on a tensor level, as given by the n-rank of a tensor. In an important sparse-vector approximation problem (compressed sensing) and the low-rank matrix recovery problem, using a convex relaxation technique proved to be a valuable solution strategy. Here, we will adapt these techniques to the tensor setting. We use the n-rank of a tensor as a sparsity measure and consider the low-n-rank tensor recovery problem, i.e. the problem of finding the tensor of the lowest n-rank that fulfills some linear constraints. We introduce a tractable convex relaxation of the n-rank and propose efficient algorithms to solve the low-n-rank tensor recovery problem numerically. The algorithms are based on the Douglas-Rachford splitting technique and its dual variant, the alternating direction method of multipliers.

Gandy, Silvia; Recht, Benjamin; Yamada, Isao

2011-02-01

102

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

103

Genetic algorithm optimization of entanglement

We present an application of a genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach in which the hopping of electrons is much stronger than the phonon dissipation

Jorge C. Navarro-Munoz; H. C. Rosu; R. Lopez-Sandoval

2006-11-13

104

1 An Improved Algorithm and Analysis for Finding the Simpson Point of a Convex Polygon Abstract. We consider the problem of finding the Simpson Point, also known as the (1|1) Centroid algorithm might be modified to find the Simpson Point when the demand distribution is non-uniform. 1

Tovey, Craig A.

105

6.253 Convex Analysis and Optimization, Spring 2004

6.253 develops the core analytical issues of continuous optimization, duality, and saddle point theory, using a handful of unifying principles that can be easily visualized and readily understood. The mathematical theory ...

Bertsekas, Dimitri

106

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

107

Parallel Selective Algorithms for Nonconvex Big Data Optimization

NASA Astrophysics Data System (ADS)

We propose a decomposition framework for the parallel optimization of the sum of a differentiable (possibly nonconvex) function and a (block) separable nonsmooth, convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. Our framework is very flexible and includes both fully parallel Jacobi schemes and Gauss- Seidel (i.e., sequential) ones, as well as virtually all possibilities "in between" with only a subset of variables updated at each iteration. Our theoretical convergence results improve on existing ones, and numerical results on LASSO, logistic regression, and some nonconvex quadratic problems show that the new method consistently outperforms existing algorithms.

Facchinei, Francisco; Scutari, Gesualdo; Sagratella, Simone

2015-04-01

108

Applications of convex optimization in signal processing and digital communication

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

Zhi-Quan Luo

2003-01-01

109

On the optimality of the neighbor-joining algorithm

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. PMID:18447942

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

2008-01-01

110

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

111

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

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

2002-01-01

112

Nonnegative Mixed-Norm Convex Optimization for Mitotic Cell Detection in Phase Contrast Microscopy

This paper proposes a nonnegative mix-norm convex optimization method for mitotic cell detection. First, we apply an imaging model-based microscopy image segmentation method that exploits phase contrast optics to extract mitotic candidates in the input images. Then, a convex objective function regularized by mix-norm with nonnegative constraint is proposed to induce sparsity and consistence for discriminative representation of deformable objects in a sparse representation scheme. At last, a Support Vector Machine classifier is utilized for mitotic cell modeling and detection. This method can overcome the difficulty in feature formulation for deformable objects and is independent of tracking or temporal inference model. The comparison experiments demonstrate that the proposed method can produce competing results with the state-of-the-art methods. PMID:24348733

Hao, Tong; Gao, Zan; Su, Yuting; Yang, Zhaoxuan

2013-01-01

113

We propose a general framework for determining optimal relationships for tensile strength of doubly convex tablets under diametrical compression. This approach is based on the observation that tensile strength is directly proportional to the breaking force and inversely proportional to a non-linear function of geometric parameters and materials properties. This generalization reduces to the analytical expression commonly used for flat faced tablets, i.e., Hertz solution, and to the empirical relationship currently used in the pharmaceutical industry for convex-faced tablets, i.e., Pitt's equation. Under proper parametrization, optimal tensile strength relationship can be determined from experimental results by minimizing a figure of merit of choice. This optimization is performed under the first-order approximation that a flat faced tablet and a doubly curved tablet have the same tensile strength if they have the same relative density and are made of the same powder, under equivalent manufacturing conditions. Furthermore, we provide a set of recommendations and best practices for assessing the performance of optimal tensile strength relationships in general. Based on these guidelines, we identify two new models, namely the general and mechanistic models, which are effective and predictive alternatives to the tensile strength relationship currently used in the pharmaceutical industry. PMID:25683146

Razavi, Sonia M; Gonzalez, Marcial; Cuitiño, Alberto M

2015-04-30

114

Evolutionary optimization algorithm by entropic sampling

NASA Astrophysics Data System (ADS)

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

Lee, Chang-Yong; Han, Seung Kee

1998-03-01

115

CONVEX OPTIMIZATION & EUCLIDEAN DISTANCE GEOMETRY DATTORRO M #12;Dattorro CONVEX OPTIMIZATION & EUCLIDEAN DISTANCE GEOMETRY Meboo #12;Convex Optimization & Euclidean Distance Geometry Jon Dattorro Moo & Euclidean Distance Geometry, Moo, 2005, v2014.04.08. ISBN 0976401304 (English) ISBN 9780615193687

Stanford University

116

An efficient algorithm for function optimization: modified stem cells algorithm

NASA Astrophysics Data System (ADS)

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

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

2013-03-01

117

Global optimization algorithms for a CAD workstation

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

118

Composite splitting algorithms for convex optimization Junzhou Huang a,

reserved. 1. Introduction The composite prior models have been used in many fields, including sparse for the compressive Magnetic Resonance (MR) imaging [1Â3] and has been widely used in recovering MR images,. . .,m are orthogonal matrices. If the function f and {gi}i=1,. . .,m are well-structured, there are two

119

Evolutionary programming based optimal power flow algorithm

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

Jason Yuryevich; Kit Po Wong

1999-01-01

120

NASA Technical Reports Server (NTRS)

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

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

1991-01-01

121

Optimization Online Digest -- February 2011

A stabilized model and an efficient solution method for the yearly optimal ... Interior-Point Algorithms for a Generalization of Linear Programming and ... A Dual Algorithm For Approximating Pareto Sets in Convex Multi-Criteria Optimization

122

A fast optimization algorithm for multicriteria intensity modulated proton therapy planning

Purpose: To describe a fast projection algorithm for optimizing intensity modulated proton therapy (IMPT) plans and to describe and demonstrate the use of this algorithm in multicriteria IMPT planning. Methods: The authors develop a projection-based solver for a class of convex optimization problems and apply it to IMPT treatment planning. The speed of the solver permits its use in multicriteria optimization, where several optimizations are performed which span the space of possible treatment plans. The authors describe a plan database generation procedure which is customized to the requirements of the solver. The optimality precision of the solver can be specified by the user. Results: The authors apply the algorithm to three clinical cases: A pancreas case, an esophagus case, and a tumor along the rib cage case. Detailed analysis of the pancreas case shows that the algorithm is orders of magnitude faster than industry-standard general purpose algorithms (MOSEK’s interior point optimizer, primal simplex optimizer, and dual simplex optimizer). Additionally, the projection solver has almost no memory overhead. Conclusions: The speed and guaranteed accuracy of the algorithm make it suitable for use in multicriteria treatment planning, which requires the computation of several diverse treatment plans. Additionally, given the low memory overhead of the algorithm, the method can be extended to include multiple geometric instances and proton range possibilities, for robust optimization. PMID:20964213

Chen, Wei; Craft, David; Madden, Thomas M.; Zhang, Kewu; Kooy, Hanne M.; Herman, Gabor T.

2010-01-01

123

A fast optimization algorithm for multicriteria intensity modulated proton therapy planning

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

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

2010-09-15

124

SOME RECENT DEVELOPMENTS IN NONLINEAR OPTIMIZATION ALGORITHMS

This article provides a condensed overview of some of the major today's features (both classical or recently developed), used in the design and development of algorithms to solve nonlinear continuous optimization problems. We rst consider the unconstrained optimization case to introduce the line-search and trust-region approaches as globalization techniques to force an algorithm to converge from any starting point. We

A. Sartenaer

2003-01-01

125

Solving combinatorial optimization problems using Karmarkar's algorithm

We describe a cutting plane algorithm for solving combinatorial optimization problems. The primal projective standard-form variant of Karmarkar's algorithm for linear programming is applied to the duals of a sequence of linear programming relaxations of the combinatorial optimization problem.

John E. Mitchell; Michael J. Todd

1992-01-01

126

A three-dimensional (3-D) ultrasound (US) system has been developed to monitor the intracranial ventricular system of preterm neonates with intraventricular hemorrhage (IVH) and the resultant dilation of the ventricles (ventriculomegaly). To measure ventricular volume from 3-D US images, a semi-automatic convex optimization-based approach is proposed for segmentation of the cerebral ventricular system in preterm neonates with IVH from 3-D US images. The proposed semi-automatic segmentation method makes use of the convex optimization technique supervised by user-initialized information. Experiments using 58 patient 3-D US images reveal that our proposed approach yielded a mean Dice similarity coefficient of 78.2% compared with the surfaces that were manually contoured, suggesting good agreement between these two segmentations. Additional metrics, the mean absolute distance of 0.65 mm and the maximum absolute distance of 3.2 mm, indicated small distance errors for a voxel spacing of 0.22 × 0.22 × 0.22 mm(3). The Pearson correlation coefficient (r = 0.97, p < 0.001) indicated a significant correlation of algorithm-generated ventricular system volume (VSV) with the manually generated VSV. The calculated minimal detectable difference in ventricular volume change indicated that the proposed segmentation approach with 3-D US images is capable of detecting a VSV difference of 6.5 cm(3) with 95% confidence, suggesting that this approach might be used for monitoring IVH patients' ventricular changes using 3-D US imaging. The mean segmentation times of the graphics processing unit (GPU)- and central processing unit-implemented algorithms were 50 ± 2 and 205 ± 5 s for one 3-D US image, respectively, in addition to 120 ± 10 s for initialization, less than the approximately 35 min required by manual segmentation. In addition, repeatability experiments indicated that the intra-observer variability ranges from 6.5% to 7.5%, and the inter-observer variability is 8.5% in terms of the coefficient of variation of the Dice similarity coefficient. The intra-class correlation coefficient for ventricular system volume measurements for each independent observer ranged from 0.988 to 0.996 and was 0.945 for three different observers. The coefficient of variation and intra-class correlation coefficient revealed that the intra- and inter-observer variability of the proposed approach introduced by the user initialization was small, indicating good reproducibility, independent of different users. PMID:25542486

Qiu, Wu; Yuan, Jing; Kishimoto, Jessica; McLeod, Jonathan; Chen, Yimin; de Ribaupierre, Sandrine; Fenster, Aaron

2015-02-01

127

Intelligent perturbation algorithms for space scheduling optimization

NASA Technical Reports Server (NTRS)

Intelligent perturbation algorithms for space scheduling optimization are presented in the form of the viewgraphs. The following subject areas are covered: optimization of planning, scheduling, and manifesting; searching a discrete configuration space; heuristic algorithms used for optimization; use of heuristic methods on a sample scheduling problem; intelligent perturbation algorithms are iterative refinement techniques; properties of a good iterative search operator; dispatching examples of intelligent perturbation algorithm and perturbation operator attributes; scheduling implementations using intelligent perturbation algorithms; major advances in scheduling capabilities; the prototype ISF (industrial Space Facility) experiment scheduler; optimized schedule (max revenue); multi-variable optimization; Space Station design reference mission scheduling; ISF-TDRSS command scheduling demonstration; and example task - communications check.

Kurtzman, Clifford R.

1991-01-01

128

Intelligent perturbation algorithms to space scheduling optimization

NASA Technical Reports Server (NTRS)

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

Kurtzman, Clifford R.

1991-01-01

129

Dikin-type algorithms for dextrous grasping force optimization

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

130

An Algorithmic Framework for Multiobjective Optimization

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

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

2013-01-01

131

An algorithmic framework for multiobjective optimization.

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

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

2013-01-01

132

Coordination and control of distributed spacecraft systems using convex optimization techniques

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

133

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

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

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

2014-01-01

134

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

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

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

2014-01-01

135

Adaptable optimization : theory and algorithms

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

Caramanis, Constantine (Constantine Michael), 1977-

2006-01-01

136

Adaptive Cuckoo Search Algorithm for Unconstrained Optimization

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

2014-01-01

137

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT segmentation based on 15 patients.

Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States)] [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Chen, Ken Chung [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China)] [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China); Shen, Steve G. F.; Yan, Jin [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China)] [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Lee, Philip K. M.; Chow, Ben [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China)] [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China); Liu, Nancy X. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China)] [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China); Xia, James J. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States) [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)] [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)

2014-04-15

138

Optimal mesh algorithms for VLSI routing

Optimal mesh algorithms are developed for several VLSI routing problems, such as river routing between rectangles, routing within a rectilinear polygon, and wiring module pins to frame pads. It is assumed that the mesh consists of ?n×?n processors, where n is the input size. Each processor has a constant amount of memory. All the algorithms run in time O(?n ).

Shing-Chong Chang; J. JaJa

1988-01-01

139

APPLYING NEW OPTIMIZATION ALGORITHMS TO MODEL PREDICTIVE

, developments in optimization, and esÂ pecially in interiorÂpoint methods, have produced a new set of algorithms the interiorÂpoint algoÂ rithm and show how it can be applied efficiently to the problem (1). We then move new algorithms for quadratic programming can be applied efficiently to this problem. The approach

Wright, Steve

140

Ant Algorithms Solve Difficult Optimization Problems

Ant Algorithms Solve Difficult Optimization Problems Marco Dorigo IRIDIA Universit´e Libre de Bruxelles 50 Avenue F. Roosevelt B-1050 Brussels, Belgium mdorigo@ulb.ac.be Abstract. The ant algorithms research field builds on the idea that the study of the behavior of ant colonies or other social insects

Libre de Bruxelles, Université

141

Finding Tradeoffs by Using Multiobjective Optimization Algorithms

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

142

Hybrid Evolutionary Multi-Objective Optimization Algorithms

-objective genetic local search (MOGLS) algorithm, which is the hybridization of a simple EMO algorithm with local search for multi- objective optimization was first implemented in [6], [7] as a multi-objective genetic by modifying the selection mechanism for choosing parent solutions for crossover in its EMO part

Coello, Carlos A. Coello

143

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

144

A parallel gradient distribution algorithm for large-scale optimization

We present a Parallel Gradient Distribution method for the solution of the unconstrained optimization problem min f(x), x {element_of} R{sup n}, where f : R{sup n} {yields} R has continuous first and second partial derivatives and n is typically very large (order of thousands). Given p processors of a parallel computing system, the proposed algorithm is characterized by a parallel phase which produces p points, exploiting the portions of the gradient of the objective function assigned to each processor. Then a coordination phase follows, which determines a new iterate solving a minimization problem in a p + 1 dimensional space, on the basis of the previous iterate and the p points generated by the parallel phase. The parallel and coordination phases are implemented using a limited memory BFGS approach for determining the search direction, and a line search procedure based on the Wolfe sufficient decrease conditions. Global and superlinear convergence results are established in the case of uniformly convex problems. The proposed parallel algorithm is compared, in terms of numerical performance, with the partitioned Quasi-Newton method of Griewank and Toint, the Block Truncated Newton method of Nash and Sofer and the Conjugate Gradient method of Shanno and Phua, using a set of large scale structured optimization problems. Furthermore, the influence of the number of processors available by extensive computational experiments carried out on distributed memory systems (NCUBE and FUJITSU) and on a network of workstations (DEC Alpha) by using PVM.

Conforti, M.; Musmanno, R.

1994-12-31

145

: A New Support Vector Method for Optimizing Partial AUC Based on a Tight Convex Upper Bound

previous method, the result- ing optimization problem can be solved using a cutting-plane algorithm, Cutting-Plane Method, ROC Curve Permission to make digital or hard copies of all or part of this work plays an important role as an evaluation tool in machine learning and data mining. In particular

Agarwal, Shivani

146

Distributed evolutionary algorithms for simulation optimization

The optimization of such complex systems as manufacturing systems often necessitates the use of simulation. In this paper, the use of evolutionary algorithms is suggested for the optimization of simulation models. Several types of variables are taken into account. The reduction of computing cost is achieved through the parallelization of this method, which allows several simulation experiments to be run

Henri Pierreval; Jean-luc Paris

2000-01-01

147

Randomized Algorithm for UAVs Group Flight Optimization

Randomized Algorithm for UAVs Group Flight Optimization Konstantin Amelin Natalia Amelina , Oleg: The problem of small UAVs flight optimization is considered. To solve this problem thermal updrafts are used. For the precise detection of the thermal updrafts center the simultaneous perturbation stochastic approximation

Granichin, Oleg

148

Algorithms for optimal dyadic decision trees

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

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

2009-01-01

149

We introduce a notion of k -convexity and explore polygons in the plane that have this property. Polygons which are k -convex can be triangulated with fast yet simple algorithms. However, recognizing them in general is ...

Aichholzer, Oswin

150

Two stochastic optimization algorithms applied to nuclear reactor core design

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

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

2006-01-01

151

An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.

Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; therebymaking our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a wellconverged and well-diversified approximation of the Pareto front. PMID:25373137

Bechikh, Slim; Chaabani, Abir; Said, Lamjed Ben

2014-10-30

152

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

153

Optimal parallel quantum query algorithms

We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that decoherence times of qubits are typically small, so it makes sense to parallelize quantum algorithms as much as possible. We show tight bounds for a number of problems, specifically Theta((n/p)^{2/3}) p-parallel queries for element distinctness and Theta((n/p)^{k/(k+1)} for k-sum. Our upper bounds are obtained by parallelized quantum walk algorithms, and our lower bounds are based on a relatively small modification of the adversary lower bound method, combined with recent results of Belovs et al. on learning graphs. We also prove some general bounds, in particular that quantum and classical p-parallel complexity are polynomially related for all total functions f when p is small compared to f's block sensitivity.

Stacey Jeffery; Frederic Magniez; Ronald de Wolf

2015-02-20

154

Protein structure optimization with a "Lamarckian" ant colony algorithm.

We describe the LamarckiAnt algorithm: a search algorithm that combines the features of a "Lamarckian" genetic algorithm and ant colony optimization. We have implemented this algorithm for the optimization of BLN model proteins, which have frustrated energy landscapes and represent a challenge for global optimization algorithms. We demonstrate that LamarckiAnt performs competitively with other state-of-the-art optimization algorithms. PMID:24407312

Oakley, Mark T; Richardson, E Grace; Carr, Harriet; Johnston, Roy L

2013-01-01

155

A Cuckoo Search Algorithm for Multimodal Optimization

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

2014-01-01

156

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

NASA Astrophysics Data System (ADS)

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

Handa, Masaya; Kawanishi, Michihiro; Kanki, Hiroshi

2007-12-01

157

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.

158

Optimal algorithms for approximate clustering

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

Tomás Feder; Daniel H. Greene

1988-01-01

159

An O( n 3 L ) primal interior point algorithm for convex quadratic programming

We present a primal interior point method for convex quadratic programming which is based upon a logarithmic barrier function approach. This approach generates a sequence of problems, each of which is approximately solved by taking a single Newton step. It is shown that the method requires\\u000a

D. Goldfarb; S. Liu

1990-01-01

160

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

161

NASA Astrophysics Data System (ADS)

In this paper we propose a fundamentally different conjugate gradient method, in which the well-known parameter [beta]k is computed by an approximation of the Hessian/vector product through finite differences. For search direction computation, the method uses a forward difference approximation to the Hessian/vector product in combination with a careful choice of the finite difference interval. For the step length computation we suggest an acceleration scheme able to improve the efficiency of the algorithm. Under common assumptions, the method is proved to be globally convergent. It is shown that for uniformly convex functions the convergence of the accelerated algorithm is still linear, but the reduction in function values is significantly improved. Numerical comparisons with conjugate gradient algorithms including CONMIN by Shanno and Phua [D.F. Shanno, K.H. Phua, Algorithm 500, minimization of unconstrained multivariate functions, ACM Trans. Math. Softw. 2 (1976) 87-94], SCALCG by Andrei [N. Andrei, Scaled conjugate gradient algorithms for unconstrained optimization, Comput. Optim. Appl. 38 (2007) 401-416; N. Andrei, Scaled memoryless BFGS preconditioned conjugate gradient algorithm for unconstrained optimization, Optim. Methods Softw. 22 (2007) 561-571; N. Andrei, A scaled BFGS preconditioned conjugate gradient algorithm for unconstrained optimization, Appl. Math. Lett. 20 (2007) 645-650], and new conjugacy condition and related new conjugate gradient by Li, Tang and Wei [G. Li, C. Tang, Z. Wei, New conjugacy condition and related new conjugate gradient methods for unconstrained optimization, J. Comput. Appl. Math. 202 (2007) 523-539] or truncated Newton TN by Nash [S.G. Nash, Preconditioning of truncated-Newton methods, SIAM J. on Scientific and Statistical Computing 6 (1985) 599-616] using a set of 750 unconstrained optimization test problems show that the suggested algorithm outperforms these conjugate gradient algorithms as well as TN.

Andrei, Neculai

2009-08-01

162

An algorithm for optimal water resources planning

AN ALGORITEM FOR OPTIMAL WATER RESOURCES PLANNING A Thesis By INDI1iKRI V. S. RAJU Submitted to the Graduate College of the Texas ASM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE January 1988... Major Subject: Industrial Engineering AN ALGORITHM FOR OPTIMAL WATER RESOURCES PLANNING A Thesis By INDUKURI V. S. RAJU Approved as to style and content by: (Chairman of Committee) Head of Department) (Member) (Member) January 1968...

Raju, Indukuri Venkata Satyanarayana

1968-01-01

163

Algorithm Optimally Allocates Actuation of a Spacecraft

NASA Technical Reports Server (NTRS)

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

Motaghedi, Shi

2007-01-01

164

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

165

Wind Mill Pattern Optimization using Evolutionary Algorithms

Wind Mill Pattern Optimization using Evolutionary Algorithms Charlie Vanaret ENAC , IRIT 7 av Ed 31062 Toulouse Cedex 9, France jean-marc.alliot@irit.fr ABSTRACT When designing a wind farm layout, we a grid, we can gain up to 3% of energy output on simple exam- ples of wind farms dealing with many

166

Parallel Simulated Annealing Algorithms in Global Optimization

Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel Simulated Annealing (SA) algorithms and compare them on an extensive test bed used previously for the assessment of various solution approaches

Esin Onba?o?lu; Linet Özdamar

2001-01-01

167

A Derivative Free Optimization Algorithm in Practice

). In this paper we discuss some particular applicaÂ tions from Boeing. One arises in helicopter rotor blade design In the paper we are considering a general derivaÂ tive free optimization (DFO) algorithm for minimizÂ ing one needs to minimize an objective measured by some experiment or by a complicated simulation package

Toint, Philippe

168

NASA Astrophysics Data System (ADS)

An algorithm based on improved real quantum evolutionary algorithm (IRQEA) was developed to solve the problem of highly non-linear economic load dispatch problem with valve point loading. The performance of the proposed algorithm is evaluated on a test case of 15 units. The performance of the algorithm is compared with floating point genetic algorithm (FPGA) and real quantum evolutionary algorithm (RQEA). Results demonstrate that the performance of the IRQEA algorithm is far better than FPGA and RQEA algorithms in terms of convergence rate and solution quality.

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

169

Algorithm for fixed-range optimal trajectories

NASA Technical Reports Server (NTRS)

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

Lee, H. Q.; Erzberger, H.

1980-01-01

170

Convergence Rates of Efficient Global Optimization Algorithms

Efficient global optimization is the problem of minimizing an unknown function f, using as few evaluations f(x) as possible. It can be considered as a continuum-armed bandit problem, with noiseless data and simple regret. Expected improvement is perhaps the most popular method for solving this problem; the algorithm performs well in experiments, but little is known about its theoretical properties. Implementing expected improvement requires a choice of Gaussian process prior, which determines an associated space of functions, its reproducing-kernel Hilbert space (RKHS). When the prior is fixed, expected improvement is known to converge on the minimum of any function in the RKHS. We begin by providing convergence rates for this procedure. The rates are optimal for functions of low smoothness, and we modify the algorithm to attain optimal rates for smoother functions. For practitioners, however, these results are somewhat misleading. Priors are typically not held fixed, but depend on parameters estimated from t...

Bull, Adam D

2011-01-01

171

A classical method for solving the variational inequality problem is the projection algorithm. We show that existing convergence results for this algorithm follow from one given by Gabay for a splitting algorithm for finding a zero of the sum of two maximal monotone operators. Moreover, we extend the projection algorithm to solve any monotone affine variational inequality problem. When applied

Paul Tsengt

1989-01-01

172

A classical method for solving the variational inequality problem is the projection algorithm. We show that existing convergence results for this algorithm follow from one given by Gabay for a splitting algorithm for finding a zero of the sum of two maximal monotone operators. Moreover, we extend the projection algorithm to solveany monotone affine variational inequality problem. When applied to

Paul Tseng

1990-01-01

173

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

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

174

AN OPTIMAL SUBGRADIENT ALGORITHM FOR LARGE-SCALE ...

sciences and engineering such as signal and image processing, machine ... tracting many researchers to work in the development of such schemes, ... topic on convex optimization, and the projection operator is available for many do- ...... analyze results from experiments in many fields such as astronomy, medical sciences,.

2015-01-06

175

?minimax: An Optimally Randomized MINIMAX Algorithm.

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

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

2012-08-01

176

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

177

Stroke volume optimization: the new hemodynamic algorithm.

Critical care practices have evolved to rely more on physical assessments for monitoring cardiac output and evaluating fluid volume status because these assessments are less invasive and more convenient to use than is a pulmonary artery catheter. Despite this trend, level of consciousness, central venous pressure, urine output, heart rate, and blood pressure remain assessments that are slow to be changed, potentially misleading, and often manifested as late indications of decreased cardiac output. The hemodynamic optimization strategy called stroke volume optimization might provide a proactive guide for clinicians to optimize a patient's status before late indications of a worsening condition occur. The evidence supporting use of the stroke volume optimization algorithm to treat hypovolemia is increasing. Many of the cardiac output monitor technologies today measure stroke volume, as well as the parameters that comprise stroke volume: preload, afterload, and contractility. PMID:25639574

Johnson, Alexander; Ahrens, Thomas

2015-02-01

178

Aimed at the shortcoming of neural network blind equalization algorithm, namely, the structure of neural network is difficult to determine, two basic principles of neural network blind equalization algorithm optimized by genetic algorithm were analyzed in the paper, by combining genetic algorithm and neural network blind equalization algorithm. At first, the structure and weight of neural network were optimized together

Liyi Zhang; Ting Liu; Yunshan Sun; Lei Chen

2010-01-01

179

Particle swarm optimization versus genetic algorithms for phased array synthesis

Particle swarm optimization is a recently invented high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational bookkeeping and generally only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array

Daniel W. Boeringer; Douglas H. Werner

2004-01-01

180

Genetic Algorithms Compared to Other Techniques for Pipe Optimization

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

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

1994-01-01

181

Multiobjective simulation optimization using an enhanced genetic algorithm

This paper presents an improved genetic algorithm ap- proach, based on new ranking strategy, to conduct multi- objective optimization of simulation modeling problems. This approach integrates a simulation model with stochas- tic nondomination-based multiobjective optimization tech- nique and genetic algorithms. New genetic operators are introduced to enhance the algorithm performance of find- ing Pareto optimal solutions and its efficiency in

Hamidreza Eskandari; Luis Rabelo; Mansooreh Mollaghasemi

2005-01-01

182

A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann

A Genetic Algorithm for Minimax Optimization Problems Jeffrey W. Herrmann Department of Mechanical-space genetic algorithm as a general technique to solve minimax optimization problems. This algorithm maintains of applications. To illustrate its potential, we use the two-space genetic algorithm to solve a parallel machine

Herrmann, Jeffrey W.

183

Intelligent perturbation algorithms for space scheduling optimization

NASA Technical Reports Server (NTRS)

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

Kurtzman, Clifford R.

1990-01-01

184

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

185

Gas pipeline optimization using adaptive algorithms

Transmission gas pipeline network consume significant amounts of energy. Then, minimizing the energy requirements is a challenging task. Due to the nonlinearity and poor knowledge of the system states, several results, based on the optimal control theory, are obtained only for simple configurations. In this paper an optimization scheme in the face of varying demand is carried out. It is based on the use of a dynamic simulation program as a plant model and the Pareto set technique to sell out useful experiments. Experiments are used for the identification of regression models based on an original class of functions. The nonlinear programming algorithm results. Its connection with regression models permits the definition off-line, and for a long time horizon, of the optimal discharge pressure trajectory for all the compressor stations. The use of adaptive algorithms, with high frequency, permits one to cancel the effect of unknown disturbances and errors in demand forecasts. In this way, an on-line optimization scheme using data of SCADA system is presented.

Smati, A.; Zemmour, N. [INH, Boumerdes (Algeria)

1996-12-31

186

Algorithms for optimizing CT fluence control

NASA Astrophysics Data System (ADS)

The ability to customize the incident x-ray fluence in CT via beam-shaping filters or mA modulation is known to improve image quality and/or reduce radiation dose. Previous work has shown that complete control of x-ray fluence (ray-by-ray fluence modulation) would further improve dose efficiency. While complete control of fluence is not currently possible, emerging concepts such as dynamic attenuators and inverse-geometry CT allow nearly complete control to be realized. Optimally using ray-by-ray fluence modulation requires solving a very high-dimensional optimization problem. Most optimization techniques fail or only provide approximate solutions. We present efficient algorithms for minimizing mean or peak variance given a fixed dose limit. The reductions in variance can easily be translated to reduction in dose, if the original variance met image quality requirements. For mean variance, a closed form solution is derived. The peak variance problem is recast as iterated, weighted mean variance minimization, and at each iteration it is possible to bound the distance to the optimal solution. We apply our algorithms in simulations of scans of the thorax and abdomen. Peak variance reductions of 45% and 65% are demonstrated in the abdomen and thorax, respectively, compared to a bowtie filter alone. Mean variance shows smaller gains (about 15%).

Hsieh, Scott S.; Pelc, Norbert J.

2014-03-01

187

Global Optimization Algorithms – Theory and Application

Don, t print me! This book is much more useful as electronic resource: you can search, terms and click links. You can t do that in a printed book. The book is frequently updated and improved as well. Printed versions will just outdate. Also think about the trees that would have to die for the paper! Don, t print me! Preface This e-book is devoted to Global Optimization algorithms, which are methods for finding solutions of high quality for an incredible wide range of problems. We introduce the basic concepts of optimization and discuss features which make optimization problems difficult and thus, should be considered when trying to solve them. In this book, we focus on

Thomas Weise

188

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

189

Design of Optimal Systolic Algorithms for the Transitive Closure Problem

New optimal systolic algorithms for the transitive closure problem on ring and linear arrays of processors is presented. The data dependency of the Warshal-Floyd algorithm is exploited to obtain highly pipelined parallel algorithms. One of the algorithms is asymptotically seven times more cost-effective than previous algorithms for computing transitive closure problems. The authors introduce a new expository device, called the

Dilip Sarkar; Amar Mukherjee

1992-01-01

190

The paper describes a general glance to the use of element exchange techniques for optimization over permutations. A multi-level description of problems is proposed which is a fundamental to understand nature and complexity of optimization problems over permutations (e.g., ordering, scheduling, traveling salesman problem). The description is based on permutation neighborhoods of several kinds (e.g., by improvement of an objective function). Our proposed operational digraph and its kinds can be considered as a way to understand convexity and polynomial solvability for combinatorial optimization problems over permutations. Issues of an analysis of problems and a design of hierarchical heuristics are discussed. The discussion leads to a multi-level adaptive algorithm system which analyzes an individual problem and selects/designs a solving strategy (trajectory).

Levin, Mark Sh

2011-01-01

191

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

192

A pivoting algorithm for convex hulls and vertex enumeration of arrangements and polyhedra

We p resent a ne wp iv ot-based algorithm which can be used with minor modifica- tion for the enumeration of the facets of the con vex hull of a set of points, or for the enu- meration of the vertices of an arrangement or of a con vex polyhedron, in arbitrary dimension. The algorithm has the following properties: (a)

David Avis; Komei Fukuda

1991-01-01

193

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

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

194

The stable conformation of a molecule is greatly important to uncover the secret of its properties and functions. Generally, the conformation of a molecule will be the most stable when it is of the minimum potential energy. Accordingly, the determination of the conformation can be solved in the optimization framework. It is, however, not an easy task to achieve the only conformation with the lowest energy among all the potential ones because of the high complexity of the energy landscape and the exponential computation increasing with molecular size. In this paper, we develop a hierarchical and heterogeneous particle swarm optimizer (HHPSO) to deal with the problem in the minimization of the potential energy. The proposed method is evaluated over a scalable simplified molecular potential energy function with up to 200 degrees of freedom and a realistic energy function of pseudo-ethane molecule. The experimental results are compared with other six PSO variants and four genetic algorithms. The results show HHPSO is significantly better than the compared PSOs with p-value less than 0.01277 over molecular potential energy function. PMID:25459763

Cheung, Ngaam J; Shen, Hong-Bin

2014-11-01

195

Intervals in evolutionary algorithms for global optimization

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

196

Urban drain layout optimization using PBIL algorithm

NASA Astrophysics Data System (ADS)

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

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

2008-10-01

197

An Efficient Interior-Point Method for Convex Multicriteria ...

specified above by solving the single-objective optimization problem min. zT f(x) ... problem is linear or strictly convex quadratic and the set of feasible points is fixed, i. e. ... interior-point algorithm as presented in [22] is rehearsed in short.

2004-02-25

198

Multiple Birth and Cut Algorithm for Point Process Optimization

Multiple Birth and Cut Algorithm for Point Process Optimization Ahmed Gamal-Eldin, Xavier Descombes, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC

199

Optimal control of trading algorithms: a general impulse control approach

inclusion of a new effect in the "optimal-control-oriented" original framework gave birth to a specificOptimal control of trading algorithms: a general impulse control approach Bruno Bouchard , Ngoc-day trading based on the control of trading algorithms. Given a generic parameterized algorithm, we control

Paris-Sud XI, Université de

200

The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects

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

Noakes, Lyle

201

An Iterative Global Optimization Algorithm for Potential Energy Minimization

[1]. The algorithm is tested on two different potential energy functions. The first function algorithms considering the potential functions as sample global optimization test problems. In [12An Iterative Global Optimization Algorithm for Potential Energy Minimization N. P. Moloi and M. M

202

An Iterative Global Optimization Algorithm for Potential Energy Minimization

[1]. The algorithm is tested on two different potential energy functions. The first function algorithms considering the potential functions as sample global optimization test problems. In [12 the potential functions as sample global optimization problems only for testing our algorithm. We do

203

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

204

Stochastic Optimization Algorithms for Support Vector Machines Classification

In this paper, we consider the problem of semi-supervised binary classification by Sup- port Vector Machines (SVM). This problem is explored as an unconstrained and non-smooth optimization task when part of the available data is unlabelled. We apply non-smooth optimiza- tion techniques to classification where the objective function considered is non-convex and non- differentiable and so difficult to minimize. We

Vaida BARTKUT

205

Instrument design and optimization using genetic algorithms

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

206

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

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

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

2009-01-01

207

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

Functional module identification in biological networks may provide new insights into the complex interactions among biomolecules for a better understanding of cellular functional organization. Most of existing functional module identification methods are based on the optimization of network modularity and cluster networks into groups of nodes within which there are a higher-than-expectation number of edges. However, module identification simply based on this topological criterion may not discover certain kinds of biologically meaningful modules within which nodes are sparsely connected but have similar interaction patterns with the rest of the network. In order to unearth more biologically meaningful functional modules, we propose a novel efficient convex programming algorithm based on the subgradient method with heuristic path generation to solve the problem in a recently proposed framework of blockmodel module identification. We have implemented our algorithm for large-scale protein-protein interaction (PPI) networks, including Saccharomyces cerevisia and Homo sapien PPI networks collected from the Database of Interaction Proteins (DIP) and Human Protein Reference Database (HPRD). Our experimental results have shown that our algorithm achieves comparable network clustering performance in comparison to the more time-consuming simulated annealing (SA) optimization. Furthermore, preliminary results for identifying fine-grained functional modules in both biological networks and the comparison with the commonly adopted Markov Clustering (MCL) algorithm have demonstrated the potential of our algorithm to discover new types of modules, within which proteins are sparsely connected but with significantly enriched biological functionalities. PMID:23368964

2013-01-01

208

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

209

Local optimality of dictionary learning algorithms Boris Mailh

Local optimality of dictionary learning algorithms Boris MailhÃ© Centre for Digital Music School dictionary learning algorithms. We focus on three algorithms: the Olshausen and Field algorithm (Ols-DLA) [1 matrix of training data. We consider the following dictionary learning problem min ,X S - X 2 2 (1

Plumbley, Mark

210

Study on Water Pollution Diffusion by Artificial Immunity Optimization Algorithm

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

211

A Distributed Particle Swarm Optimization Algorithm for Swarm Robotic Applications

We have derived a version of the particle swarm optimization algorithm that is suitable for a swarm consisting of a large number of small, mobile robots. The algorithm, called the distributed PSO (dPSO), is for \\

James M. Hereford

2006-01-01

212

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

213

McCormick-Based Relaxations of Algorithms

Theory and implementation for the global optimization of a wide class of algorithms is presented via convex/affine relaxations. The basis for the proposed relaxations is the systematic construction of subgradients for the ...

Mitsos, Alexander

214

Global Optimization of Chemical Processes using Stochastic Algorithms

of a fermentation process, to deterÂ mine multiphase equilibria, for the optimal control of a penicillin reactor of the penicillin reactor and the nonÂdifferentiable system. 1. INTRODUCTION GradientÂbased optimization algorithms

Neumaier, Arnold

215

Improved hybrid optimization algorithm for 3D protein structure prediction.

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins. PMID:25069136

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

216

Optimal nonmonotonic convergence of the iterative Fourier-transform algorithm

NASA Astrophysics Data System (ADS)

The increase of the monotonic convergence rate is an important issue for iterative Fourier-transform algorithms. However, the steepest monotonic convergence of the iterative Fourier-transform algorithm does not always promise an optimal solution in the design of a diffractive optical element. The optimal nonmonotonic convergence of the iterative Fourier-transform algorithm is investigated by employing a microgenetic algorithm. The proposed hybrid scheme of the iterative Fourier-transform algorithm and the microgenetic algorithm show nonmonotonic convergence, and this results in a superior design.

Kim, Hwi; Lee, Byoungho

2005-02-01

217

A parallel textured algorithm for optimal power flow

Abur (Member) ostas N. Georglna&les (Member) )of Mo'pgan , ' (Member) Jo W. Howze (Head of Department) August 1992 ABSTRACT A Parallel Textured Algorithm for Optimal Power I'low Analysis. (August 1992) Shih-Chieh Hsieh, B. S. , National.... The Optimal Power Flow Problems B. Present Status of Optimal Power Flow Analysis C. Solution Method D. Thesis Structure . FORMULATION OF OPTIMAL POWER FLOW ANALYSIS . 6 A. Conventional Formulation of Optimal Power Analysis B. Reformulation of Optimal...

Hsieh, Shih-Chieh

1992-01-01

218

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

219

A robust unit commitment algorithm for hydro-thermal optimization

This paper presents a unit commitment algorithm which combines the Lagrangian relaxation (LR), sequential unit commitment (SUC), and optimal unit decommitment (UD) methods to solve a general hydro-thermal optimization (HTO) problem. We argue that this approach retains the advantages of the LR method while addressing the method's observed weaknesses to improve overall algorithm performance and quality of solution. The proposed

Chaa-An Li; Raymond B. Johnson; Alva J. Svoboda; Chung-Li Tseng; Eric Hsu

1998-01-01

220

A robust unit commitment algorithm for hydro-thermal optimization

This paper presents a unit commitment algorithm which combines the Lagrangian relaxation (LR), sequential unit commitment (SUC), and optimal unit decommitment (UD) methods to solve a general hydro-thermal optimization (HTO) problem. The authors argue that this approach retains the advantages of the LR method while addressing the method's observed weaknesses to improve overall algorithm performance and quality of solution. The

Chao-An Li; Raymond B. Johnson; Alva J. Svoboda; Chung-Li Tseng; Eric Hsu

1997-01-01

221

Optimized Monte Carlo Path Generation using Genetic Algorithms

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

222

Optimal Algorithms for GSM Viterbi Modules M.Sc. Student

design of the 3rd-Generation Global System for Mobile communica- tions(GSM 3G) unit's channel codeOptimal Algorithms for GSM Viterbi Modules Kehuai Wu M.Sc. Student at Department of Informatics and Mathematical Modelling Technical University of Denmark #12;#12;Optimal Algorithms for GSM Viterbi Modules #12

223

Particle Swarm Optimization algorithm for facial emotion detection

Particle Swarm Optimization (PSO) algorithm has been applied and found to be efficient in many searching and optimization related applications. In this paper, we present a modified version of the algorithm that we successfully applied to facial emotion detection. Our approach is based on tracking the movements of facial action units (AUs) placed on the face of a subject and

Bashir Mohammed Ghandi; R. Nagarajan; Hazry Desa

2009-01-01

224

Concurrent genetic algorithms for optimization of large structures

In a recent article, the writers presented an augmented Lagrangian genetic algorithm for optimization of structures. The optimization of large structures such as high-rise building structures and space stations with several hundred members by the hybrid genetic algorithm requires the creation of thousands of strings in the population and the corresponding large number of structural analyses. In this paper, the

Hojjat Adeli; Nai-Tsang Cheng

1994-01-01

225

Genetic Algorithms Are NOT Function Optimizers Kenneth A. De Jong

Genetic Algorithms Are NOT Function Optimizers Kenneth A. De Jong Computer Science Department George Mason University Fairfax, VA 22030, USA kdejong@aic.gmu.edu Abstract Genetic Algorithms (GAs) have received a great deal of attention regarding their potential as optimization techniques for complex

George Mason University

226

Generalized Proximal Method for Efficient Solutions in Vector Optimization

This article is devoted to developing the generalized proximal algorithm of finding efficient solutions to the vector optimization problem for a mapping from a uniformly convex and uniformly smooth Banach space to a real Banach space with respect to the partial order induced by a pointed closed convex cone. In contrast to most published literature on this subject, our algorithm

T. D. Chuong

2011-01-01

227

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

228

Fast Algorithm To Generate Near Optimal Binary Decision Programs

NASA Astrophysics Data System (ADS)

Binary decision (BD) programs have widespread applicability in diverse areas. In many situations, problems are defined in terms of Boolean expressions, which must be converted to decision programs for evaluation. Since the construction of optimal BD programs is NP-complete, absolute optimization appears computationally intractable. This paper presents a fast heuristic algorithm for constructing near-optimal decision programs and provides an optimality metric. The algorithm involves two steps: preprocessing and optimization. The preprocessor builds a sub-optimal (in some conditions near-optimal) decision program in linear time. If sub-optimal programs are generated, the optimizer is invoked, producing a near-optimal program, using decision tables, in 0(n2) time, where n is the size of the reduced decision table generated in the first step.

Baracos, P. C.; Vroomen, L. C.; Vroomen, L. J.

1987-10-01

229

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

230

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

231

A simple elitist genetic algorithm for constrained optimization

In this paper we propose a novel approach for solving constrained optimization problems using genetic algorithms. The main emphasis of this algorithm is to be problem independent and to produce consistent results in terms of the quality of feasible solutions. The basic characteristic of this algorithm is the complete ignorance of the objective function till at least one feasible solution

Sangameswar Venkatraman; Gary G. Yen

2004-01-01

232

M-PAES: a memetic algorithm for multiobjective optimization

A memetic algorithm for tackling multiobjective optimization problems is presented. The algorithm employs the proven local search strategy used in the Pareto archived evolution strategy (PAES) and combines it with the use of a population and recombination. Verification of the new M-PAES (memetic PAES) algorithm is carried out by testing it on a set of multiobjective 0\\/1 knapsack problems. On

Joshua D. Knowles; David W. Corne

2000-01-01

233

A real-time optimal control algorithm for greenhouse heating

A real-time control algorithm for generating optimal heating setpoints has been implemented and tested on a commercial greenhouse nursery with a tomato crop. The algorithm is based on a model of the greenhouse energy requirements and on a numerical method for optimisation, and uses weather forecasts supplied by the Meteorological Office. The algorithm resides on a PC which communicates with

Z. S. Chalabi; B. J. Bailey; D. J. Wilkinson

1996-01-01

234

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN

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 ...

235

A multi-sexual genetic algorithm for multiobjective optimization

In this paper a new method for solving multicriteria optimization problems by Genetic Algorithms is proposed. Standard Genetic Algorithms use a population, where each individual has the same sex (or has no sex) and any two individuals can be crossed over. In the proposed Multisexual Genetic Algorithm (MSGA), individuals have an additional feature, their sex or gender and one individual

Joanna Lis; A. E. Eiben

1997-01-01

236

An optimal online algorithm for metrical task systems

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

Allan Borodin; Nathan Linial; Michael E. Saks

1987-01-01

237

Hybrid Evolutionary Algorithm for Solving Global Optimization Problems

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

Radha Thangaraj; Millie Pant; Ajith Abraham; Youakim Badr

2009-01-01

238

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

239

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

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

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

1995-11-01

240

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

241

A New Optimized GA-RBF Neural Network Algorithm

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

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

2014-01-01

242

Optimal design of the magnetic microactuator using the genetic algorithm

This paper presents the optimal design of the magnetic microactuator using the genetic algorithm. The magnetic microactuator is composed of an enclosed core and a permalloy plate to form a closed magnetic circuit. The present design allows the area of the magnetic poles to be optimally enlarged and achieve a maximum force generation. To obtain the optimal geometry and maximum

C. H. Ko; J. C. Chiou

2003-01-01

243

Optimal barreling of steel shells via simulated annealing algorithm

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

J. B?achut

2003-01-01

244

Nonholonomic motion planning based on Newton algorithm with energy optimization

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

245

Implementation of the simultaneous perturbation algorithm for stochastic optimization

The need for solving multivariate optimization problems is pervasive in engineering and the physical and social sciences. The simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted considerable attention for challenging optimization problems where it is difficult or impossible to directly obtain a gradient of the objective function with respect to the parameters being optimized. SPSA is based on an

JAMES C. SPALL

1998-01-01

246

A new yield optimization algorithm and its applications

The authors propose a novel yield optimization algorithm for IC design. To design a practical yield optimization system, two efforts must be made. One is to get a suitable convergence criterion, the other is to propose an efficient optimization method, by which one can reach the maximum yield point as soon as possible. A convergence criterion based on sequential tests

Zhihua Wang; Huazhong Yang; Rensheng Liu; Chongzhi Fan

1991-01-01

247

Optimization Online - Linear, Cone and Semidefinite Programming ...

Polynomial interior point algorithms for general LCPs ... A polynomial-time interior-point method for conic optimization, with inexact barrier evaluations ... A Sequential Convex Semidefinite Programming Algorithm for Multiple-Load Free Material Optimization ... An Affine-Scaling Pivot Algorithm for Linear Programming

248

Optimization Online - All Areas Submissions - February 2011

A Note on Superlinear Convergence of a Primal-dual Interior Point Method for ... On the Number of Solutions Generated by the Dual Simplex Method ... A Dual Algorithm For Approximating Pareto Sets in Convex Multi-Criteria Optimization

249

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

250

Biology-Derived Algorithms in Engineering Optimization

Biology-derived algorithms are an important part of computational sciences, which are essential to many scientific disciplines and engineering applications. Many computational methods are derived from or based on the analogy to natural evolution and biological activities, and these biologically inspired computations include genetic algorithms, neural networks, cellular automata, and other algorithms.

Yang, Xin-She

2010-01-01

251

We present a further study and analysis of an exponential annealing based algorithm for convex optimization. We begin by developing a general framework for applying exponential annealing to conic optimization. We analyze ...

Chen, Jeremy, S.M. Massachusetts Institute of Technology

2007-01-01

252

Evaluation of a particle swarm algorithm for biomechanical optimization.

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. PMID:16060353

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

2005-06-01

253

An Adaptive Unified Differential Evolution Algorithm for Global Optimization

In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.

Qiang, Ji; Mitchell, Chad

2014-11-03

254

Genetic algorithms for multicriteria shape optimization of induction furnace

NASA Astrophysics Data System (ADS)

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

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

2012-09-01

255

Combining Immune with Ant Colony Algorithm for Geometric Constraint Solving

Geometric constraint problem can be transformed to an optimization problem which the objective function and constraints are non-convex functions. In this paper an evolutionary algorithm based on ant colony optimization algorithm and the immune system model is proposed to provide solution to the geometric constraints problem. In the new algorithm, affinity calculation process and pheromone trail lying is embedded to

Hua Yuan; Yi Li; Wenhui Li; Kong Zhao; Duo Wang; Rongqin Yi

2008-01-01

256

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860

Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.

2014-01-01

257

PIECEWISE QUADRATIC APPROXIMATIONS IN CONVEX ...

For instance, the recent [31] uses a different model ...... Fortunately, increasing ? and/or ? is a reaction to the fact that the ? obtained by ..... Piecewise quadratic approximations in convex numerical optimization. 25 name n function. 1 CB2. 2.

2010-12-11

258

Subgradient methods for convex minimization

Many optimization problems arising in various applications require minimization of an objective cost function that is convex but not differentiable. Such a minimization arises, for example, in model construction, system ...

NediÄ‡ , Angelia

2002-01-01

259

Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization

address composite optimization problems of the form minimize xRd f(x) := g(x) + h(x), (1) where g and h is 1-regularized least squares [3, 4], minimize xRd Ax - b 2 + x 1, 1 inria-00618152,version1-12Sep of a proximal-gradient method requires the calculation of the proximity operator, proxL(y) = arg min xRd L 2 x

260

Reliability-Based Optimization Using Evolutionary Algorithms

Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist ...

Deb, Kalyanmoy

261

two-layer perceptron networks by C. Cabrelli1 , U. Molter1 Departamento de MatemÂ´atica, Facultad de-layer Perceptron) using threshold activation functions is that either it be a convex polytope, or that intersected not be arbitrarily large. An artificial Neural Network consisting of a single such neuron is known as a Perceptron

Cabrelli, Carlos

262

Bio-inspired optimization algorithms for smart antennas

This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external ...

Zuniga, Virgilio

2011-11-22

263

A Quasi-Newton Algorithm for Nonconvex, Nonsmooth Optimization ...

May 26, 2014 ... value optimization [1], compressed sensing [8, 9, 16], and .... the term “Hessian” loosely as a matrix that approximates changes in ?f about ...... In this section, we prove that Algorithm 1 is globally convergent from remote.

2014-05-26

264

Application of a gradient-based algorithm to structural optimization

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

Ghisbain, Pierre

2009-01-01

265

Greedy Algorithms for Optimized DNA Sequencing Allon G. Percus

Greedy Algorithms for Optimized DNA Sequencing Allon G. Percus David C. Torney Abstract We discussÂB258, Los Alamos National Laboratory, Los Alamos, NM 87545. E-mail: percus@lanl.gov. TÂ10 and Center

Percus, Allon

266

New Hybrid Optimization Algorithms for Machine Scheduling Problems

decision (or constraint satisfaction) problems resulting from a dichotomy to locate the .... is allowed to start at time rj; once started, the job will occupy the machine ...... the theory and applications of large-scale optimization algorithms, discrete.

2006-12-03

267

PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm

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

268

Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with

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

Richardson, David

269

RELIABLE SOLUTION OF CONVEX QUADRATIC PROGRAMS ...

Key words. active set, convex, homotopy method, parametric quadratic programming ... impluse response design, optimal power flow, economic dispatch, etc. Several ... *Interdisciplinary Center for Scientific Computing, Heidelberg University, ...

2011-01-28

270

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

271

A Unified Differential Evolution Algorithm for Global Optimization

Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.

Qiang, Ji; Mitchell, Chad

2014-06-24

272

Cellular Probabilistic Evolutionary Algorithms for Real-Coded Function Optimization

NASA Astrophysics Data System (ADS)

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

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

273

An Optimal Minimum Spanning Tree Algorithm

We establish that the algorithmic complexity of the minimum spanning tree problem isequal to its decision-tree complexity. Specifically, we present a deterministic algorithm to find aminimum spanning forest of a graph with n vertices and m edges that runs in time O(T(m; n))where Tis the minimum number of edge-weight comparisons needed to determine the solution.The algorithm is quite simple and

Seth Pettie; Vijaya Ramachandran

2000-01-01

274

Superscattering of light optimized by a genetic algorithm

We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.

Mirzaei, Ali, E-mail: ali.mirzaei@anu.edu.au; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S. [Nonlinear Physics Center, Research School of Physics and Engineering, Australian National University, Canberra ACT 0200 (Australia)

2014-07-07

275

Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms

HORIZONTAL WELL PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE May 2010 Major Subject: Petroleum Engineering HORIZONTAL WELL PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate...

Gibbs, Trevor Howard

2011-08-08

276

Optimization of image processing algorithms on mobile platforms

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

Pramod Poudel; Mukul Shirvaikar

2011-01-01

277

algorithm for minimizing a smooth function over a convex set is de- scribed. Each iteration of the method or parsimonious models. In the case of i.i.d. regression or classification, there are many efficient algorithms (e.g., Andrew and Gao (2007)) for solving such problems. In the case of structured models, such as Markov random

Friedlander, Michael P.

278

-based methods are, in general, much more cost effective than algorithms that require only the function valuesMethods for optimizing large molecules Part III.y An improved algorithm for geometry optimization structure), (b) oscillation around an inflection point on the potential energy surface, (c) numerical

Schlegel, H. Bernhard

279

A Hybrid Genetic Algorithm for Routing Optimization in IP Networks

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

Riedl, Anton

280

Genetic algorithm optimization applied to electromagnetics: a review

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

281

An optimal scheduling algorithm for electronic control unit in vehicles

In this paper, we propose an optimal scheduling algorithm for electronic control units in vehicles which usually connect with low speed CAN bus or LIN bus and apply to automotive body control, such as lamp control, power window control, windscreen wiper control. This algorithm is used to (1) guarantee schedulability of mandatory parts of periodic tasks, (2) guarantee aperiodic tasks

Yuan Sun; Xiaobing Zhao; Guosheng Yang

2009-01-01

282

Reconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics

: the reconstruction of evolutionary histories (phylogenies) from molecular data such as DNA sequences. OurReconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics Bernard M: we conducted an exÂ tensive study of quartetÂbased reconstruction algorithms within a parameter

Moret, Bernard

283

MOMMIE Knows Best: Systematic Optimizations for Verifiable Distributed Algorithms

Introduction Complex distributed algorithms become running systems through an integration with optimizations that target the system's deployment environment. Although expedient, this approach has disadvantages. First as the fundamental cause for this problem. On one hand, algorithm designers need abstraction to simplify the job

Maniatis, Petros

284

MOGA algorithm for multi-objective optimization of aircraft detection

This paper presents effective multi-objective genetic algorithms (MOGA) method, whose character lies in that evolutionary population is preference ranked based on concordance model, which was applied to a multi-objective optimization of aircraft, measure of fitness degree was discussed as an emphasis. The solutions were analyzed and compares with original BP neural networks algorithm, which is better than the network trained

Hongguang Sun; Yuxue Pan; Jingbo Zhang

2006-01-01

285

AN ADAPTIVE PROJECTION ALGORITHM FOR MULTIRATE FILTER BANK OPTIMIZATION

to the nonquadratic nature of the cost function to be minimized, and accordingly non gradient algorithms may offer to the global minimum of the cost function, while at the same time avoiding potential local minima due to itsAN ADAPTIVE PROJECTION ALGORITHM FOR MULTIRATE FILTER BANK OPTIMIZATION Dong-Yan Huang and Phillip

Regalia, Phillip A.

286

Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem

have looked at the application of genetic algorithms to optimization of nonlinear functions; our algorithm works. The method operates with a set of potential solutions. This is referred to as a population individuals. Based on this fitness function a number of individuals are selected as potential parents

Ferris, Michael C.

287

A polynomial-time interior-point method for conic optimization, with ...

Interior-point methods (IPMs) are the algorithms of choice for solving many convex opti- ... most efficient algorithms require barrier functions for both the primal and dual problems. ... method for linear optimization problems can in principle be applied to conic .... Let S ? Rn be a closed convex set with nonempty interior.

2008-04-29

288

Searching for Pareto-optimal Randomised Algorithms

, providing an avenue of optimisation to satisfy non-functional requirements. We use Multi that the satisfaction of non-functional requirements is an impor- tant consideration in algorithm design that the choice of probability distribution influences the non-functional prop- erties of such algorithms

White, David R.

289

Neural Networks Training with Optimal Bounded Ellipsoid Algorithm

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

290

Monotonically convergent algorithm for quantum optimal control with dissipation

NASA Astrophysics Data System (ADS)

This paper extends a monotonically convergent algorithm for quantum optimal control to treat systems with dissipation. The algorithm working with the density matrix is proved to exhibit quadratic and monotonic convergence. Several numerical tests are implemented in three-level model systems. The algorithm is exploited to control various targets, including the expectation value of a Hermitian operator, the modulus square of the expectation value of a non-Hermitian operator, and off-diagonal elements of the density matrix.

Ohtsuki, Yukiyoshi; Zhu, Wusheng; Rabitz, Herschel

1999-05-01

291

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

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

292

Enhanced Dynamic Programming Algorithms for Series Line Optimization

1 Enhanced Dynamic Programming Algorithms for Series Line Optimization Michael H. Veatch* May 2005 Abstract Dynamic programming value iteration is made more ef cient on a ve-machine unreliable series line of optimal policies are identi ed. Index Terms Make-to-stock, production line, dynamic programming, control

Veatch, Michael H.

293

Optimal distributed algorithm for minimum spanning trees revisited

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

294

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm

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

Christoph Burger

2007-01-01

295

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

296

Optimization of the Solovay-Kitaev algorithm

NASA Astrophysics Data System (ADS)

The Solovay-Kitaev algorithm is the standard method used for approximating arbitrary single-qubit gates for fault-tolerant quantum computation. In this paper we introduce a technique called search space expansion, which modifies the initial stage of the Solovay-Kitaev algorithm, increasing the length of the possible approximating sequences but without requiring an exhaustive search over all possible sequences. This technique is combined with an efficient space search method called geometric nearest-neighbor access trees, modified for the unitary matrix lookup problem, in order to reduce significantly the algorithm run time. We show that, with low time cost, our techniques output gate sequences that are almost an order of magnitude smaller for the same level of accuracy. This therefore reduces the error correction requirements for quantum algorithms on encoded fault-tolerant hardware.

Pham, Tien Trung; Van Meter, Rodney; Horsman, Clare

2013-05-01

297

A Discrete Lagrangian Algorithm for Optimal Routing Problems

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

298

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

299

Integrated genetic algorithm for optimization of space structures

NASA Astrophysics Data System (ADS)

Gradient-based mathematical-optimization algorithms usually seek a solution in the neighborhood of the starting point. If more than one local optimum exists, the solution will depend on the choice of the starting point, and the global optimum cannot be found. This paper presents the optimization of space structures by integrating a genetic algorithm with the penalty-function method. Genetic algorithms are inspired by the basic mechanism of natural evolution, and are efficient for global-searches. The technique employs the Darwinian survival-of-the-fittest theory to yield the best or better characters among the old population, and performs a random information exchange to create superior offspring. Different types of crossover operations are used in this paper, and their relative merit is investigated. The integrated genetic algorithm has been implemented in C language and is applied to the optimization of three space truss structures. In each case, an optimum solution was obtained after a limited number of iterations.

Adeli, Hojjat; Cheng, Nai-Tsang

1993-10-01

300

Scaled conjugate gradient algorithms for unconstrained optimization

In this work we present and analyze a new scaled conjugate gradient algorithm and its implementation, based on an interpretation\\u000a of the secant equation and on the inexact Wolfe line search conditions. The best spectral conjugate gradient algorithm SCG\\u000a by Birgin and Martínez (2001), which is mainly a scaled variant of Perry’s (1977), is modified in such a manner to

Neculai Andrei

2007-01-01

301

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

302

A hybrid IWO\\/PSO algorithm for fast and global optimization

This paper presents a hybrid optimization algorithm which originates from Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO). Based on the novel and distinct qualifications of IWO and PSO, we introduce IWO\\/PSO algorithm and try to combine their excellent features in this extended algorithm. The efficiency of this algorithm both in the case of speed of convergence and optimality

Hossein Hajimirsadeghi; Caro Lucas

2009-01-01

303

Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem

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

Le Dinh, Luong; Vo Ngoc, Dieu

2013-01-01

304

Artificial bee colony algorithm for solving optimal power flow problem.

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

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

2013-01-01

305

A near optimal algorithm for lifetime optimization in wireless sensor networks

A near optimal algorithm for lifetime optimization in wireless sensor networks Karine Deschinkel1.deschinkel, mourad.hakem}@univ-fcomte.fr Keywords: target coverage, wireless sensor networks, centralized method in wireless sensor networks (WSN) is lifetime optimization. Indeed, in WSN each sensor node is battery powered

Paris-Sud XI, Université de

306

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

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

2011-01-01

307

An Ant Colony Optimization Algorithm for the Optimization of the Keyboard Arrangement

An Ant Colony Optimization Algorithm for the Optimization of the Keyboard Arrangement Problem Jan account of ergonomic criteria is proposed. Based on the generic framework of Ant Colony Optimiza- tion computations, ant colony optimization, key- board arrangement 1 Introduction A computer user or typist

Paris-Sud XI, Université de

308

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

309

An Improved PSO Algorithm and its Application to Structural Fatigue Life Optimization

Particle swarm optimization is a new type of intelligent optimization algorithm. In order to improve the efficiency of structural fatigue optimization, a novel optimization strategy based on the improved particle swarm optimization algorithm is proposed. According to this strategy, one optimization software is developed. As an application of this strategy, the fatigue optimization of a landing gear is presented. The

Liu Bing; Xue Caijun; Tan Wei

2010-01-01

310

Background Microbial hosts offer a number of unique advantages when used as production systems for both native and heterologous small-molecules. These advantages include high selectivity and benign environmental impact; however, a principal drawback is low yield and/or productivity, which limits economic viability. Therefore a major challenge in developing a microbial production system is to maximize formation of a specific product while sustaining cell growth. Tools to rationally reconfigure microbial metabolism for these potentially conflicting objectives remain limited. Exhaustively exploring combinations of genetic modifications is both experimentally and computationally inefficient, and can become intractable when multiple gene deletions or insertions need to be considered. Alternatively, the search for desirable gene modifications may be solved heuristically as an evolutionary optimization problem. In this study, we combine a genetic algorithm and elementary mode analysis to develop an optimization framework for evolving metabolic networks with energetically favorable pathways for production of both biomass and a compound of interest. Results Utilization of thermodynamically-weighted elementary modes for flux reconstruction of E. coli central metabolism revealed two clusters of EMs with respect to their ?Gp°. For proof of principle testing, the algorithm was applied to ethanol and lycopene production in E. coli. The algorithm was used to optimize product formation, biomass formation, and product and biomass formation simultaneously. Predicted knockouts often matched those that have previously been implemented experimentally for improved product formation. The performance of a multi-objective genetic algorithm showed that it is better to couple the two objectives in a single objective genetic algorithm. Conclusion A computationally tractable framework is presented for the redesign of metabolic networks for maximal product formation combining elementary mode analysis (a form of convex analysis), pathway thermodynamics, and a genetic algorithm to optimize the production of two industrially-relevant products, ethanol and lycopene, from E. coli. The designed algorithm can be applied to any small-scale model of cellular metabolism theoretically utilizing any substrate and applied towards the production of any product. PMID:20416071

2010-01-01

311

Fast Approximate Convex Decomposition

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

Ghosh, Mukulika

2012-10-19

312

On the convexity of the multiplicative version of Karmarkar's potential function

Karmarkar's potential function is quasi-convex, but not convex. This note investigates the multiplicative version of the potential function, and shows that it is not necessarily convex in general, but is strictly convex when the corresponding feasible region is bounded. This implies that the multiplicative version of the potential function in Karmarkar's algorithm is convex, since it works on a simplex.

Hiroshi Imai

1988-01-01

313

Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms

Optimizing Melodic Extraction Algorithm for Jazz Guitar Recordings Using Genetic Algorithms Sergio of jazz standards, and we collected commercial audio recordings extracted from jazz guitar CDs. Based on the MIDI record- ings as ground truth, two different instrument settings are compared (Jazz trio

314

A Solution Quality Assessment Method for Swarm Intelligence Optimization Algorithms

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

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

2014-01-01

315

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

Zhu, Zhouquan

2013-01-01

316

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

Wang, Peng; Zhu, Zhouquan; Huang, Shuai

2013-01-01

317

A training algorithm for optimal margin classifiers

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

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

1992-01-01

318

Parallel Algorithms for Big Data Optimization

at least one (block) component which is within a factor ? ? (0, 1] “far ..... affect in any way the theoretical convergence properties of the algorithms. On the other .... in comparison with parallel methods; therefore we excluded. ADMM and GS in .... because we already ascertained that they are not competitive. The tuning of the ...

2014-02-21

319

Optimal Speedup of Las Vegas Algorithms

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

Michael Luby; Alistair Sinclair; David Zuckerman

1993-01-01

320

Turbo codes optimization using genetic algorithms

Turbo codes have been an important revolution in the digital communications world. Since their discovery, the coding community has been trying to understand, explain and improve turbo codes. The floor phenomenon is the parallel concatenated convolutional turbo codes main problem. In this paper, genetic algorithms are used to lower the free distance of such a code. Results in terms of

Nicolas Durand; Jean-Marc Alliot; B. Bartolome

1999-01-01

321

Binary wavefront optimization using a genetic algorithm

NASA Astrophysics Data System (ADS)

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

Zhang, Xiaolong; Kner, Peter

2014-12-01

322

Optimization of mass spectrometers using the adaptive particle swarm algorithm.

Optimization of mass spectrometers using the adaptive particle swarm algorithm (APSA) is described along with implementations for ion optical simulations and various time-of-flight (TOF) instruments. The need for in situ self optimization is addressed through discussion of the reflectron TOF mass spectrometer (RTOF) on the European Space Agency mission Rosetta. In addition, a tool for optimization of laboratory mass spectrometers is presented and tested on two different instruments. After the application of APSA optimization, a substantial increase in performance for mass spectrometers that have manually been tuned for several weeks or months is demonstrated. PMID:22124986

Bieler, A; Altwegg, K; Hofer, L; Jäckel, A; Riedo, A; Sémon, T; Wahlström, P; Wurz, P

2011-11-01

323

Modified ant-colony-optimization algorithm as an alternative to genetic algorithms

NASA Astrophysics Data System (ADS)

An alternative approach for the optimization strategy in quantum control experiments is proposed. Genetic algorithms are used frequently to improve the laser fields driving quantum processes. We present a flexible scheme based on ant-colony-optimization, which introduces a correlation of the mask function pixels and allows a decrease in the shaped pulse complexity and duration without loss of efficiency.

Gollub, C.; de Vivie-Riedle, R.

2009-02-01

324

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

Zeigler, B. P.

1974-01-01

325

A computational study of the homogeneous algorithm for largescale convex optimization

. Andersen \\Lambda Yinyu Ye y May 1996 (Revised marts 1997) Abstract Recently the authors have proposed and DMIÂ9522507. 1 #12; Key words: Monotone complementarity problem, homogeneous and selfÂdual model and practical behavior of the primalÂdual interiorÂpoint methods for solving general or specific classes

Ye, Yinyu

326

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

327

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

328

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

329

Coordinate Search Algorithms in Multilevel Optimization

rithms in a multilevel optimization paradigm. We develop a .... discuss the issue of selecting the operators, and adopt as a default choice the linear ..... ? = ?t2 is a classical forcing function which ensures sufficient decrease; we set ? = 10?4 in ...

2012-10-16

330

An optimal extraction algorithm for imaging photometry

This paper is primarily an investigation of whether the `optimal extraction' techniques used in CCD spectroscopy can be applied to imaging photometry. It is found that using such techniques provides a gain of around 10 per cent in signal-to-noise ratio over normal aperture photometry. Formally, it is shown to be equivalent to profile fitting, but offers advantages of robust error

Tim Naylor

1998-01-01

331

Initializing Partition-Optimization Algorithms Ranjan Maitra

is a challenging problem needed in a wide array of applications. Partition-optimization ap- proaches, such as k applicability of the problem. Most approaches involve a certain degree of empiricism but broadly fall- like structure for demarcating groups, with the property that all observations in a group at some

Maitra, Ranjan

332

A software-optimized encryption algorithm

We describe a fast, software-oriented, encryption algorithm. Computational cost on a 32-bit processor is about 5 elementary machine instructions per byte of text. The cipher is a pseudorandom function; under control of a key (first pre-processed into an internal table) it stretches a short index into a much longer pseudorandom string. This string can be used as a one-time pad.

Phillip Rogaway; Don Coppersmith

333

Parallel Particle Swarm Optimization Algorithm Based on Graphic Processing Units

\\u000a A novel parallel approach to implement particle swarm optimization(PSO) algorithm on graphic processing units(GPU) in a personal\\u000a computer is proposed in this chapter. By using the general-purpose computing ability of GPU and under the software platform\\u000a of compute unified device architecture(CUDA) which is developed by NVIDIA, the PSO algorithm can be executed in parallel on\\u000a the GPU. The process of

Ying Tan; You Zhou

334

An optimal on-line algorithm for metrical task system

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

Allan Borodin; Nathan Linial; Michael E. Saks

1992-01-01

335

Shape Optimization of Rubber Bushing Using Differential Evolution Algorithm

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

2014-01-01

336

New near-optimal feedback guidance algorithms for space missions

NASA Astrophysics Data System (ADS)

This dissertation describes several different spacecraft guidance algorithms, with applications including asteroid intercept and rendezvous, planetary landing, and orbital transfer. A comprehensive review of spacecraft guidance algorithms for asteroid intercept and rendezvous. Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) guidance is introduced and applied to asteroid intercept and rendezvous, and to a wealth of different example problems, including missile intercept, planetary landing, and orbital transfer. It is seen that the ZEM/ZEV guidance law can be used in many different scenarios, and that it provides near-optimal performance where an analytical optimal guidance law does not exist, such as in a non-linear gravity field.

Hawkins, Matthew Jay

337

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

338

Optimization Algorithm for the Generation of ONCV Pseudopotentials

We present an optimization algorithm to construct pseudopotentials and use it to generate a set of Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotentials for elements up to Z=83 (Bi) (excluding Lanthanides). We introduce a quality function that assesses the agreement of a pseudopotential calculation with all-electron FLAPW results, and the necessary plane-wave energy cutoff. This quality function allows us to use a Nelder-Mead optimization algorithm on a training set of materials to optimize the input parameters of the pseudopotential construction for most of the periodic table. We control the accuracy of the resulting pseudopotentials on a test set of materials independent of the training set. We find that the automatically constructed pseudopotentials provide a good agreement with the all-electron results obtained using the FLEUR code with a plane-wave energy cutoff of approximately 60 Ry.

Schlipf, Martin

2015-01-01

339

Chaos Time Series Prediction Based on Membrane Optimization Algorithms

This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (?, m) and least squares support vector machine (LS-SVM) (?, ?) by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Li, Meng; Yi, Liangzhong; Pei, Zheng; Gao, Zhisheng

2015-01-01

340

In this paper an improved ant colony algorithm is presented and an algorithm in combination with particle swarm optimization algorithm and the improved ant colony algorithm for multi-objective flexible job shop scheduling problem are employed. The algorithm proposed in this paper includes two parts. The first part makes use of the fast convergence of PSO to search the particles optimum

Li Li; Wang Keqi; Zhou Chunnan

2010-01-01

341

A particle swarm optimization algorithm for balancing assembly lines

Purpose – The purpose of this paper is to apply particle swarm optimization (PSO) a known combinatorial optimization algorithm to multi-objective (MO) balancing of large assembly lines. Design\\/methodology\\/approach – A novel approach based on PSO is developed to tackle the simple assembly line balancing problem (SALBP), a well-known NP-hard production and operations management problem. Line balancing is considered for two-criteria

Dimitris I. Petropoulos; Andreas C. Nearchou

2011-01-01

342

Efficient and extensible algorithms for multi query optimization

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multiquery optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were

Prasan Roy; S. Seshadri; S. Sudarshan; Siddhesh Bhobe

2000-01-01

343

Efficient and Extensible Algorithms for Multi Query Optimization

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multi- query optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms

Prasan Roy; S. Seshadri; S. Sudarshan; Siddhesh Bhobe

2000-01-01

344

Faster optimal parallel prefix circuits: New algorithmic construction

Parallel prefix circuits are parallel prefix algorithms on the combinational circuit model. A prefix circuit with n inputs is depth-size optimal if its depth plus size equals 2n-2. Smaller depth implies faster computation, while smaller size implies less power consumption, less VLSI area, and less cost. To be of practical use, the depth and fan-out of a depth-size optimal prefix

Yen-chun Lin; Chin-yu Su

2005-01-01

345

Steepest Descent Algorithms for Optimization Under Unitary Matrix Constraint

In many engineering applications we deal with constrained optimization problems with respect to complex-valued matrices. This paper proposes a Riemannian geometry approach for optimization of a real-valued cost function T of complex-valued matrix argument W, under the constraint that W is an n times n unitary matrix. We derive steepest descent (SD) algorithms on the Lie group of unitary matrices

Traian E. Abrudan; Jan Eriksson; Visa Koivunen

2008-01-01

346

Seeker Optimization Algorithm for Digital IIR Filter Design

Since the error surface of digital infinite-impulse-response (IIR) filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima. In this paper, a seeker-optimization-algorithm (SOA)-based evolutionary method is proposed for digital IIR filter design. SOA is based on the concept of simulating the act of human searching in which the search direction is based

Chaohua Dai; Weirong Chen; Yunfang Zhu

2010-01-01

347

A Hybrid Search Algorithm for Porous Air Bearings Optimization

The study deals with the development of a hybrid search algorithm for efficient optimization of porous air bearings. Both the compressible Reynolds equation and Darcy's law are linearized and solved iteratively by a successive-over-relaxation method for modeling parallel-surface porous bearings. Three factors affecting the computational efficiency of the numerical model are highlighted and discussed. The hybrid optimization is performed by

Nenzi Wang; Yau-Zen Chang

2002-01-01

348

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

349

In this work, we propose an inexact interior proximal-type algorithm for solving convex second-order cone programs. This kind of problem consists of minimizing a convex function (possibly nonsmooth) over the intersection of an affine linear space with the Cartesian product of second-order cones. The proposed algorithm uses a variable metric, which is induced by a class of positive-definite matrices and

Felipe Alvarez; Julio López; C. Héctor Ramírez

2010-01-01

350

Rational function optimization using genetic algorithms

NASA Astrophysics Data System (ADS)

In the absence of either satellite ephemeris information or camera model, rational functions are introduced by many investigators as mathematical model for image to ground coordinate system transformation. The dependency of this method on many ground control points (GCPs), numerical complexity, particularly terms selection, can be regarded as the most known disadvantages of rational functions. This paper presents a mathematical solution to overcome these problems. Genetic algorithms are used as an intelligent method for optimum rational function terms selection. The results from an experimental test carried out over a test field in Iran are presented as utilizing an IKONOS Geo image. Different numbers of GCPs are fed through a variety of genetic algorithms (GAs) with different control parameter settings. Some initial constraints are introduced to make the process stable and fast. The residual errors at independent check points proved that sub-pixel accuracies can be achieved even when only seven and five GCPs are used. GAs could select rational function terms in such a way that numerical problems are avoided without the need to normalize image and ground coordinates.

Valadan Zoej, M. J.; Mokhtarzade, M.; Mansourian, A.; Ebadi, H.; Sadeghian, S.

2007-12-01

351

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

352

A multiobjective memetic algorithm based on particle swarm optimization.

In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance within the swarm. The proposed features are examined to show their individual and combined effects in MO optimization. The comparative study shows the effectiveness of the proposed MA, which produces solution sets that are highly competitive in terms of convergence, diversity, and distribution. PMID:17278557

Liu, Dasheng; Tan, K C; Goh, C K; Ho, W K

2007-02-01

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

For the cooling system of plastic injection mold affects significantly the productivity and quality of the finial products, the cooling system design is of great importance. In this paper, a hybrid approach combining particle swarm optimization (PSO) and genetic algorithms (GA) is developed to achieve the cooling system optimal design. Based on the finite element method (FEM) and the finite

Li Ren; WenXiao Zhang

2011-01-01

355

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

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

356

SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization

. Sequential quadratic programming (SQP) methods have proved highly effective forsolving constrained optimization problems with smooth nonlinear functions in the objective andconstraints. Here we consider problems with general inequality constraints (linear and nonlinear).We assume that first derivatives are available, and that the constraint gradients are sparse.We discuss an SQP algorithm that uses a smooth augmented Lagrangian merit function andmakes explicit

Philip E. Gill; Walter Murray; Michael A. Saunders

1997-01-01

357

A CONDITIONAL GAUSSIAN MARTINGALE ALGORITHM FOR GLOBAL OPTIMIZATION

is to change, at each new repetition, the location and dispersion parameters of the probability GaussianA CONDITIONAL GAUSSIAN MARTINGALE ALGORITHM FOR GLOBAL OPTIMIZATION MANUEL L. ESQUÂ´IVEL Abstract be thought to belong to the random search class but although we use Gaussian distributions at each repetition

Lisbon, University of

358

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

359

Algorithms for Noisy Problems in Gas Transmission Pipeline Optimization \\Lambda

trillion standard cubic feet of natural gas per year, representing roughly a third of worldwide consumption consider minimization of the cost of fuel and/or electric power for the compressor stations in a gasAlgorithms for Noisy Problems in Gas Transmission Pipeline Optimization \\Lambda R. G. Carter y J. M

360

Algorithms for Noisy Problems in Gas Transmission Pipeline Optimization

trillion standard cubic feet of natural gas per year, representing roughly a third of worldwide consumption minimization of the cost of fuel and/or electric power for the compressor stations in a gas pipeline networkAlgorithms for Noisy Problems in Gas Transmission Pipeline Optimization R. G. Cartery J. M

361

Optimal and Practical Algorithms for Sorting on the PDM

The Parallel Disks Model (PDM) has been proposed to alleviate the I\\/O bottle- neck that arises in the processing of massive data sets. Sorting has been extensively studied on the PDM model due to the fundamental nature of the problem - several asymptotically optimal algorithms are known for sorting. Although randomization has been frequently ex- ploited, most of the prior

Sanguthevar Rajasekaran; Sandeep Sen

2008-01-01

362

Optimal Scheduling of Booster Chlorination with Immune Algorithm

This paper describes the methodology and application of an immune algorithm (IA) scheme tailor-made for the EPANET for simultaneously optimizing the injection rates and scheduling of chlorine booster stations under the unsteady state of a water distribution network system (WDNS). The objective of this study is to initiate a total chlorination dose to satisfy the minimum and maximum required chlorine

Chien-Wei Chu; Min-Der Lin; Kang-Ting Tsai

2008-01-01

363

8. Reconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics

: the reconstruction of evolution- ary histories (phylogenies) from molecular data such as DNA sequences. Our8. Reconstructing Optimal Phylogenetic Trees: A Challenge in Experimental Algorithmics Bernard M. E an extensive study of quartet-based reconstruction algo- rithms within a parameter-rich simulation space, using

Moret, Bernard

364

GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS

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

365

A new particle swarm optimization algorithm for dynamic image clustering

In this paper, we present ACPSO a new dynamic image clustering algorithm based on particle swarm optimization. ACPSO can partition image into compact and well separated clusters without any knowledge on the real number of clusters. It uses a swarm of particles with variable number of length, which evolve dynamically using mutation operators. Experimental results on real images demonstrate that

Salima Ouadfel; Mohamed Batouche; Abdelmalik Taleb-Ahmed

2010-01-01

366

Intelligent evolutionary algorithms for large parameter optimization problems

This work proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene collector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IMOEA. Based on orthogonal experimental design, IGC uses a divide-and-conquer approach, which consists of adaptively dividing two individuals of parents

Shinn-ying Ho; Li-sun Shu; Jian-hung Chen

2004-01-01

367

Optimization flow control—I: basic algorithm and convergence

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

368

Optimal Algorithms for Well-Conditioned Nonlinear Systems of Equations

of systems of ordinary differential equations. More precisely, the numerical inte- gration of the classicalOptimal Algorithms for Well-Conditioned Nonlinear Systems of Equations Monica Bianchini, Stefano Fanelli, and Marco Gori, Fellow, IEEE AbstractÐWe propose solving nonlinear systems of equations

Fanelli, Stephen

369

Comparison of probabilistic and deterministic optimizations using genetic algorithms

This paper describes an application of genetic algorithms to deterministic and probabilistic (reliability-based) optimization of damping augmentation for a truss structure. The probabilistic formulation minimizes the probability of exceeding upper limits on the magnitude of the dynamic response of the structure due to uncertainties in the properties of the damping devices. The corresponding deterministic formulation maximizes a safety margin with

E. Ponslet; G. Maglaras; R. T. Haftka; E. Nikolaidis; H. H. Cudney

1995-01-01

370

Parallel evolutionary algorithms for optimization problems in aerospace engineering

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

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

2002-01-01

371

Optimal Sleep-Wakeup Algorithms for Barriers of Wireless Sensors

Optimal Sleep-Wakeup Algorithms for Barriers of Wireless Sensors Santosh Kumar Ten H. Lai Marc E,posner.1,sinha.43}@osu.edu Abstract-- The problem of sleep wakeup has been extensively studied the sleep- wakeup problem is NP-Hard for this model, several heuristics ex- ist. For the model of barrier

Sinha, Prasun

372

Allocating optimal index positions on tool magazines using genetic algorithms

This paper presents an optimisation system software developed for the determination of optimal index positions of cutting tools on the automatic tool changer (ATC) or turret magazine of CNC machine tools. Position selection is performed using a genetic algorithm (GA) which takes a list of cutting tools assigned to certain machining operations together with total number of index positions available

Türkay Dereli; I. Hüseyin Filiz

2000-01-01

373

A Niched Pareto Genetic Algorithm for Multiobjective Optimization

Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination

Jeffrey Horn; Nicholas Nafpliotis; David E. Goldberg

1994-01-01

374

THE ACO/F-RACE ALGORITHM FOR COMBINATORIAL OPTIMIZATION

optimization and on F-Race. The latter is a general method for the comparison of a number of candidates under uncertainty with the empirical estimation approach. F-Race [6, 5] is an algorithm for tuning metaheuristics.1 In the present paper, F-Race is used in an original way as a component of an ant

Libre de Bruxelles, UniversitÃ©

375

GLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION

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

Lunds Universitet

376

An Optimal Algorithm for Intersecting Line Segments in the Plane

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

377

An Adaptive Penalty Approach for Constrained GeneticAlgorithm Optimization

). These include: 1. Rejection of infeasible solutions (the death penalty). 2. Using a mapping function so that allAn Adaptive Penalty Approach for Constrained GeneticÂAlgorithm Optimization Khaled Rasheed shehata@cs.rutgers.edu ABSTRACT In this paper we describe a new adaptive penalty approach for handling

Rasheed, Khaled

378

Numerical Optimization Algorithms and Software for Systems Biology

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

379

Local search genetic algorithm for optimal design of reliable networks

This paper presents a genetic algorithm (GA) with specialized encoding, initialization and local search operators to optimize the design of communication network topologies. This NP-hard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible networks. Another complication is that the fitness function involves calculating the all-terminal reliability of the network, a calculation that

Berna Dengiz; Fulya Altiparmak; Alice E. Smith

1997-01-01

380

A genetic algorithm approach in interface and surface structure optimization

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.

Zhang, Jian

2010-05-16

381

Fast Optimal Load Balancing Algorithms for 1D Partitioning

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

Pinar, Ali; Aykanat, Cevdet

2002-12-09

382

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

383

Global structual optimizations of surface systems with a genetic algorithm

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

384

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

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

Ioannis G. Akrotirianakis; Christodoulos A. Floudas

2004-01-01

385

An unsupervised self-optimizing gene clustering algorithm.

We have devised a gene-clustering algorithm that is completely unsupervised in that no parameters need be set by the user, and the clustering of genes is self-optimizing to yield the set of clusters that minimizes within-cluster distance and maximizes between-cluster distance. This algorithm was implemented in Java, and tested on a randomly selected 200-gene subset of 3000 genes from cell-cycle data in S. cerevisiae. AlignACE was used to evaluate the resulting optimized cluster set for upstream cis-regulons. The optimized cluster set was found to be of comparable quality to cluster sets obtained by two established methods (complete linkage and k-means), even when provided with only a small, randomly selected subset of the data (200 vs 3000 genes), and with absolutely no supervision. MAP and specificity scores of the highest ranking motifs identified in the largest clusters were comparable. PMID:12463911

Schachter, Asher D.; Kohane, Isaac S.

2002-01-01

386

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

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 e...

Canto, J; Martinez-Gomez, E; 10.1051/0004-6361/200911740

2009-01-01

387

Optimal graph algorithms on a fixed-size linear array

Parallel algorithms for computing the minimum spanning tree of a weighted undirected graph, and the bridges and articulation points of an undirected graphs on a fixed-size linear array of processors are presented. For a graph of n vertices, the algorithms operate on a linear array of rho processors and require O(n/sup 2//rho) time for all rho, 1 less than or equal to rho -- n. In particular, using n processors the algorithms require O(n) time which is optimal on this model. The paper describes two approaches to limit the communication requirements for solving the problems. The first is a divide-and-conquer strategy applied to Sollin's algorithm for finding the minimum spanning tree of a graph. The second uses a novel data-reduction technique that constructs an auxiliary graph with no more that 2n - 2 edges, whose bridges and articulation points are the bridges and articulation points of the original graph.

Doshi, K.A.; Varman, P.J.

1987-04-01

388

Optimization of circuits using a constructive learning algorithm

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

389

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

390

Optimization of image processing algorithms on mobile platforms

NASA Astrophysics Data System (ADS)

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

Poudel, Pramod; Shirvaikar, Mukul

2011-03-01

391

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 satisfied 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

392

Hierarchical artificial bee colony algorithm for RFID network planning optimization.

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

393

MULTIOBJECTIVE OPTIMIZATION OF HEAT TRANSFER PLANT USING DECISION TABLE CONTROLER AND GENETIC 74 029 Abstract - A genetic algorithm based procedure for di- rect decision table adjusting by means of genetic algorithm. The proposed evolutional optimizing procedure of the multiobjective

Coello, Carlos A. Coello

394

A Multi-Objective Ant Colony Optimization Algorithm for Infrastructure Routing

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

McDonald, Walter

2012-07-16

395

Nonconvex compressed sensing by nature-inspired optimization algorithms.

The l 0 regularized problem in compressed sensing reconstruction is nonconvex with NP-hard computational complexity. Methods available for such problems fall into one of two types: greedy pursuit methods and thresholding methods, which are characterized by suboptimal fast search strategies. Nature-inspired algorithms for combinatorial optimization are famous for their efficient global search strategies and superior performance for nonconvex and nonlinear problems. In this paper, we study and propose nonconvex compressed sensing for natural images by nature-inspired optimization algorithms. We get measurements by the block-based compressed sampling and introduce an overcomplete dictionary of Ridgelet for image blocks. An atom of this dictionary is identified by the parameters of direction, scale and shift. Of them, direction parameter is important for adapting to directional regularity. So we propose a two-stage reconstruction scheme (TS_RS) of nature-inspired optimization algorithms. In the first reconstruction stage, we design a genetic algorithm for a class of image blocks to acquire the estimation of atomic combinations in all directions; and in the second reconstruction stage, we adopt clonal selection algorithm to search better atomic combinations in the sub-dictionary resulted by the first stage for each image block further on scale and shift parameters. In TS_RS, to reduce the uncertainty and instability of the reconstruction problems, we adopt novel and flexible heuristic searching strategies, which include delicately designing the initialization, operators, evaluating methods, and so on. The experimental results show the efficiency and stability of the proposed TS_RS of nature-inspired algorithms, which outperforms classic greedy and thresholding methods. PMID:25148677

Liu, Fang; Lin, Leping; Jiao, Licheng; Li, Lingling; Yang, Shuyuan; Hou, Biao; Ma, Hongmei; Yang, Li; Xu, Jinghuan

2015-05-01

396

Approximating convex Pareto surfaces in multiobjective radiotherapy planning

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

397

Convex Dynamic Programming and its Applications to Hydroelectric Energy

In this paper, the concept of convex dynamic programming is presented. Sufficient condition of the convexity is found for a dynamic programming model of reservoir dispatching. Based on the convexity property, an algorithm is proposed and implemented for the Zhexi Hydropower Station in Hunan Province, People's Republic of China. As a result, annual power output in Zhexi Hydropower Station is

Yong-Chuan Zhang; Dalen Chiang

1985-01-01

398

Convex Dynamic Programming and Its Applications to Hydroelectric Energy

In this paper, the concept of convex dynamic programming is presented. Sufficient condition of the convexity is found for a dynamic programming model of reservoir dispatching. Based on the convexity property, an algorithm is proposed and implemented for the Zhexi Hydropower Station in Hunan Province, People's Republic of China. As a result, annual power output in Zhexi Hydropower Station is

Yong-Chuan Zhang; Dalen T. Chiang

1985-01-01

399

Parallel evolutionary algorithms for optimization problems in aerospace engineering

NASA Astrophysics Data System (ADS)

This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the notion of interconnected sub-populations evolving independently. Previous studies have shown the advantages of introducing a multi-layered hierarchical topology in parallel GAs. Such a topology allows the use of multiple models for optimization problems, and shows that it is possible to mix fast low-fidelity models for exploration and expensive high-fidelity models for exploitation. Finally, a new class of multi-objective optimizers mixing HGAs and Nash Game Theory is defined. These methods are tested for solving design optimization problems in aerodynamics. A parallel version of this approach running a cluster of PCs demonstrate the convergence speed up on an inverse nozzle problem and a high-lift problem for a multiple element airfoil.

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

2002-12-01

400

Fuel management optimization using genetic algorithms and code independence

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

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

1994-12-31

401

Preliminary flight evaluation of an engine performance optimization algorithm

NASA Technical Reports Server (NTRS)

A performance seeking control (PSC) algorithm has undergone initial flight test evaluation in subsonic operation of a PW 1128 engined F-15. This algorithm is designed to optimize the quasi-steady performance of an engine for three primary modes: (1) minimum fuel consumption; (2) minimum fan turbine inlet temperature (FTIT); and (3) maximum thrust. The flight test results have verified a thrust specific fuel consumption reduction of 1 pct., up to 100 R decreases in FTIT, and increases of as much as 12 pct. in maximum thrust. PSC technology promises to be of value in next generation tactical and transport aircraft.

Lambert, H. H.; Gilyard, G. B.; Chisholm, J. D.; Kerr, L. J.

1991-01-01

402

Generalized monotonically convergent algorithms for solving quantum optimal control problems

NASA Astrophysics Data System (ADS)

A wide range of cost functionals that describe the criteria for designing optimal pulses can be reduced to two basic functionals by the introduction of product spaces. We extend previous monotonically convergent algorithms to solve the generalized pulse design equations derived from those basic functionals. The new algorithms are proved to exhibit monotonic convergence. Numerical tests are implemented in four-level model systems employing stationary and/or nonstationary targets in the absence and/or presence of relaxation. Trajectory plots that conveniently present the global nature of the convergence behavior show that slow convergence may often be attributed to "trapping" and that relaxation processes may remove such unfavorable behavior.

Ohtsuki, Yukiyoshi; Turinici, Gabriel; Rabitz, Herschel

2004-03-01

403

Generalized monotonically convergent algorithms for solving quantum optimal control problems.

A wide range of cost functionals that describe the criteria for designing optimal pulses can be reduced to two basic functionals by the introduction of product spaces. We extend previous monotonically convergent algorithms to solve the generalized pulse design equations derived from those basic functionals. The new algorithms are proved to exhibit monotonic convergence. Numerical tests are implemented in four-level model systems employing stationary and/or nonstationary targets in the absence and/or presence of relaxation. Trajectory plots that conveniently present the global nature of the convergence behavior show that slow convergence may often be attributed to "trapping" and that relaxation processes may remove such unfavorable behavior. PMID:15267426

Ohtsuki, Yukiyoshi; Turinici, Gabriel; Rabitz, Herschel

2004-03-22

404

Graph Implementations for Nonsmooth Convex Programs

programming, conic optimization, nondifferentiable functions. 1 Introduction It is well known that convex, as well as for certain standard forms such as semidefinite programs (SDPs), that are efficient in bothGraph Implementations for Nonsmooth Convex Programs Michael C. Grant I and Stephen P. Boyd 2 1

405

Loopy Substructural Local Search for the Bayesian Optimization Algorithm

NASA Astrophysics Data System (ADS)

This paper presents a local search method for the Bayesian optimization algorithm (BOA) based on the concepts of substructural neighborhoods and loopy belief propagation. The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the topology of the neighborhoods explored in local search. On the other hand, belief propagation in graphical models is employed to find the most suitable configuration of conflicting substructures. The results show that performing loopy substructural local search (SLS) in BOA can dramatically reduce the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.

Lima, Claudio F.; Pelikan, Martin; Lobo, Fernando G.; Goldberg, David E.

406

Blind Data Classification Using Hyper-Dimensional Convex Polytopes

A blind classification algorithm is presented that uses hyper- dimensional geometric algorithms to locate a hypothesis, in the form of a convex polytope or hyper-sphere. The convex polytope geometric model provides a well-fitted class representation that does not require training with instances of opposing classes. Further, the classification algorithm creates models for as many training classes of data as are

Brent T. Mcbride; Gilbert L. Peterson

2004-01-01

407

Most of the optimization problems in the real world have constraints. In recent years, evolutionary algorithms caught a lot of researchers' attention for solving constrained optimization problems. Infeasible individuals are often underrated by most of the current evolutionary algorithms when evolutionary algorithms are used for solving constraint optimization problems. This paper proposes an approach to balance the feasible and infeasible

Ming Yuchi; Jong-Hwan Kim

2004-01-01

408

Non-Uniform search domain based Genetic algorithm for the optimization of real time FFT Processor

Non-Uniform search domain based Genetic algorithm for the optimization of real time FFT Processor for optimization of word length coefficients in a pipelined FFT processor. The algorithm optimizes memory and buses data and coefficients in real time pipelined FFT processor architectures. The algorithm is specially

Arslan, Tughrul

409

A Honey-bee Mating Optimization Algorithm for Educational Timetabling Problems

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

Qu, Rong

410

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

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

411

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

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

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

2007-01-01

412

Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework

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

413

Parallel Algorithms for Graph Optimization using Tree Decompositions

Although many 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 required dynamic programming tables and excessive running times 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 maximum weighted independent set. 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.

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

2013-01-01

414

Genetic Algorithm Application in Optimization of Wireless Sensor Networks

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

415

Quantum algorithm for molecular properties and geometry optimization.

Quantum computers, if available, could substantially accelerate quantum simulations. We extend this result to show that the computation of molecular properties (energy derivatives) could also be sped up using quantum computers. We provide a quantum algorithm for the numerical evaluation of molecular properties, whose time cost is a constant multiple of the time needed to compute the molecular energy, regardless of the size of the system. Molecular properties computed with the proposed approach could also be used for the optimization of molecular geometries or other properties. For that purpose, we discuss the benefits of quantum techniques for Newton's method and Householder methods. Finally, global minima for the proposed optimizations can be found using the quantum basin hopper algorithm, which offers an additional quadratic reduction in cost over classical multi-start techniques. PMID:20001019

Kassal, Ivan; Aspuru-Guzik, Alán

2009-12-14

416

Parallel Algorithms for Graph Optimization using Tree Decompositions

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

417

Quantum algorithm for molecular properties and geometry optimization

NASA Astrophysics Data System (ADS)

Quantum computers, if available, could substantially accelerate quantum simulations. We extend this result to show that the computation of molecular properties (energy derivatives) could also be sped up using quantum computers. We provide a quantum algorithm for the numerical evaluation of molecular properties, whose time cost is a constant multiple of the time needed to compute the molecular energy, regardless of the size of the system. Molecular properties computed with the proposed approach could also be used for the optimization of molecular geometries or other properties. For that purpose, we discuss the benefits of quantum techniques for Newton's method and Householder methods. Finally, global minima for the proposed optimizations can be found using the quantum basin hopper algorithm, which offers an additional quadratic reduction in cost over classical multi-start techniques.

Kassal, Ivan; Aspuru-Guzik, Alán

2009-12-01

418

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

419

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

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

A. Kaveh; S. Talatahari

2009-01-01

420

Algorithms for optimal introduction of redundant logic for timing and area optimization

In this paper we study algorithms for systematically introducing redundant into a circuit for timing and formulations of the optimization problem. First we study a logic-level versions of the problem and show that they are NP-hard, but not in the strong sense. We then propose pseudo-polynomial algorithms for these problems. Second, we introduce a layout level problem formulation in which

John Lillis; Chung-Kuan Cheng; T.-T. Y. Lin

1996-01-01

421

Optimizing phase-estimation algorithms for diamond spin magnetometry

NASA Astrophysics Data System (ADS)

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

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

2014-07-01

422

Dynamic Optimal Design of Groundwater Remediation Using Genetic Algorithms

The use of genetic algorithms for the dynamic optimal design of pump-and-treat groundwater remediation systems is demonstrated\\u000a through two new dynamic formulations. In the first formulation in which the contaminant sorption was assumed to be in equilibrium,\\u000a the lengths of management periods were decision variables. The second formulation assumed a pulsed pumping approach to remove\\u000a a contaminant with mass-transfer-limited sorption.

Amy Chan Hilton; Aysegul Aksoy; Teresa B. Culver

423

Water Distribution System Optimization Using Genetic Simulated Annealing Algorithm

\\u000a Water supply system optimization makes use of the latest advances in hybrid genetic algorithm to automatically determine the\\u000a least-cost pump operation for each pump station in large-scale water distribution system while satisfying simplified hydraulic\\u000a performance requirements. Calibration results of the original model were pretty good. The comparison results show that the\\u000a difference between the simplified and the original mode simulation

Shihu. Shu

2011-01-01

424

Parent-centric differential evolution algorithm for global optimization problems

Differential evolution (DE) is a population based evolutionary search algorithm widely used for solving optimization problems.\\u000a In the present article we investigate the application of parent-centric approach on the performance of classical DE, without\\u000a tampering with the basic structure of DE. The parent-centric approach is embedded in the mutation phase of DE. We propose\\u000a two versions of (DE) called differential

Millie Pant; Musrrat Ali; V. P. Singh

2009-01-01

425

Genetic Algorithms applications to optimization and system identification

. Optimal Results of the Second Model; Abs. Viscosity vs. Rot. Speed. . . . . . . . 20 5. The Frequency Spectrum of the Real System. . . . 25 6 The Frequency Spectrum of the Estimated System by GA. . . . . . . 25 vlu LIST OF TABLES TABLE Page 1... the strings of first generation. Start Produce 10 strings Fitness- evaluation Ranking Reproduction Selecting Reproducing Crossover Mutation Fig. 1 Flow Chart of Genetic Algorithms Program The second step is for the 10 binary strings to go through...

Lin, Yun-Jeng

1998-01-01

426

An Optimal Algorithm for Distributed System Level Diagnosis

A system consisting of n identical processors connected by links in which some processors could be faulty is considered. Initially each unit knows only its own i.d. and the i.d.'s of its immediate neighbors; no unit has any global knowledge about the system. An optimal algorithm for system level diagnosis in such a system that is based on the transmission

Anindo Bagchi; S. Louis Hakimi

1991-01-01

427

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

428

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

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

Kong, Adams Wai-Kin

2013-03-01

429

NASA Astrophysics Data System (ADS)

SAR Tomography is the extension of the conventional interferometric radar signal processing, extended in the height dimension. In order to improve the vertical resolution with respect to the classical Fourier methods, high resolution approaches, based on the Convex Optimization (CVX), has been implemented. This methods recast in the Compressed Sensing (CS) framework that optimize tomographic smooth profiles via atomic decomposition, in order to obtain sparsity. The optimum solution has been estimated by Interior Point Methods (IPM). The problem for such kind of signal processing is that the tomographic phase information may be suppressed and only the optimized energy information is available. In this paper we propose a method in order to estimate an optimized spectra and phase information projecting each vector components of each tomographic resolution cell spanned in the real and the imaginary component. The tomographic solutions has been performed by processing multi-baseline SAR datasets, in a full polarimetric mode, acquired by a portable small Continuous Wave (CW) radar in the X band.

Biondi, Filippo; Sarri, Antonio; Fiori, Luca; Dell'Omodarme, Kevin

2014-10-01

430

An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel

NASA Astrophysics Data System (ADS)

This study employed Accelerated Particle Swarm Optimization (APSO) algorithm to optimize the machining parameters that lead to a maximum Material Removal Rate (MRR), minimum surface roughness and minimum kerf width values for Wire Electrical Discharge Machining (WEDM) of AISI D3 die-steel. Four machining parameters that are optimized using APSO algorithm include Pulse on-time, Pulse off-time, Gap voltage, Wire feed. The machining parameters are evaluated by Taguchi's L9 Orthogonal Array (OA). Experiments are conducted on a CNC WEDM and output responses such as material removal rate, surface roughness and kerf width are determined. The empirical relationship between control factors and output responses are established by using linear regression models using Minitab software. Finally, APSO algorithm, a nature inspired metaheuristic technique, is used to optimize the WEDM machining parameters for higher material removal rate and lower kerf width with surface roughness as constraint. The confirmation experiments carried out with the optimum conditions show that the proposed algorithm was found to be potential in finding numerous optimal input machining parameters which can fulfill wide requirements of a process engineer working in WEDM industry.

Muthukumar, V.; Suresh Babu, A.; Venkatasamy, R.; Senthil Kumar, N.

2015-01-01

431

Managing and learning with multiple models: Objectives and optimization algorithms

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

432

Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. The honey-bees mating process may also be considered as a typical swarm-based approach to optimization,

Omid Bozorg Haddad; Abbas Afshar; Miguel A. Mariño

2006-01-01

433

In this article, a hybrid optimization method has been proposed consisting of Adaptive Genetic Algorithms (AGAs) and Constrained Nonlinear Programming (NLP) to solve the problems of performance optimization of circular array antenna consist of parallel center feeding short dipoles elements with two complex nonlinear optimization problems. In the first problem, the hybrid optimization algorithm is used to reduce the value

Ali Abdulhadi Noaman; Abdul Kareem S. Abdallah; Ramzy S. Ali

2010-01-01

434

Automatic algorithms for completeness-optimization of Gaussian basis sets.

We present the generic, object-oriented C++ implementation of the completeness-optimization approach (Manninen and Vaara, J. Comput. Chem. 2006, 27, 434) in the freely available ERKALE program, and recommend the addition of basis set stability scans to the completeness-optimization procedure. The design of the algorithms is independent of the studied property, the used level of theory, as well as of the role of the optimized basis set: the procedure can be used to form auxiliary basis sets in a similar fashion. This implementation can easily be interfaced with various computer programs for the actual calculation of molecular properties for the optimization, and the calculations can be trivially parallelized. Routines for general and segmented contraction of the generated basis sets are also included. The algorithms are demonstrated for two properties of the argon atom--the total energy and the nuclear magnetic shielding constant--and they will be used in upcoming work for generation of cost-efficient basis sets for various properties. PMID:25487276

Lehtola, Susi

2015-02-15

435

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

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

Sve?ko, Rajko

2014-01-01

436

Coil optimization for electromagnetic levitation using a genetic like algorithm

NASA Astrophysics Data System (ADS)

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

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

2013-06-01

437

Magnet shape optimization of brushless machine by Self-Organizing Migrating Algorithm

The paper deals with AC motor optimization. Optimized machine is three phases 14kW Surface Mount Permanent Magnet (SMPM) machine intended to work with servo amplifier. The optimization is based on Self Organizing Migrating Algorithm - SOMA with strategy \\

Jiri Kurfurst; Jiri Duron; Miroslav Skalka; Marcel Janda; Cestmir Ondrusek

2011-01-01

438

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

439

Particle Swarm Optimization algorithms for geophysical inversion, practical hints

NASA Astrophysics Data System (ADS)

PSO is a stochastic optimization technique that has been successfully used in many different engineering fields. PSO algorithm can be physically interpreted as a stochastic damped mass-spring system (Fernandez Martinez and Garcia Gonzalo 2008). Based on this analogy we present a whole family of PSO algorithms and their respective first order and second order stability regions. Their performance is also checked using synthetic functions (Rosenbrock and Griewank) showing a degree of ill-posedness similar to that found in many geophysical inverse problems. Finally, we present the application of these algorithms to the analysis of a Vertical Electrical Sounding inverse problem associated to a seawater intrusion in a coastal aquifer in South Spain. We analyze the role of PSO parameters (inertia, local and global accelerations and discretization step), both in convergence curves and in the a posteriori sampling of the depth of an intrusion. Comparison is made with binary genetic algorithms and simulated annealing. As result of this analysis, practical hints are given to select the correct algorithm and to tune the corresponding PSO parameters. Fernandez Martinez, J.L., Garcia Gonzalo, E., 2008a. The generalized PSO: a new door to PSO evolution. Journal of Artificial Evolution and Applications. DOI:10.1155/2008/861275.

Garcia Gonzalo, E.; Fernandez Martinez, J.; Fernandez Alvarez, J.; Kuzma, H.; Menendez Perez, C.

2008-12-01

440

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

441

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

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

White, Tony

442

Bird mating optimizer: An optimization algorithm inspired by bird mating strategies

NASA Astrophysics Data System (ADS)

Thanks to their simplicity and flexibility, evolutionary algorithms (EAs) have attracted significant attention to tackle complex optimization problems. The underlying idea behind all EAs is the same and they differ only in technical details. In this paper, we propose a novel version of EAs, bird mating optimizer (BMO), for continuous optimization problems which is inspired by mating strategies of bird species during mating season. BMO imitates the behavior of bird species metaphorically to breed broods with superior genes for designing optimum searching techniques. On a large set of unimodal and multimodal benchmark functions, BMO represents a competitive performance to other EAs.

Askarzadeh, Alireza

2014-04-01

443

Award DE-FG02-04ER52655 Final Technical Report: Interior Point Algorithms for Optimization Problems

Over the period of this award we developed an algorithmic framework for constraint reduction in linear programming (LP) and convex quadratic programming (QP), proved convergence of our algorithms, and applied them to a variety of applications, including entropy-based moment closure in gas dynamics.

O'Leary, Dianne P. [Univ. of Maryland] [Univ. of Maryland; Tits, Andre [Univ. of Maryland] [Univ. of Maryland

2014-04-03

444

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

Dantzig, G.B.

1992-10-01

445

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

Dantzig, G.B.

1992-10-01

446

Genetic algorithm parameter optimization: applied to sensor coverage

NASA Astrophysics Data System (ADS)

Genetic Algorithms are powerful tools, which when set upon a solution space will search for the optimal answer. These algorithms though have some associated problems, which are inherent to the method such as pre-mature convergence and lack of population diversity. These problems can be controlled with changes to certain parameters such as crossover, selection, and mutation. This paper attempts to tackle these problems in GA by having another GA controlling these parameters. The values for crossover parameter are: one point, two point, and uniform. The values for selection parameters are: best, worst, roulette wheel, inside 50%, outside 50%. The values for the mutation parameter are: random and swap. The system will include a control GA whose population will consist of different parameters settings. While this GA is attempting to find the best parameters it will be advancing into the search space of the problem and refining the population. As the population changes due to the search so will the optimal parameters. For every control GA generation each of the individuals in the population will be tested for fitness by being run through the problem GA with the assigned parameters. During these runs the population used in the next control generation is compiled. Thus, both the issue of finding the best parameters and the solution to the problem are attacked at the same time. The goal is to optimize the sensor coverage in a square field. The test case used was a 30 by 30 unit field with 100 sensor nodes. Each sensor node had a coverage area of 3 by 3 units. The algorithm attempts to optimize the sensor coverage in the field by moving the nodes. The results show that the control GA will provide better results when compared to a system with no parameter changes.

Sahin, Ferat; Abbate, Giuseppe

2004-08-01

447

Optimization of Offset Assignment Problems for DSP with Quasi-PSO Algorithm

?Abstract?Optimal design is significant when,the DSP software is designed by C programming language. A number of different algorithms for optimized offset assignmentin DSPc ode generation aredeveloped recently,which ai m atco nstructing a layout oflocal variablesi nme mory, so that the addresses of variables ,can be ,computed efficiently. This paper presents a quasi-PSO optimization algorithm integrating PSO algorithm and genetic algorithm.

SUN Dong-yan; TONG Ji-gang; ZHAO Jia-xiang; CHEN Zeng-qiang

2008-01-01

448

NASA Astrophysics Data System (ADS)

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

Singh, R.; Verma, H. K.

2013-12-01

449

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

450

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

451

This paper presents a new memetic algorithm, which is based on the concepts of genetic algorithms (GAs), particle swarm optimization\\u000a (PSO) and greedy randomized adaptive search procedure (GRASP), for optimally clustering N objects into K clusters. The proposed\\u000a algorithm is a two phase algorithm which combines a memetic algorithm for the solution of the feature selection problem and\\u000a a GRASP

Yannis Marinakis; Magdalene Marinaki; Nikolaos F. Matsatsinis; Constantin Zopounidis

2009-01-01

452

An optimal algorithm for computing all subtree repeats in trees

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. PMID:24751873

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

2014-01-01

453

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

454

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.

David Joiner

455

Optimizing phase estimation algorithms for diamond spin magnetometry

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

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

2014-07-26

456

Generalized Particle Swarm Algorithm for HCR Gearing Geometry Optimization

NASA Astrophysics Data System (ADS)

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

2012-12-01

457

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

458

Previous work in antenna optimization has primarily focused on applications of optimization algorithms in conjunction with problem-specific or semi-analytic tools. However, previous developments in fast algorithms now offer the possibility of designs and moreover allow for full flexibility in material specification across three dimensions. As an example, this paper combines genetic algorithms (GA) and simulated annealing (SA) with fast hybrid

Zhifang Li; Yunus E. Erdemli; John L. Volakis; Panos Y. Papalambros

2002-01-01

459

Optimal CostSensitive Distributed Minimum Spanning Tree Algorithm Teresa Przytycka 1

Optimal CostÂSensitive Distributed Minimum Spanning Tree Algorithm Teresa Przytycka 1 University. We present a distributed Minimum Cost Spanning tree algorithm that is optimal with respect algorithm that finds a minimum spanning tree in a weighted connected network of processors. A network

Higham, Lisa

460

Optimal KNN Positioning Algorithm via Theoretical Accuracy Criterion in WLAN Indoor Environment

This paper proposes the optimal K nearest neighbors (KNN) positioning algorithm via theoretical accuracy criterion (TAC) in wireless LAN (WLAN) indoor environment. As far as we know, although the KNN algorithm is widely utilized as one of the typical distance dependent positioning algorithms, the optimal selection of neighboring reference points (RPs) involved in KNN has not been significantly analyzed. Therefore,

Yubin Xu; Mu Zhou; Weixiao Meng; Lin Ma

2010-01-01

461

Optimized implementation of real-time image processing algorithms on field programmable gate arrays

We present the algorithm architecture “adequation” methodology for the optimized implementation of real-time image processing algorithms on field programmable gate arrays. This methodology is based on a single factorized graphs model, used from the algorithm specification down to the architecture implementation, through optimizations expressed in terms of defactorization transformations. A simple image processing example is presented to illustrate the methodology

AILTON F. DIAS; CHRISTOPHE LAVARENNE; MOHAMED AKIL; YVES SOREL

1998-01-01

462

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

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

Kaya, Yalcin

463

Gradient, Non-Gradient and Hybrid Algorithms for Optimizing 3D Forging Sequences with Uncertainties

in order to minimize the potential of fold formation. Keywords: Optimization Algorithm, Response Surface be substituted to the exact evaluations of the objective function within costly global algorithmsGradient, Non-Gradient and Hybrid Algorithms for Optimizing 3D Forging Sequences with Uncertainties

Paris-Sud XI, UniversitÃ© de

464

The study of water supply network optimization based on the immune mechanism of ant colony algorithm

NASA Astrophysics Data System (ADS)

For ant colony algorithm to search for a long time, easy to occur Stagnant phenomenon and sink into local most superior defects, and puts forward a kind of immune mechanism of the ant colony algorithm, and the algorithm is applied to typical water supply network in the combinatorial optimization problem. Combined with water supply network for example problem using respectively based on immune mechanisms of the ant colony algorithm and genetic algorithm and the basic ant colony algorithm for example to analysis. The results show that the improved algorithm is easier to realize the global optimal solution, and high efficiency, the optimization algorithm is better than other traditional optimization for solving water distribution network ability. So demonstrate the effectiveness of the algorithm.

Wang, Zongjiang

2013-03-01

465

A comparison of three optimization algorithms for intensity modulated radiation therapy.

In intensity modulated treatment techniques, the modulation of each treatment field is obtained using an optimization algorithm. Multiple optimization algorithms have been proposed in the literature, e.g. steepest descent, conjugate gradient, quasi-Newton methods to name a few. The standard optimization algorithm in our in-house inverse planning tool KonRad is a quasi-Newton algorithm. Although this algorithm yields good results, it also has some drawbacks. Thus we implemented an improved optimization algorithm based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) routine. In this paper the improved optimization algorithm is described. To compare the two algorithms, several treatment plans are optimized using both algorithms. This included photon (IMRT) as well as proton (IMPT) intensity modulated therapy treatment plans. To present the results in a larger context the widely used conjugate gradient algorithm was also included into this comparison. On average, the improved optimization algorithm was six times faster to reach the same objective function value. However, it resulted not only in an acceleration of the optimization. Due to the faster convergence, the improved optimization algorithm usually terminates the optimization process at a lower objective function value. The average of the observed improvement in the objective function value was 37%. This improvement is clearly visible in the corresponding dose-volume-histograms. The benefit of the improved optimization algorithm is particularly pronounced in proton therapy plans. The conjugate gradient algorithm ranked in between the other two algorithms with an average speedup factor of two and an average improvement of the objective function value of 30%. PMID:18705611

Pflugfelder, Daniel; Wilkens, Jan J; Nill, Simeon; Oelfke, Uwe

2008-01-01

466

An optimized algorithm for detecting and annotating regional differential methylation

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

2013-01-01

467

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

468

Topology-changing shape optimization with the genetic algorithm

NASA Astrophysics Data System (ADS)

The goal is to take a traditional shape optimization problem statement and modify it slightly to allow for prescribed changes in topology. This modification enables greater flexibility in the choice of parameters for the topology optimization problem, while improving the direct physical relevance of the results. This modification involves changing the optimization problem statement from a nonlinear programming problem into a form of mixed-discrete nonlinear programing problem. The present work demonstrates one possible way of using the Genetic Algorithm (GA) to solve such a problem, including the use of "masking bits" and a new modification to the bit-string affinity (BSA) termination criterion specifically designed for problems with "masking bits." A simple ten-bar truss problem proves the utility of the modified BSA for this type of problem. A more complicated two dimensional bracket problem is solved using both the proposed approach and a more traditional topology optimization approach (Solid Isotropic Microstructure with Penalization or SIMP) to enable comparison. The proposed approach is able to solve problems with both local and global constraints, which is something traditional methods cannot do. The proposed approach has a significantly higher computational burden --- on the order of 100 times larger than SIMP, although the proposed approach is able to offset this with parallel computing.

Lamberson, Steven E., Jr.

469

Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.

This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500

Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo

2015-01-01

470

Optimization of reconstruction algorithms using Monte Carlo simulation

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

Hanson, K.M.

1989-01-01

471

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

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

2013-01-01

472

AN INTERIOR POINT ALGORITHM FOR SOLVING LINEAR OPTIMIZATION OVER THE EFFICIENT SET PROBLEMS

The efficient set of a multiple objective linear programming problem is usually nonconvex, hence linear optimization over the efficient set problem is classified as a nonlinear optimization problem. This paper presents a modified interior point algorithm for solving linear optimization over the efficient set problems. Using computational experiments, we show that the modified algorithm provides an effective and accurate approach

Wei-Tai Weng; Ue-Pyng Wen

2001-01-01

473

In-Space Radiator Shape Optimization using Genetic Algorithms

NASA Technical Reports Server (NTRS)

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

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

2006-01-01

474

Genetic Algorithm for Structural Optimization of Tubular Nanostructures

NASA Astrophysics Data System (ADS)

Why can metals or oxides form nanotubes, sometimes even chiral ones? How could silicon, which has little or no propensity for sp2 hybridization, form nanotubes akin with the well understood carbon nanotubes in which the atoms are unequivocally sp2 hybridized? It would be perhaps beneficial, if not expected, for the theory to step up to the plate and engage in the discovery of credible growth mechanisms and atomic structures for the tubular and multi-shell structures that can determine future directions in nanoscience and nanotechnology. In an effort to contribute to answering these questions, we present here a global optimization method designed specifically for tubular structures. Due to the recent success of the genetic algorithms in elucidating structures of 1- and 2-dimensional nanoscale materials, we base our optimization procedure on the same evolutionary principles. We have found that the cross-over operations based on planar cuts (which were so successful previously) are not sufficient to ensure convergence to lowest energy structures, and design new ones. The application of the new and more diverse cross-over operations has resulted in converged structures for different materials, which provides confidence in pursuing the application of genetic algorithm for finding the structures of new tubular materials.

Davies, Teresa E. B.; Popa, Mihail M.; Ciobanu, Cristian V.

2007-11-01

475

LPS auto-calibration algorithm with predetermination of optimal zones.

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. PMID:22346649

Ruiz, Francisco Daniel; Ureña, Jesús; García, Juan C; Jiménez, Ana; Hernández, Alvaro; García, Juan J

2011-01-01

476

LPS Auto-Calibration Algorithm with Predetermination of Optimal Zones

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. PMID:22346649

Ruiz, Francisco Daniel; Ureña, Jesús; García, Juan C.; Jiménez, Ana; Hernández, Álvaro; García, Juan J.

2011-01-01

477

In protein-ligand docking, an optimization algorithm is used to find the best binding pose of a ligand against a protein target. This algorithm plays a vital role in determining the docking accuracy. To evaluate the relative performance of different optimization algorithms and provide guidance for real applications, we performed a comparative study on six efficient optimization algorithms, containing two evolutionary algorithm (EA)-based optimizers (LGA, DockDE) and four particle swarm optimization (PSO)-based optimizers (SODock, varCPSO, varCPSO-ls, FIPSDock), which were implemented into the protein-ligand docking program AutoDock. We unified the objective functions by applying the same scoring function, and built a new fitness accuracy as the evaluation criterion that incorporates optimization accuracy, robustness, and efficiency. The varCPSO and varCPSO-ls algorithms show high efficiency with fast convergence speed. However, their accuracy is not optimal, as they cannot reach very low energies. SODock has the highest accuracy and robustness. In addition, SODock shows good performance in efficiency when optimizing drug-like ligands with less than ten rotatable bonds. FIPSDock shows excellent robustness and is close to SODock in accuracy and efficiency. In general, the four PSO-based algorithms show superior performance than the two EA-based algorithms, especially for highly flexible ligands. Our method can be regarded as a reference for the validation of new optimization algorithms in protein-ligand docking. PMID:24935106

Guo, Liyong; Yan, Zhiqiang; Zheng, Xiliang; Hu, Liang; Yang, Yongliang; Wang, Jin

2014-07-01

478

Architecture of a Quantum Multicomputer Optimized for Shor's Factoring Algorithm

The quantum multicomputer consists of a large number of small nodes and a qubus interconnect for creating entangled state between the nodes. The primary metric chosen is the performance of such a system on Shor's algorithm for factoring large numbers: specifically, the quantum modular exponentiation step that is the computational bottleneck. This dissertation introduces a number of optimizations for the modular exponentiation. My algorithms reduce the latency, or circuit depth, to complete the modular exponentiation of an n-bit number from O(n^3) to O(n log^2 n) or O(n^2 log n), depending on architecture. Calculations show that these algorithms are one million times and thirteen thousand times faster, when factoring a 6,000-bit number, depending on architecture. Extending to the quantum multicomputer, five different qubus interconnect topologies are considered, and two forms of carry-ripple adder are found to be the fastest for a wide range of performance parameters. The links in the quantum multicomputer are serial; parallel links would provide only very modest improvements in system reliability and performance. Two levels of the Steane [[23,1,7

Rodney Doyle Van Meter III

2006-07-11

479

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

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

D'Helon, CD

2004-08-18

480

Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm. PMID:24955402

Liu, Liqiang; Dai, Yuntao

2014-01-01

481

Brief Announcement: Energy-Optimal Distributed Algorithms for Minimum Spanning Trees

Brief Announcement: Energy-Optimal Distributed Algorithms for Minimum Spanning Trees Yongwook Choi-efficient distributed algorithms for energy-constraint networks. This paper addresses the minimum spanning tree (MST Algorithm, Energy-Efficient, Mini- mum Spanning Tree, Distributed Approximation Algorithm 1. MODEL

Khan, Maleq

482

In this extended abstract we use a modular approach for the design of SLS algorithms for MCOPs that are solved in terms of Pareto optimality. We assume that SLS algorithms can be seen as combinations of algorithm components that can be coupled together to solve a given problem. These algorithm components have dieren t targets that are important for successful

Luis Paquete; Fachgebiet Intellektik

2005-01-01

483

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

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

Hua, Haiyan; Lin, Shuwen

2014-03-01

484

A partially inexact bundle method for convex semi-infinite minmax problems

NASA Astrophysics Data System (ADS)

We present a bundle method for solving convex semi-infinite minmax problems which allows inexact solution of the inner maximization. The method is of the partially inexact oracle type, and it is aimed at reducing the occurrence of null steps and at improving bundle handling with respect to existing methods. Termination of the algorithm is proved at a point satisfying an approximate optimality criterion, and the results of some numerical experiments are also reported.

Fuduli, Antonio; Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna

2015-04-01

485

In this paper, we derive a novel analytical framework for designing optimal multiple-input multiple-output (MIMO) uplink space-time scheduling algorithms with respect to general convex utility functions. The novel approach we take is to discretize the search space and apply integer-programming techniques. We assume that mobile terminal has nT transmit antennas while the base station has nR receive antennas. With multiple

Kin-Nang Lau

2004-01-01

486

Matrix Algebras in Quasi-Newtonian Algorithms for Optimal Learning in Multi-Layer Perceptrons

Matrix Algebras in Quasi-Newtonian Algorithms for Optimal Learning in Multi-Layer Perceptrons in a Multi- Layer Perceptron (MLP) environment a new class of Quasi-Newtonian (QN) methods. The algorithms

Fanelli, Stephen

487

An optimal technology mapping algorithm for delay optimization in lookup-table based FPGA designs

In this paper we present a polynomial time technology mapping algorithm, called Flow-Map, that optimally solves the LUT-based FPGA technology mapping problem for depth minimization for general Boolean networks. This theoretical breakthrough makes a sharp contrast with the fact that conventional technology mapping problem in library-based designs is NP-hard. A key step in Flow-Map is to compute a minimum height

Jason Cong; Yuzheng Ding

1992-01-01

488

Parallel global optimization with the particle swarm algorithm.

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

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

2004-12-01

489

NASA Astrophysics Data System (ADS)

Pt-Pd alloy nanoparticles, as potential catalyst candidates for new-energy resources such as fuel cells and lithium ion batteries owing to their excellent reactivity and selectivity, have aroused growing attention in the past years. Since structure determines physical and chemical properties of nanoparticles, the development of a reliable method for searching the stable structures of Pt-Pd alloy nanoparticles has become of increasing importance to exploring the origination of their properties. In this article, we have employed the particle swarm optimization algorithm to investigate the stable structures of alloy nanoparticles with fixed shape and atomic proportion. An improved discrete particle swarm optimization algorithm has been proposed and the corresponding scheme has been presented. Subsequently, the swap operator and swap sequence have been applied to reduce the probability of premature convergence to the local optima. Furthermore, the parameters of the exchange probability and the 'particle' size have also been considered in this article. Finally, tetrahexahedral Pt-Pd alloy nanoparticles has been used to test the effectiveness of the proposed method. The calculated results verify that the improved particle swarm optimization algorithm has superior convergence and stability compared with the traditional one.

Shao, Gui-Fang; Wang, Ting-Na; Liu, Tun-Dong; Chen, Jun-Ren; Zheng, Ji-Wen; Wen, Yu-Hua

2015-01-01

490

Experimental optimization of protein refolding with a genetic algorithm

Refolding of proteins from solubilized inclusion bodies still represents a major challenge for many recombinantly expressed proteins and often constitutes a major bottleneck. As in vitro refolding is a complex reaction with a variety of critical parameters, suitable refolding conditions are typically derived empirically in extensive screening experiments. Here, we introduce a new strategy that combines screening and optimization of refolding yields with a genetic algorithm (GA). The experimental setup was designed to achieve a robust and universal method that should allow optimizing the folding of a variety of proteins with the same routine procedure guided by the GA. In the screen, we incorporated a large number of common refolding additives and conditions. Using this design, the refolding of four structurally and functionally different model proteins was optimized experimentally, achieving 74–100% refolding yield for all of them. Interestingly, our results show that this new strategy provides optimum conditions not only for refolding but also for the activity of the native enzyme. It is designed to be generally applicable and seems to be eligible for all enzymes. PMID:20799347

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

2010-01-01

491

NASA Astrophysics Data System (ADS)

A bi-objective optimization problem with Lipschitz objective functions is considered. An algorithm is developed adapting a univariate one-step optimal algorithm to multidimensional problems. The univariate algorithm considered is a worst-case optimal algorithm for Lipschitz functions. The multidimensional algorithm is based on the branch-and-bound approach and trisection of hyper-rectangles which cover the feasible region. The univariate algorithm is used to compute the Lipschitz bounds for the Pareto front. Some numerical examples are included.

Žilinskas, Antanas; Žilinskas, Julius

2015-04-01

492

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

493

NASA Astrophysics Data System (ADS)

The dynamically dimensioned search (DDS) continuous global optimization algorithm by Tolson and Shoemaker (2007) is modified to solve discrete, single-objective, constrained water distribution system (WDS) design problems. The new global optimization algorithm for WDS optimization is called hybrid discrete dynamically dimensioned search (HD-DDS) and combines two local search heuristics with a discrete DDS search strategy adapted from the continuous DDS algorithm. The main advantage of the HD-DDS algorithm compared with other heuristic global optimization algorithms, such as genetic and ant colony algorithms, is that its searching capability (i.e., the ability to find near globally optimal solutions) is as good, if not better, while being significantly more computationally efficient. The algorithm's computational efficiency is due to a number of factors, including the fact that it is not a population-based algorithm and only requires computationally expensive hydraulic simulations to be conducted for a fraction of the solutions evaluated. This paper introduces and evaluates the algorithm by comparing its performance with that of three other algorithms (specific versions of the genetic algorithm, ant colony optimization, and particle swarm optimization) on four WDS case studies (21- to 454-dimensional optimization problems) on which these algorithms have been found to perform well. The results obtained indicate that the HD-DDS algorithm outperforms the state-of-the-art existing algorithms in terms of searching ability and computational efficiency. In addition, the algorithm is easier to use, as it does not require any parameter tuning and automatically adjusts its search to find good solutions given the available computational budget.

Tolson, Bryan A.; Asadzadeh, Masoud; Maier, Holger R.; Zecchin, Aaron

2009-12-01