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1

An iterated 1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision

An iterated 1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision Peter Ochs1-convexity is particularly interesting in combination with total general- ized variation and learned image priors. Efficient algorithms. About two decades ago, people started to replace quadratic regularization terms by non-smooth 1

Teschner, Matthias

2

Convex Analysis and Optimization, D. P. Bertsekas! CONVEX OPTIMIZATION

1! Convex Analysis and Optimization, D. P. Bertsekas! CONVEX OPTIMIZATION: A SELECTIVE OVERVIEW Dimitri Bertsekas! M.I.T.! Taiwan! May 2010! #12;2! Convex Analysis and Optimization, D. P. Bertsekas! Â· Unifying framework for existence of solutions and duality gap analysis! Â· Use of duality in algorithms! #12

Bertsekas, Dimitri

3

NASA Astrophysics Data System (ADS)

The primal-dual optimization algorithm developed in Chambolle and Pock (CP) (2011 J. Math. Imag. Vis. 40 1-26) 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 this paper, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity x-ray illumination is presented.

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

2012-05-01

4

NASA Astrophysics Data System (ADS)

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

Cevher, Volkan; Becker, Stephen; Schmidt, Mark

2014-09-01

5

NSDL National Science Digital Library

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

Tim Lambert

6

An Inner Convex Approximation Algorithm for BMI Optimization and Applications in Control

In this work, we propose a new local optimization method to solve a class of nonconvex semidefinite programming (SDP) problems. The basic idea is to approximate the feasible set of the nonconvex SDP problem by inner positive semidefinite convex approximations via a parameterization technique. This leads to an iterative procedure to search a local optimum of the nonconvex problem. The convergence of the algorithm is analyzed under mild assumptions. Applications in static output feedback control are benchmarked and numerical tests are implemented based on the data from the COMPLeib library.

Dinh, Quoc Tran; Diehl, Moritz

2012-01-01

7

INTRODUCTION PROBLEM FORMULATION METHODOLOGY DUALITY Algorithms CONCLUSION Dense convex, 2014 1/30 #12;INTRODUCTION PROBLEM FORMULATION METHODOLOGY DUALITY Algorithms CONCLUSION OUTLINE Methodology Three ways to simplify the problem 2/30 #12;INTRODUCTION PROBLEM FORMULATION METHODOLOGY DUALITY

8

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

9

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

10

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

11

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

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

2014-11-01

12

Convex optimization methods for model reduction

Model reduction and convex optimization are prevalent in science and engineering applications. In this thesis, convex optimization solution techniques to three different model reduction problems are studied.Parameterized ...

Sou, Kin Cheong, 1979-

2008-01-01

13

Stable nonlinear identification from noisy repeated experiments via convex optimization

This paper introduces new techniques for using convex optimization to fit input-output data to a class of stable nonlinear dynamical models. We present an algorithm that guarantees consistent estimates of models in this ...

Tobenkin, Mark M.

14

Kernel regression for travel time estimation via convex optimization

Kernel regression for travel time estimation via convex optimization SÃ©bastien 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

15

iPiano: Inertial Proximal Algorithm for Non-convex Optimization

explicit (forward) gradient steps w.r.t. the smooth part with proximal (backward) steps w.r.t. the non, (xn - xn-1 ) as inertial term, and (I + g)-1 as backward or proximal step. For g 0 the proximal step (possi- bly non-differentiable) function. The algorithm iPiano combines forward- backward splitting

Teschner, Matthias

16

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

]: Social and Behavioral Sciences General Terms: Algorithms, Economics, Theory Additional Key Words and Phrases: Market design, securities market, prediction market, automated market maker, convex analysis12 Efficient Market Making via Convex Optimization, and a Connection to Online Learning JACOB

Chen, Yiling

17

GLOBAL OPTIMIZATION IN COMPUTER VISION: CONVEXITY, CUTS AND

GLOBAL OPTIMIZATION IN COMPUTER VISION: CONVEXITY, CUTS AND APPROXIMATION ALGORITHMS CARL OLSSON in computer vision. Numerous prob- lems in this field as well as in image analysis and other branches. International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, 2007. Â· C. Olsson, F. Kahl, R

Lunds Universitet

18

Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization

. INTRODUCTION Trajectory optimization algorithms have two roles in robotic motion planning. First, they can used our algorithm for motion planning. Top left: planning an arm trajectory for the PR2 in simulation robotic motion planning problems. At the core of our approach are (i) A sequential convex optimization

Abbeel, Pieter

19

On Equilibrium Pricing as Convex Optimization Jiawei Zhang

On Equilibrium Pricing as Convex Optimization Lihua Chen Yinyu Ye Jiawei Zhang Abstract We study competitive economy equilibrium computation. We show that, for the first time, the equilibrium sets-homogeneous utility functions; are convex or log-convex. Furthermore, an equilibrium can be computed as convex

Ye, Yinyu

20

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

and Behavioral Sciences General Terms: Algorithms, Economics, Theory Additional Key Words and Phrases: Market design, securities market, prediction market, automated market maker, convex analysis, online linearX Efficient Market Making via Convex Optimization, and a Connection to Online Learning Jacob

Abernethy, Jake

21

Fast algorithms for convex quadratic programming and multicommodity flows

In the first part of the paper, we extend Karmarkar's interior point method to give an algorithm for Convex Quadratic Programming which requires O(Na'~7(logL)(logN)L) arithmetic operations. At each iteration, Karmarkar's method locally minimizes the linear (convex) numerator of a transformed objective function in the transformed domain. However, in the case of Convex Quadratic Programming the numerator of the transformed objective

Sanjiv Kapoor; Pravin M. Vaidya

1986-01-01

22

1 Automatic Code Generation for Real-Time Convex Optimization

.3.4 Sliding window estimation 19 1.3.5 Real-time input design 20 1.3.6 Model predictive control 20 11 Automatic Code Generation for Real-Time Convex Optimization Jacob Mattingley and Stephen Boyd Press, 2009. This chapter concerns the use of convex optimization in real-time embedded systems

23

Optimal risk allocation for convex risk functionals in general domains

Optimal risk allocation for convex risk functionals in general domains Swen Kiesel and Ludger RÂ¨uschendorf University of Freiburg Abstract In this paper we extend the classical optimal risk allocation problem to the case of general convex risk functionals defined on real Banach spaces. In particular we characterize

RÃ¼schendorf, Ludger

24

Convex Optimization Methods for Graphs and Statistical Modeling

be solved exactly or approximately via semidefinite programming. We provide sharp estimates (based linear measurements required for exact and robust recovery in a variety of settings. Â· We present convex: Â· We propose a convex optimization method for decomposing the sum of a sparse matrix and a low

25

Motion Planning with Sequential Convex Optimization and Convex Collision Checking

, Pieter Abbeel Abstract--We present a new optimization-based approach for robotic motion planning among OMPL, with regard to planning time and path quality. We consider motion planning for 7 DOF robot arms for solving high- dimensional motion planning problems. Trajectory optimization is fundamental in optimal

North Carolina at Chapel Hill, University of

26

Newton-Raphson consensus for distributed convex optimization

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

Schenato, Luca

27

Mapping the Energy Landscape of Non-Convex Optimization Problems

-convex energy landscapes. In this paper, inspired by the success of visualizing the landscapes of Ising and Spin-glass in the landscape). ELMs can be efficiently constructed by running a MCMC algorithm that fea- tures a dynamic for the spin-glass model. Liang [6, 7] generalizes the Wang-Landau algorithm [13] for random walks in the state

Zhu, Song Chun

28

Algorithm for detecting human faces based on convex-hull

NASA Astrophysics Data System (ADS)

In this paper, we proposed a new method to detect faces in color based on the convex-hull. We detect two kinds of regions that are skin and hair likeness region. After preprocessing, we apply the convex-hull to their regions and can find a face from their intersection relationship. The proposed algorithm can accomplish face detection in an image involving rotated and turned faces as well as several faces. To validity the effectiveness of the proposed method, we make experiment with various cases.

Park, Minsick; Park, Chang-Woo; Park, Mignon; Lee, Chang-Hoon

2002-03-01

29

A Survey of Algorithms for Convex Multicommodity Flow Problems

There are many problems related to design a networks. Among them, the message routing problem plays a determinant role in the optimization of network performance. Much of the motivation of this work comes from this problem which is shown to belong to the lass of nonlinear convex multicomodity flow problems.

A. Ouorou; P. Mahey; P. P. H. Vial

1997-01-01

30

A Survey of Algorithms for Convex Multicommodity Flow Problems

Routing problems appear frequently when dealing with the operation of communication or transportation networks. Among them, the message routing problem plays a determinant role in the optimization of network performance. Much of the motivation for this work comes from this problem which is shown to belong to the class of nonlinear convex multicommodity flow problems. This paper emphasizes the message

A. Ouorou; P. Mahey; J.-Ph. Vial

2000-01-01

31

Design of PI Controllers based on Non-Convex Optimization

This paper presents an efficient numerical method for designing PI controllers. The design is based on optimization of load disturbance rejection with constraints on sensitivity and weighting of set point response. Thus, the formulation of the design problem captures three essential aspects of industrial control problems, leading to a non-convex optimization problem. Efficient ways to solve the problem are presented.

K. J. ÅSTRÖM; H. PANAGOPOULOS; T. HÄGGLUND

1998-01-01

32

Formulating Cyber-Security as Convex Optimization Problems

Formulating Cyber-Security as Convex Optimization ProblemsÃ? Kyriakos G. Vamvoudakis1 , Jo~ao P,vigna}@cs.ucsb.edu Abstract. Mission-centric cyber-security analysts require a complete overview and understanding a cyber-mission with a limited amount of resources, based on a model that takes into account potential

Vigna, Giovanni

33

Newton-Raphson consensus for distributed convex optimization

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

Schenato, Luca

34

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

35

Balancing of high-speed rotating machinery using convex optimization

The vibration caused by rotor mass imbalance is a major source of maintenance problems in high-speed rotating machinery. To minimize the vibration by balancing under practical constraints and data uncertainty is a decision making problem. In this paper, the flexible rotor balancing problem based on the influence coefficient method is formulated as a convex optimization problem. This formulation not only

Guoxin Li; Zongli Lin; C. Untaroiu; P. E. Allaire

2003-01-01

36

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography.

Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion magnetic resonance imaging (MRI) data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic microstructural features of the tissue, such as axonal density and diameter, by using multicompartment models. In this paper, we present a novel framework to reestablish the link between tractography and tissue microstructure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e., the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically plausible assessment of the structural connectivity of the brain. PMID:25167548

Daducci, Alessandro; Dal Palu, Alessandro; Lemkaddem, Alia; Thiran, Jean-Philippe

2015-01-01

37

Convex Analysis and Optimization, D. P. Bertsekas A NEW LOOK AT

1 Convex Analysis and Optimization, D. P. Bertsekas A NEW LOOK AT CONVEX ANALYSIS AND OPTIMIZATION Dimitri Bertsekas M.I.T. May 2007 #12;2 Convex Analysis and Optimization, D. P. Bertsekas OUTLINE by visualization Â Unification and intuition enhanced by geometry Â· Three unifying lines of analysis Â Common

Bertsekas, Dimitri

38

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

39

Robust quantum error correction via convex optimization.

We present a semidefinite program optimization approach to quantum error correction that yields codes and recovery procedures that are robust against significant variations in the noise channel. Our approach allows us to optimize the encoding, recovery, or both, and is amenable to approximations that significantly improve computational cost while retaining fidelity. We illustrate our theory numerically for optimized 5-qubit codes, using the standard [5,1,3] code as a benchmark. Our optimized encoding and recovery yields fidelities that are uniformly higher by 1-2 orders of magnitude against random unitary weight-2 errors compared to the [5,1,3] code with standard recovery. PMID:18232841

Kosut, Robert L; Shabani, Alireza; Lidar, Daniel A

2008-01-18

40

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

41

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

42

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

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

Sambasivam, Mashilamani

1999-01-01

43

Optimal Stochastic Approximation Algorithms for Strongly Convex ...

Jul 1, 2010 ... The analysis of these SA methods (goes back to the works [8] .... We would like to find a linear form V(u) = ?x, u? to describe the ... support vector machine [5]: f(x) = E[max{0,v?x, u?] + ?x2 ... For example, in the ridge regression problem, the l2 norm regularization term can be stated as a .... the conditional.

2012-06-18

44

Target position localization in a passive radar system through convex optimization

NASA Astrophysics Data System (ADS)

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

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

2013-05-01

45

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

46

OPTIMAL INEQUALITIES IN PROBABILITY THEORY: A CONVEX OPTIMIZATION APPROACH

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

DIMITRIS BERTSIMAS; IOANA POPESCU

2000-01-01

47

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms #12;Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example #12;Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic

KjellstrÃ¶m, Hedvig

48

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms

Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic Algorithms Foundations Algorithm Components Numerical Optimization Genetic Programming 1 Foundations 2 Algorithm Programming Example Foundations Algorithm Components Numerical Optimization Genetic Programming Genetic

KjellstrÃ¶m, Hedvig

49

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.

50

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

51

Ant Algorithms for Discrete Optimization

Ant Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, Universit#19;e, Switzerland luca@idsia.ch Abstract This paper overviews recent work on ant algorithms, that is, algorithms for discrete optimization which took inspiration from the observation of ant colonies foraging behavior

Ducatelle, Frederick

52

Ant Algorithms for Discrete Optimization

Ant Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, UniversitÂ´e Libre, Switzerland luca@idsia.ch Abstract This paper overviews recent work on ant algorithms, that is, algorithms for discrete optimization which took inspiration from the observation of ant colonies foraging behavior

Gambardella, Luca Maria

53

Convex Optimization Methods for Model Reduction Kin Cheong Sou

procedure based on integral quadratic constraint analysis and a theoretical statement based on L2 gain of the model reduction problem as a quasi-convex program allows the flexi- bility to enforce constraints-Hammerstein system. The identification problem is formulated as a non-convex 3 #12;quadratic prog

Daniel, Luca

54

Efficient Design of Cosine-Modulated Filter Banks via Convex Optimization

Thispaperpresentsefficientapproachesfordesigning cosine-modulated filter banks with linear phase prototype filter. First, we show that the design problem of the prototype filter being a spectral factor of th-band filter is a nonconvex optimization problem with low degree of nonconvexity. As a result, the non- convex optimization problem can be cast into a semi-definite pro- gramming (SDP) problem by a convex relaxation technique.

Ha Hoang Kha; Hoang Duong Tuan; Truong Q. Nguyen

2009-01-01

55

Advanced global optimization algorithms for parameterized LMIs

Parameterized linear matrix inequalities (PLMIs) frequently arise in analysis and synthesis problems of robust control theory. However, in contrast to linear matrix inequalities (LMIs) which are convex optimization problems with available efficient polynomial-time interior-point methods, PLMIs are highly nonconvex and thus are very hard to solve. In this paper, we exploit partial convexity properties of PLMIs that are useful for

H. D. Tuan; P. Apkarian; H. Tuy

1999-01-01

56

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

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

57

Ant algorithms for discrete optimization.

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic. PMID:10633574

Dorigo, M; Di Caro, G; Gambardella, L M

1999-01-01

58

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

59

A case study in the performance and scalability of optimization algorithms

We analyze the performance and scalabilty of algorithms for the solution of large optimization problems on high-performance parallel architectures. Our case study uses the GPCG (gradient projection, conjugate gradient) algorithm for solving bound-constrained convex quadratic problems. Our implementation of the GPCG algorithm within the Toolkit for Advanced Optimization (TAO) is available for a wide range of high-performance architectures and has

Steven J. Benson; Lois Curfman McInnes; Jorge J. Moré

2001-01-01

60

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

61

NASA Astrophysics Data System (ADS)

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

Panicker, Rahul Alex

62

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

63

A Convex Quadratic SDMA Grouping Algorithm Based on Spatial Correlation

Space Division Multiple Access (SDMA) is a promis- ing solution to improve the spectral efficiency of future mobile radio systems. However, finding the group of MSs that maximizes system capacity using SDMA is a complex combinatorial prob- lem, which can only be assuredly solved through an Exhaustive Search (ES). Because an ES is usually too complex, there are several sub-optimal

Tarcisio F. Maciel; Anja Klein

2007-01-01

64

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

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

Tibshirani, Ryan

65

PATTERN SYNTHESIS OF PLANAR ANTENNA ARRAY VIA CONVEX OPTIMIZATION FOR AIRBORNE FORWARD LOOKING RADAR

When airborne forward looking planar antenna is used to detect ground moving target, targets may be masked by strong clutter due to high sidelobes of the antenna pattern. In this paper, transmitting pattern is synthesized via convex optimization in order to suppress clutter from ground. Transmitting pattern has a low sidelobe illuminating short ranges and a high sidelobe focused into

Yi Qu; Guisheng Liao; Sheng-Qi Zhu; Xiang-Yang Liu

2008-01-01

66

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

67

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

NASA Astrophysics Data System (ADS)

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

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

2010-09-01

68

An Effective Branch-and-Bound Algorithm for Convex Quadratic Integer Programming

NASA Astrophysics Data System (ADS)

We present a branch-and-bound algorithm for minimizing a convex quadratic objective function over integer variables subject to convex constraints. In a given node of the enumeration tree, corresponding to the fixing of a subset of the variables, a lower bound is given by the continuous minimum of the restricted objective function. We improve this bound by exploiting the integrality of the variables using suitably-defined lattice-free ellipsoids. Experiments show that our approach is very fast on both unconstrained problems and problems with box constraints. The main reason is that all expensive calculations can be done in a preprocessing phase, while a single node in the enumeration tree can be processed in linear time in the problem dimension.

Buchheim, Christoph; Caprara, Alberto; Lodi, Andrea

69

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. PMID:18506069

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

2008-06-21

70

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

71

A locally optimal handoff algorithm

The design of handoff algorithms for cellular communication systems based on mobile signal strength measurements is considered. The design problem is posed as an optimization to obtain the best tradeoff between expected number of service failures and expected number of handoffs, where a service failure is defined to be the event that the signal strength falls below a level required

O. E. Kelly; V. V. Veeravalli

1995-01-01

72

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

73

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

74

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

75

Construction of Convex Relaxations Using Automated Code Generation Techniques

This paper describes how the automated code generation tool DAEPACK can be used to construct convex relaxations of codes implementing nonconvex functions. Modern deterministic global optimization algorithms involving continuous and\\/or integer variables often require such convex relaxations. Within the described framework, the user supplies a code implementing the objective and constraints of a nonconvex optimization problem. DAEPACK then analyzes this

Edward P. Gatzke; John E. Tolsma; Paul I. Barton

2002-01-01

76

problem was first discussed in Carpentier's paper [1] in 1962. The objective of an Optimal Power Flow (OPF to convexity the AC OPF problem, various convex relaxation techniques have been developed. Semidefinite

Lavaei, Javad

77

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

rameter is u ? U, the constraint for optimization problem is expressed as f(x,u) ? 0 ...... the truncated normal distribution on A, the integration in q1 is reduced to one ...... Lipschitz constant L: We consider a quadratic constraint function f(x,u) in x ...

2008-12-23

78

Fast First-Order Methods for Composite Convex Optimization with ...

Research supported in part by NSF Grant DMS 10-16571, AFOSR Grant ... In Section 3 we introduce and analyze the FISTA algorithm with full line search. ... On the other hand condition (2.1) may be satisfied with a larger value of µ. ... As a first step consider any two vectors u, v ? Rn and let [u, v] denote the set of points on ...

2013-01-02

79

Developing learning algorithms via optimized discretization of continuous dynamical systems.

Most of the existing numerical optimization methods are based upon a discretization of some ordinary differential equations. In order to solve some convex and smooth optimization problems coming from machine learning, in this paper, we develop efficient batch and online algorithms based on a new principle, i.e., the optimized discretization of continuous dynamical systems (ODCDSs). First, a batch learning projected gradient dynamical system with Lyapunov's stability and monotonic property is introduced, and its dynamical behavior guarantees the accuracy of discretization-based optimizer and applicability of line search strategy. Furthermore, under fair assumptions, a new online learning algorithm achieving regret O(?T) or O(logT) is obtained. By using the line search strategy, the proposed batch learning ODCDS exhibits insensitivity to the step sizes and faster decrease. With only a small number of line search steps, the proposed stochastic algorithm shows sufficient stability and approximate optimality. Experimental results demonstrate the correctness of our theoretical analysis and efficiency of our algorithms. PMID:21880573

Tao, Qing; Sun, Zhengya; Kong, Kang

2012-02-01

80

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

81

A Convex Relaxation Method for a Class of Vector-valued Minimization Problems with

as problems of this class. We provide several ex- perimental results to demonstrate that our convex algorithm of a number of widely used models. In general, non-convex functionals are much more difficult to minimize than great watershed in optimization isn't between linearity and nonlinear- ity, but convexity and non

Soatto, Stefano

82

We design a polynomial-time, truthful-in-expectation, (1-1/e)-approximation mechanism for welfare maximization for a fundamental class of combinatorial auctions. Our results apply to bidders with valuations that are matroid rank sums (MRS), which encompass most concrete examples of submodular functions studied in this context, including coverage functions and matroid weighted-rank functions. Our approximation factor is the best possible, even for known and explicitly given coverage valuations, assuming P != NP. Our mechanism is the first truthful-in-expectation and polynomial-time mechanism to achieve a constant-factor approximation for an NP-hard welfare maximization problem in combinatorial auctions with heterogeneous goods and restricted valuations. Our mechanism is an instantiation of a new framework for designing approximation mechanisms based on randomized rounding algorithms. A typical such algorithm first optimizes over a fractional relaxation of the original problem, and then randomly rounds the frac...

Dughmi, Shaddin; Yan, Qiqi

2011-01-01

83

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

84

Composite splitting algorithms for convex optimization Junzhou Huang a,

have been applied in sparse learning [7] and compressive MR imaging [2]. The Iterative Shrink- age- cessfully used in signal processing [8,9], matrix completion [10] and multi-task learning [11]. To handle

85

OPTIMAL STEEPEST DESCENT ALGORITHMS FOR ...

Complexity results for nonlinear programming are limited to convex problems, but they are .... worst case complexity cannot be improved, but the speed can be improved in practical ... technical results on quadratic functions, we describe all variables, functions and pa- ... Data: x0 ? Rn, v0 = x0, ?0 > µ (?0 = L if L is known

2008-08-02

86

Flexible optimization of text recognition algorithms

This paper presents a system for the optimization of text recognition algorithms. First a theoretic four-staged model of text recognition is proposed. In this four-staged model, the second stage called text localization is optimized. A reinterpreted version of the F measure is used as a fitness indicator for optimization of the localization. The optimization method is described and the role

Britta Meixner; Florian Pein; Harald Kosch

2010-01-01

87

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

88

PID Design by Convex-Concave Optimization M. Hast1, K.J. Astrom1, B. Bernhardsson1, S. Boyd2

PID Design by Convex-Concave Optimization M. Hast1, K.J. Â°AstrÂ¨om1, B. Bernhardsson1, S. Boyd2 Abstract-- This paper describes how PID controllers can be designed by optimizing performance subject convex-concave program- ming for design of PID controllers. Following the ideas in [2], [15] we consider

89

Algorithms for nonlinear multicommodity network flow problems

This paper presents a class of algorithms for optimization of convex multi-commodity flow problems. The algorithms are based on the ideas of Gallager's methods for distributed optimization of delay in data communication networks [1],

Dimitri P. Bertsekas

90

Genetic symbiosis algorithm for multiobjective optimization problem

Evolutionary algorithms are often well-suited for optimization problems. Since the mid-1980's, interest in multiobjective problems has been expanding rapidly. Various evolutionary algorithms have been developed which are capable of searching for multiple solutions concurrently in a single run. In this paper, we proposed a genetic symbiosis algorithm (GSA) for multi-object optimization problems (MOP) based on the symbiotic concept found widely

Jiangming Mao; K. Hirasawa; Jinlu Hu; J. Murata

2000-01-01

91

Hybrid genetic algorithm for electromagnetic topology optimization

This paper proposes a hybrid genetic algorithm (GA) for electromagnetic topology optimization. A two-dimensional (2-D) encoding technique, which considers the geometrical topology, is first applied to electromagnetics. Then, a 2-D geographic crossover is used as the crossover operator. A novel local optimization algorithm, called the on\\/off sensitivity method, hybridized with the 2-D encoded GA, improves the convergence characteristics. The algorithm

Chang-Hwan Im; Hyun-Kyo Jung; Yong-Joo Kim

2003-01-01

92

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

93

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

94

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

95

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

96

Stochastic optimization algorithms for barrier dividend strategies

NASA Astrophysics Data System (ADS)

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

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

2009-01-01

97

In this paper we present a new class of sequential and parallel algorithms for multicommodity transportation problems with linear and convex costs. First, we consider a capacitated multicommodity transportation problem with an orthogonal quadratic objective function. We develop two new solution methods. Both exploit the fact that a projection on the conservation of flow constraints has an explicit form which was proved in an early paper by I. Chabini and M. Florian. The two algorithms deal differently with the remaining constraints namely the non negativity and capacity constraints. We prove the convergence of both algorithms using a basic general theory (developed by the author) which generalizes Bregman`s theory. The above algorithms can be extended for differentiable convex cost multicommodity transportation problems as follows. For strictly convex costs we use a variant of the projected gradient method. The quadratic proximal minimization algorithm is applied to the linear cost multicommodity transportation problems. For both cases we solve an orthogonal projection multicommodity transportation problem at each iteration. The algorithms developed are well-suited for a coarse grained parallelization. The different steps may be decomposed by nodes, by arcs and/or by commodities. We investigate different strategies depending on the structure of the problem, the number of commodities and the architecture of the parallel machine. We present computational results for these different approaches on parallel and serial platforms such as a network of Transputers or Sun workstations. Very large problems are solved. The parallel implementations are analyzed using especially a new measure of performance developed previously by I. Chabini and M. Florian.

Chabini, I.

1994-12-31

98

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

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

2014-09-01

99

Filterbank optimization with convex objectives and the optimality of principal component forms

This paper proposes a general framework for the optimization of orthonormal filterbanks (FBs) for given input statistics. This includes as special cases, many previous results on FB optimization for compression. It also solves problems that have not been considered thus far. FB optimization for coding gain maximization (for compression applications) has been well studied before. The optimum FB has been

Sony Akkarakaran; P. P. Vaidyanathan

2001-01-01

100

Aerodynamic Shape Optimization using an Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem-both single and two-objective variations is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.

Hoist, Terry L.; Pulliam, Thomas H.

2003-01-01

101

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

102

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

103

Ant Algorithms Solve Difficult Optimization Problems

Ant Algorithms Solve Difficult Optimization Problems Marco Dorigo IRIDIA UniversitÂ´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, UniversitÃ©

104

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

105

Optimization Based Image Segmentation by Genetic Algorithms

Optimization Based Image Segmentation by Genetic Algorithms S. Chabrier1 , C. Rosenberger2 , B them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation cri- terion which quantifies the quality of an image segmentation result

Paris-Sud XI, UniversitÃ© de

106

Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

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

Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao

107

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

108

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

109

Research on Two-Dimensional Bar Code Positioning Approach Based on Convex Hull Algorithm

During the process of the recognizing two-dimensional bar code, the two-dimensional bar can be corrected further only after accurately positing bar code in original image. In this paper, in case of the two-dimensional bar data matrix, the concept of convex hull in computational geometry is used to locate bar code in the paper. The vertices in convex hulls are selected

Zhi Liu; Herong Zheng; Wenting Cai

2009-01-01

110

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

111

An algorithm for online optimization of accelerators

NASA Astrophysics Data System (ADS)

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

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

2013-10-01

112

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

113

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

114

Algorithm selection in structural optimization

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

Clune, Rory P. (Rory Patrick)

2013-01-01

115

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

116

Cooperative evolutionary algorithm for space trajectory optimization

NASA Astrophysics Data System (ADS)

A hybrid evolutionary algorithm which synergistically exploits differential evolution, genetic algorithms and particle swarm optimization, has been developed and applied to spacecraft trajectory optimization. The cooperative procedure runs the three basic algorithms in parallel, while letting the best individuals migrate to the other populations at prescribed intervals. Rendezvous problems and round-trip Earth-Mars missions have been considered. The results show that the hybrid algorithm has better performance compared to the basic algorithms that are employed. In particular, for the rendezvous problem, a 100% efficiency can be obtained both by differential evolution and the genetic algorithm only when particular strategies and parameter settings are adopted. On the other hand, the hybrid algorithm always attains the global optimum, even though nonoptimal strategies and parameter settings are adopted. Also the number of function evaluations, which must be performed to attain the optimum, is reduced when the hybrid algorithm is used. In the case of Earth-Mars missions, the hybrid algorithm is successfully employed to determine mission opportunities in a large search space.

Rosa Sentinella, Matteo; Casalino, Lorenzo

2009-11-01

117

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

118

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

119

Path Cost Optimization Using Genetic Algorithm with Supervised Crossover Operator

Path Cost Optimization Using Genetic Algorithm with Supervised Crossover Operator Chi-Tsun Cheng of these algorithms rise dras- tically. Meta-heuristic algorithms such as evolutionary al- gorithms and genetic-based path cost optimization algorithm is proposed. The generic crossover operator in genetic algorithms

Tse, Chi K. "Michael"

120

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

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

Agarwal, Alekh

121

Optimal Monte Carlo Algorithms Ivan T. Dimov

Optimal Monte Carlo Algorithms Ivan T. Dimov Institute for Parallel Processing Department Centre University of Reading Whiteknights, PO Box 217, Reading, RG6 6AH, UK E-mail: I.T.Dimov@reading.ac.uk; ivdimov@bas.bg Web site: http://www.personal.rdg.ac.uk/ sis04itd/ Abstract The question "what Monte Carlo

Dimov, Ivan

122

Groundwater Remediation Strategy Using Global Optimization Algorithms

Groundwater Remediation Strategy Using Global Optimization Algorithms Shreedhar Maskey1 ; Andreja Jonoski2 ; and Dimitri P. Solomatine3 Abstract: The remediation of groundwater contamination by pumping as decision variables. Groundwater flow and particle-tracking models MODFLOW and MODPATH and a GO tool GLOBE

Neumaier, Arnold

123

A genetic algorithm for fin profile optimization

In the present work a genetic algorithm is proposed in order to optimize the thermal performances of finned surfaces. The bidimensional temperature distribution on the longitudinal section of the fin is calculated by resorting to the finite elements method. The heat flux dissipated by a generic profile fin is compared with the heat flux removed by the rectangular profile fin

Giampietro Fabbri

1997-01-01

124

Genetic Algorithms for Real Parameter Optimization

This paper is concerned with the application of gen etic algorithms to optimization problems over several real parameters. It is shown that k-point crossover (for k small relative to the number of parameters) can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters. Mutation can also be co nsidered as a

Alden H. Wright

1991-01-01

125

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

126

Interior search algorithm (ISA): a novel approach for global optimization.

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. PMID:24785823

Gandomi, Amir H

2014-07-01

127

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

128

A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems

A simple but effective evolutionary algorithm is proposed in this paper for solving complicated optimization problems. The new algorithm presents two hybridization operations incorporated with the conventional genetic ...

Xu, Y.G.

129

This paper describes an algorithm for decomposing a simple polygon into star-convex pieces and for finding the kernel of each piece. The algorithm considers the edges of a polygon to be ordered in a counterclockwise fashion and proceeds by visiting each... edge of the polygon and cumulatively intersecting the half-spaces defined by the edges. If all edges are traversed and the intersection is non-null, then the polygon is star-convex and the cumulative intersection is the kernel. Otherwise, the polygon...

Alford, Jennifer Reynolds

2012-06-07

130

Solving ptychography with a convex relaxation

Ptychography is a powerful computational imaging technique that transforms a collection of low-resolution images into a high-resolution sample reconstruction. Unfortunately, algorithms that are currently used to solve this reconstruction problem lack stability, robustness, and theoretical guarantees. Recently, convex optimization algorithms have improved the accuracy and reliability of several related reconstruction efforts. This paper proposes a convex formulation of the ptychography problem. This formulation has no local minima, it can be solved using a wide range of algorithms, it can incorporate appropriate noise models, and it can include multiple a priori constraints. The paper considers a specific algorithm, based on low-rank factorization, whose runtime and memory usage are near-linear in the size of the output image. Experiments demonstrate that this approach offers a 25% lower background variance on average than alternating projections, the current standard algorithm for ptychographic reconstruction...

Horstmeyer, Roarke; Ou, Xiaoze; Ames, Brendan; Tropp, Joel A; Yang, Changhuei

2014-01-01

131

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

132

Algorithm For Optimal Control Of Large Structures

NASA Technical Reports Server (NTRS)

Cost of computation appears competitive with other methods. Problem to compute optimal control of forced response of structure with n degrees of freedom identified in terms of smaller number, r, of vibrational modes. Article begins with Hamilton-Jacobi formulation of mechanics and use of quadratic cost functional. Complexity reduced by alternative approach in which quadratic cost functional expressed in terms of control variables only. Leads to iterative solution of second-order time-integral matrix Volterra equation of second kind containing optimal control vector. Cost of algorithm, measured in terms of number of computations required, is of order of, or less than, cost of prior algoritms applied to similar problems.

Salama, Moktar A.; Garba, John A..; Utku, Senol

1989-01-01

133

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

134

Learning Computer Programs with the Bayesian Optimization Algorithm

of the Bayesian Optimization Algorithm (BOA), a probabilistic model building genetic algorithm, to the domain of program tree evolution. The new system, BOA programming (BOAP), improves significantly on previous algorithms, such as the (hierarchical) Bayesian Optimization Algorithm (BOA) [6]. BOA is asymptotically more

Fernandez, Thomas

135

Hybrid genetic algorithm research and its application in problem optimization

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

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

2004-01-01

136

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.

137

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

138

Genetic algorithm optimization for aerospace electromagnetic design and analysis

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

J. Michael Johnson; Yahya Rahmat-Samii

1996-01-01

139

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

140

Simultaneous Optimization via Approximate Majorization for Concave Profits or Convex Costs

For multi-criteria problems and problems with poorly characterized objective, it is often desirable to simultaneously approximate the optimum solution for a large class of objective functions. We consider two such classes: 1. Maximizing all symmetric concave functions, and 2. Minimizing all symmetric convex functions. The first class corresponds to maximizing profit for a resource allocation problem (such as allocation of

Ashish Goel; Adam Meyerson

2006-01-01

141

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

142

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

143

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

144

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, UniversitÃ© de

145

Towards a Genetic Algorithm for Function Optimization Sonja Novkovic

Towards a Genetic Algorithm for Function Optimization Sonja Novkovic and Davor Sverko Abstract: This article analyses a version of genetic algorithm (GA, Holland 1975) designed for function optimization, such as non-coding segments, elitist selection and multiple crossover. Key words: Genetic algorithm, Royal

146

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

147

A branch-and-bound algorithm for convex multi-objective Mixed ...

procedure is designed, which allows to improve the approximated Pareto set. ... tailored refinement procedure to determine good quality Pareto solutions. The proposed algorithm is ... guidelines for future research in Section 6. 2 Literature ...

2013-12-28

148

Algorithms for the Electrical Optimization of Digital MOS Circuits

Algorithms for the Electrical Optimization of Digital MOS Circuits Kye S. Hedlund, Assistant;1 Abstract This work addresses the problem of automating the electrical optimization of digital MOS circuits algorithms for automating the electrical optimization of digital MOS circuits. Improvements to a circuit

North Carolina at Chapel Hill, University of

149

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

150

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

151

Toward an FPGA architecture optimized for public-key algorithms

Cryptographic algorithms are constantly evolving to meet security needs, and modular arithmetic is an integral part of these algorithms, especially in the case of public-key cryptosystems. To achieve optimal system performance while maintaining physical security, it is desirable to implement cryptographic algorithms in hardware. However, many public- key cryptographic algorithms require the implementation of modular arithmetic, specifically modular multiplication, for

Adam J. Elbirt; Christof Paar

1999-01-01

152

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform Marcos L-dominated Sorting Genetic Algorithm-II is presented. The algorithm provides significant decrease in compu- tational. This research shows the union of UT and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to give circuit de

Paris-Sud XI, UniversitÃ© de

153

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

154

DAOmap: A Depth-optimal Area Optimization Mapping Algorithm for FPGA Designs

DAOmap: A Depth-optimal Area Optimization Mapping Algorithm for FPGA Designs Deming Chen, Jason quality. We guarantee optimal mapping depth under the unit delay model. Experimental results show that our to the state-of-the-art depth- optimal, area minimization mapping algorithm CutMap [21], DAOmap is 16

Chen, Deming

155

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

156

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

157

Optimization algorithm for compact slab lasers

NASA Astrophysics Data System (ADS)

The pump structure greatly influences the characteristics of a diode side-pumped laser. To achieve high absorption efficiency and a homogeneous pump-beam distribution simultaneously, a systemic algorithm has been established to optimize the pump structure, where multiple reflections occur on the internal wall of the reflector inside the pump chamber. A novel design of an efficient, highly reliable, and good beam quality diode side-pumped solid-state laser is presented. Effort has been done to obtain a highly uniform pumping intensity in the active area, which simultaneously reduces the effects of thermal gradient. In this design a novel lens duct configuration is used. By this way a uniform power distribution and a maximum absorption of pump power is resulted. Numerical analysis also indicates the superiority of the design to other methods such as direct and diffusive pumping techniques.

Cao, Changqing; Zeng, Xiaodong; An, Yuying

2010-11-01

158

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

159

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

160

A polynomial-time algorithm for linear optimization based on a new class of kernel functions

NASA Astrophysics Data System (ADS)

In this paper we present a class of polynomial primal-dual interior-point algorithms for linear optimization based on a new class of kernel functions. This class is fairly general and includes the class of finite kernel functions by Y.Q. Bai, M.El Ghami and C. Roos [Y.Q. Bai, M. El Ghami, and C. Roos. A new efficient large-update primal-dual interior-point method based on a finite barrier, SIAM Journal on Optimization, 13 (3) (2003) 766-782]. The proposed functions have a finite value at the boundary of the feasible region. They are not exponentially convex and also not strongly convex like the usual barrier functions. The goal of this paper is to investigate such a class of kernel functions and to show that the interior-point methods based on these functions have favorable complexity results. In order to achieve these complexity results, several new arguments had to be used for the analysis. The iteration bound of large-update interior-point methods based on these functions and analyzed in this paper, is shown to be . For small-update interior-point methods the iteration bound is , which is currently the best-known bound for primal-dual IPMs. We also present some numerical results which show that by using a new kernel function, the best iteration numbers were achieved in most of the test problems.

El Ghami, M.; Ivanov, I.; Melissen, J. B. M.; Roos, C.; Steihaug, T.

2009-02-01

161

Fuzzy multiple objective optimal system design by hybrid genetic algorithm

In this paper, we propose a method for solving fuzzy multiple objective optimal system design problems with GUB structure by hybridized genetic algorithms (HGA). This approach enables the flexible optimal system design by applying fuzzy goals and fuzzy constraints. In this genetic algorithm (GA), we propose the new chromosomes representation that represents the GUB structure simply and effectively at the

Masato Sasaki; Mitsuo Gen

2003-01-01

162

Optimization and realization of a rotor dynamic balance measureing algorithm

Based on the research on the least square influential coefficient method of rotor dynamic balance, to deal with some problems like excessive residual vibration and unsatisfied balance effect, genetic algorithm was introduced to optimize and realize the least square influential coefficient method by using the characteristic of global optimization search. Experimental result shows that the balance algorithm based on genetic

Zi-qiang Zhang; Chuan-jiang Li; Li-li Wan

2010-01-01

163

Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water

oz343 Genetic Algorithms for Optimal Scheduling of Chlorine Dosing in Water Distribution Systems for determining the optimal schedule of chlorine dosing within a water distribution system considering multiple. The model is also capable of handling improved nonlinear chlorine decay algorithms by separating the genetic

Coello, Carlos A. Coello

164

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

165

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 ...

166

A hybrid algorithm for optimizing welding points of compliant assemblies

Purpose – The purpose of this paper is to propose a hybrid algorithm of the heuristic algorithm and the orthogonal design to optimize schemes of welding points (WPs). Assembly variation plays an important role in product manufacture. Different schemes of WPs can influence the sensitivity matrices between part and assembly variations. Design\\/methodology\\/approach – The paper proposes a hybrid algorithm to

Xing Yan-Feng

2009-01-01

167

Bacterial Foraging Optimization Algorithm for neural network learning enhancement

Backpropagation algorithm is widely used to solve many real-world problems, using the concept of Multilayer Perceptron. However, main disadvantages of Backpropagation are the convergence rate of it being relatively slow, and it is often trapped in the local minima. To solve this problem, it is found in the literatures, an evolutionary algorithm such as Particle Swarm Optimization algorithm is applied

Ismail Ahmed A. AL-Hadi; Siti Zaiton Mohd Hashim; Siti Mariyam Hj Shamsuddin

2011-01-01

168

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

169

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

170

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

171

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

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

Dervis Karaboga; Bahriye Basturk

2007-01-01

172

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

173

Krill herd: A new bio-inspired optimization algorithm

NASA Astrophysics Data System (ADS)

In this paper, a novel biologically-inspired algorithm, namely krill herd (KH) is proposed for solving optimization tasks. The KH algorithm is based on the simulation of the herding behavior of krill individuals. The minimum distances of each individual krill from food and from highest density of the herd are considered as the objective function for the krill movement. The time-dependent position of the krill individuals is formulated by three main factors: (i) movement induced by the presence of other individuals (ii) foraging activity, and (iii) random diffusion. For more precise modeling of the krill behavior, two adaptive genetic operators are added to the algorithm. The proposed method is verified using several benchmark problems commonly used in the area of optimization. Further, the KH algorithm is compared with eight well-known methods in the literature. The KH algorithm is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.

Gandomi, Amir Hossein; Alavi, Amir Hossein

2012-12-01

174

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

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

2013-11-01

175

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

176

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

177

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

178

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

179

An algorithm for the systematic disturbance of optimal rotational solutions

NASA Technical Reports Server (NTRS)

An algorithm for introducing a systematic rotational disturbance into an optimal (i.e., single axis) rotational trajectory is described. This disturbance introduces a motion vector orthogonal to the quaternion-defined optimal rotation axis. By altering the magnitude of this vector, the degree of non-optimality can be controlled. The metric properties of the distortion parameter are described, with analogies to two-dimensional translational motion. This algorithm was implemented in a motion-control program on a three-dimensional graphic workstation. It supports a series of human performance studies on the detectability of rotational trajectory optimality by naive observers.

Grunwald, Arthur J.; Kaiser, Mary K.

1989-01-01

180

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

181

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

182

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

183

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

184

Parallel projected variable metric algorithms for unconstrained optimization

NASA Technical Reports Server (NTRS)

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

Freeman, T. L.

1989-01-01

185

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

186

Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos

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

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

2008-01-01

187

An investigation of two network flow optimization algorithms

ABSTRACT An Investigation of Two Network Flow Optimization Algorithms. (May 1973) John Anders Steelquist, B. S. , Baylor University Directed by: Dr. Wilbur L. Meier, Jr. This thesis provides an investigation of two network optimization algorithms... and Conjectures 53 56 V. CONCLUSIONS AND RECOMMENDATIONS FOR FURTHER RESEARCH 64 Conclusions Recommendations 64 68 CHAPTER Page REFERENCES APPENDIX 72 A, OPERATOR'S MANUAL B. INPUT/OUTPUT ROUTINE C. SUBROUTINE SUPERKIL D. SUBROUTINE OUT...

Steelquist, John Anders

2012-06-07

188

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

189

Designing Stochastic Optimization Algorithms for Real-world Applications

NASA Astrophysics Data System (ADS)

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

Someya, Hiroshi; Handa, Hisashi; Koakutsu, Seiichi

190

The optimal path algorithm for emergency rescue for drilling accidents

Addressing flaws in the traditional Dijkstra Algorithm, this paper proposes an improved optimal path algorithm applicable to the GIS drilling accident emergency rescue system. To begin, the paper uses the modified comprehensive analytic hierarchy process to analyze various factors of road conditions, considers the element of urgency, then sets up the digraph with weights of the running time of the

Wenjing Ma; Yingzhuo Xu; Hui Xie

2009-01-01

191

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

192

DIRECT algorithm : A new definition of potentially optimal ...

discussed. Keywords: Global optimization; DIRECT algorithm; Two-points based sampling method; ... described by the following steps: the first step in the algorithm is the initialization, it consists by ..... Future work should be done on numerical tests to compare ... Mountain Conference On Iterative Methods, April 2, (2004).

chiter

2005-08-26

193

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

194

Serial and Parallel Genetic Algorithms as Function Optimizers

Parallel genetic algorithms are often very differentfrom the "traditional" genetic algorithmproposed by Holland, especially withregards to population structure and selectionmechanisms. In this paper we compare severalparallel genetic algorithms across a widerange of optimization functions in an attemptto determine whether these changes have positiveor negative impact on their problemsolvingcapabilities. The findings indicatethat the parallel structures perform as well asor ...

V. Scott Gordon; L. Darrell Whitley

1993-01-01

195

Terminating Decision Algorithms Optimally Tuomas Sandholm

often solve larger prob- lem instances than complete ones. The drawback is that one does not know provide at different times, and the algorithm's run-time distribution. We present a linear-time algorithm. Let us define the following symbols: SOLt ="Solution found by time t" (so, if a solution is found

Gordon, Geoffrey J.

196

Application of particle swarm optimization algorithm to image texture classification

NASA Astrophysics Data System (ADS)

This paper describes a kind of robust texture feature invariant to rotation and scale changes, which is the texture energy associated with a mask generated by particle swarm optimization algorithms. The detail procedure and algorithm to generate the mask is discussed in the paper. Furthermore, feature extraction experiments on aerial images are done. Experimental results indicate that the robust feature is effective and PSO-based algorithm is a viable approach for the "tuned" mask training problem.

Ye, Zhiwei; Zheng, Zhaobao; Zhang, Jinping; Yu, Xin

2007-12-01

197

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

198

In search of optimal clusters using genetic algorithms

Genetic Algorithms (GAs) are generally portrayed as search procedures which can optimize functions based on a limited sample of function values. In this paper, GAs have been used in an attempt to optimize a specified objective function related to a clustering problem. Several experiments on synthetic and real life data sets show the utility of the proposed method. K-Means is

C. A. Murthy; Nirmalya Chowdhury

1996-01-01

199

A memetic algorithm for global optimization in chemical process synthesis

Engineering optimization often deals with very large search spaces which are highly constrained by nonlinear equations that restrict the values of the continuous variables. In this contribution the development of a memetic algorithm (MA) for global optimization in the solution of a problem in the chemical process engineering domain is described. The combination of an evolutionary strategy and a local

Maren Urselmann; Guido Sand; Sebastian Engell

2009-01-01

200

Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

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

Tang, Xinggang; Zhang, Weihong; Zhu, Jihong

201

OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM

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

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

2010-06-15

202

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

203

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

204

A parallel variable metric optimization algorithm

NASA Technical Reports Server (NTRS)

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

Straeter, T. A.

1973-01-01

205

Stochastic Search for Signal Processing Algorithm Optimization

large number of different but mathematically equivalent formulas. When these formulas are implemented be represented by mathematical formulas and a single signal processing algorithm can be represented by many different, but mathematically equivalent, formulas (Auslander et al., 1996). Interestingly, when

206

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

207

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, UniversitÃ© de

208

Approximation algorithms for trilinear optimization with nonconvex ...

Apr 2, 2011 ... In this paper, we study trilinear optimization problems with ... first case is related to the biquadratic form studied by Zhang et al ...... [2] A. Einstein, B. Podolsky and N. Rosen, Can quantum-mechanical description of physical.

2011-04-02

209

Stochastic Search for Signal Processing Algorithm Optimization

large number of different but mathematically equivalent formulas. When these formulas are implemented different, but mathematically equivalent, formulas (Auslander et al., 1996). Interestingly, when optimization aims at finding the fastest formula. We present a new approach that successfully solves

210

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

211

Optimization of catalysts using specific, description-based genetic algorithms.

This paper deals with the key optimization task that has to be solved when improving the performance of many chemical processes--optimization of the catalysts used in the reaction via the optimization of its composition and preparation. A novel approach is presented that allows for the preservation of the advantages of genetic algorithms developed specifically for the optimization of catalytic materials but avoids the disadvantageous necessity to reimplement the algorithm when the scope of the optimized materials changes. Its main idea is to automatically generate problem-tailored implementations from requirements concerning the materials with a program generator. For the specification of such requirements, a formal description language, called catalyst description language, has been developed. PMID:18254615

Holena, Martin; Cukic, Tatjana; Rodemerck, Uwe; Linke, David

2008-02-01

212

Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

213

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

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

2014-01-01

214

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

215

A Genetic Algorithm Approach to Multiple-Response Optimization

Many designed experiments require the simultaneous optimization of multiple responses. A common approach is to use a desirability function combined with an optimization algorithm to find the most desirable settings of the controllable factors. However, as the problem grows even moderately in either the number of factors or the number of responses, conventional optimization algorithms can fail to find the global optimum. An alternative approach is to use a heuristic search procedure such as a genetic algorithm (GA). This paper proposes and develops a multiple-response solution technique using a GA in conjunction with an unconstrained desirability function. The GA requires that several parameters be determined in order for the algorithm to operate effectively. We perform a robust designed experiment in order to tune the genetic algorithm to perform well regardless of the complexity of the multiple-response optimization problem. The performance of the proposed GA method is evaluated and compared with the performance of the method that combines the desirability with the generalized reduced gradient (GRG) optimization. The evaluation shows that only the proposed GA approach consistently and effectively solves multiple-response problems of varying complexity.

Ortiz, Francisco; Simpson, James R.; Pignatiello, Joseph J.; Heredia-Langner, Alejandro

2004-10-01

216

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

217

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

218

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

219

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

220

Optimization of a Predictive Dialing Algorithm

Most of enterprises rely on the outbound call centers which play a very important role in Customer Relationship Management (CRM) and marketing. Outbound Calls centers that employ predictive dialing system obtain greater productivity and higher service levels by dynamically generating outbound traffic. An algorithm is proposed to provide a trade-off between outbound dialing rate and agent occupation in order to

Sonia Fourati; Sami Tabbane

2010-01-01

221

Terminating Decision Algorithms Optimally Tuomas Sandholm

at different times, and the algorithm's run- time distribution. We present a linear-time algo- rithm a probability estimate that a solution exists. Let us define the following symbols: SOLt ="Solution found solve larger problem instances than complete ones. The drawback is that one does not know whether

Gordon, Geoffrey J.

222

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

223

Parallel sorting algorithms for optimizing particle simulations

Real world particle simulation codes have to handle a huge number of particles and their interactions. Thus, parallel implementations are required to get suitable production codes. Parallel sorting is often used to organize the set of particles or to redistribute data for locality and load balancing concerns. In this article, the use and design of parallel sorting algorithms for parallel

Michael Hofmann; G. Runger; P. Gibbon; R. Speck

2010-01-01

224

Bilinear programming: An exact algorithm

The Bilinear Programming Problem is a structured quadratic programming problem whose objective function is, in general, neither convex nor concave. Making use of the formal linearity of a dual formulation of the problem, we give a necessary and sufficient condition for optimality, and an algorithm to find an optimal solution.

Giorgio Gallo; Aydin Ülkücü

1977-01-01

225

Robust gain-scheduled estimation: A convex solution

In this paper we present an algorithm for the systematic synthesis of robust gain-scheduled estimators through convex optimization. We consider uncertain linear parameter-varying (LPV) dynamical systems described in the standard LFT form, while the uncertainty and scheduling blocks in the interconnection are described by general dynamic and static full-block IQC-multipliers respectively. It is shown how to unify the recent results

Joost Veenmanand; Carsten W. Scherer

2011-01-01

226

A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm

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

Peibo, Duan; Changsheng, Zhang; Bin, Zhang

2014-01-01

227

Restarted local search algorithms for continuous black box optimization.

Several local search algorithms for real-valued domains (axis parallel line search, Nelder-Mead simplex search, Rosenbrock's algorithm, quasi-Newton method, NEWUOA, and VXQR) are described and thoroughly compared in this article, embedding them in a multi-start method. Their comparison aims (1) to help the researchers from the evolutionary community to choose the right opponent for their algorithm (to choose an opponent that would constitute a hard-to-beat baseline algorithm), (2) to describe individual features of these algorithms and show how they influence the algorithm on different problems, and (3) to provide inspiration for the hybridization of evolutionary algorithms with these local optimizers. The recently proposed Comparing Continuous Optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that in low dimensional spaces, the old method of Nelder and Mead is still the most successful among those compared, while in spaces of higher dimensions, it is better to choose an algorithm based on quadratic modeling, such as NEWUOA or a quasi-Newton method. PMID:22779407

Pošík, Petr; Huyer, Waltraud

2012-01-01

228

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

229

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

230

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

231

Wind Mill Pattern Optimization using Evolutionary Algorithms

31062 Toulouse Cedex 9, France jean-marc.alliot@irit.fr ABSTRACT When designing a wind farm layout, we]: Design Tools and Techniques--Computer-aided software engineering Keywords wind energy, wind farm layout dramatically; Gon- zalez's recent review [2] lists almost 150 bibliographic refer- ences for the optimal wind-turbine

232

PID Parameters Optimization by Using Genetic Algorithm

Time delays are components that make time-lag in systems response. They arise in physical, chemical, biological and economic systems, as well as in the process of measurement and computation. In this work, we implement Genetic Algorithm (GA) in determining PID controller parameters to compensate the delay in First Order Lag plus Time Delay (FOLPD) and compare the results with Iterative Method and Ziegler-Nichols rule results.

Mirzal, Andri; Furukawa, Masashi

2012-01-01

233

Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence

This paper analyzes convergence properties of the Bayesian optimization algorithm (BOA). It settles the BOA into the framework of problem decomposition used frequently in order to model and understand the behavior of simple genetic algorithms. The growth of the population size and the number of generations until convergence with respect to the size of a problem is theoretically analyzed. The theoretical results are supported by a number of experiments.

Pelikan, M.; Goldberg, D.E.; Cantu-Paz, E.

2000-01-19

234

Genetic algorithm for multi-objective experimental optimization.

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations). PMID:17048033

Link, Hannes; Weuster-Botz, Dirk

2006-12-01

235

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

NASA Astrophysics Data System (ADS)

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

Mohanty, Prases K.; Parhi, Dayal R.

2014-12-01

236

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

NASA Astrophysics Data System (ADS)

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

Mohanty, Prases K.; Parhi, Dayal R.

2014-08-01

237

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 writers extend their previous work by presenting two concurrent augmented Lagrangian genetic algorithms for optimization of large structures utilizing the multiprocessing capabilities of high-performance computers such as the Cray Y-MP 8/864 supercomputer. Efficiency of the algorithms has been investigated by applying them to four space structures including two high-rise building structures. It is observed that the performance of both algorithms improves with the size of the structure, making them particularly suitable for optimization of large structures. A maximum parallel processing speed of 7.7 is achieved for a 35-story tower (with 1,262 elements and 936 degrees of freedom), using eight processors. 9 refs.

Adeli, H.; Cheng, N. (Ohio State Univ., Columbus, OH (United States))

1994-07-01

238

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

239

Application of Ant Colony Optimization Algorithm to Multi-Join Query Optimization

Multi-join query optimization (MJQO) is an important technique for designing and implementing database manage system. It is\\u000a a crucial factor that affects the capability of database. This paper proposes a new algorithm to solve the problem of MJQO\\u000a based on ant colony optimization (ACO). In this paper, details of the algorithm used to solve MJQO problem have been interpreted,\\u000a including

Nana Li; Yujuan Liu; Yongfeng Dong; Junhua Gu

2008-01-01

240

Optimal Parallel Merging Algorithms on BSR

Merging is one of the most fundamental problems in computer science. It is well known that ?(N\\/p+loglogN) time is required to merge two sorted sequences each of length N on CRCW PRAM with p processors, where p⩽N log?N for any constant ?. We describe two optimal O(1) time solutions to the problem for p=N on BSR (Broadcasting with Selective Reduction).

Limin Xiang; Kazuo Ushijima

2000-01-01

241

Control optimization, stabilization and computer algorithms for aircraft applications

NASA Technical Reports Server (NTRS)

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

1975-01-01

242

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

243

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

244

NASA Astrophysics Data System (ADS)

A hybrid algorithm combining particle swarm optimization (PSO) algorithm with the Legendre pseudospectral method (LPM) is proposed for solving time-optimal trajectory planning problem of underactuated spacecrafts. At the beginning phase of the searching process, an initialization generator is constructed by the PSO algorithm due to its strong global searching ability and robustness to random initial values, however, PSO algorithm has a disadvantage that its convergence rate around the global optimum is slow. Then, when the change in fitness function is smaller than a predefined value, the searching algorithm is switched to the LPM to accelerate the searching process. Thus, with the obtained solutions by the PSO algorithm as a set of proper initial guesses, the hybrid algorithm can find a global optimum more quickly and accurately. 200 Monte Carlo simulations results demonstrate that the proposed hybrid PSO-LPM algorithm has greater advantages in terms of global searching capability and convergence rate than both single PSO algorithm and LPM algorithm. Moreover, the PSO-LPM algorithm is also robust to random initial values.

Zhuang, Yufei; Huang, Haibin

2014-02-01

245

Genetic Algorithms Can Improve the Construction of D-Optimal Experimental Designs

algorithms for constructing D- optimal designs are Monte Carlo algorithms, heuristics, that base on the idea that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k better results. Key-Words: - Genetic Algorithm, Memetic Algorithm, Design of Experiments, DOE, D

Zell, Andreas

246

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

247

Multiobjective optimal design of high frequency transformers using genetic algorithm

This paper deals with the multiobjective optimization (MO) design of high frequency (HF) transformers using genetic algorithms (GAs). In its most general form, the design problem requires minimizing the mass or overall dimensions of the core and windings as well as the loss of the transformer while ensuring the satisfaction of a number of constraints. In this contribution, the area

C. Versele; O. Deblecker; J. Lobry

2009-01-01

248

A genetic algorithm for optimizing off-farm irrigation scheduling

This paper examines the use of genetic algorithm (GA) optimization to identify water delivery schedules for an open-channel irrigation system. Significant objectives and important constraints are identified for this system, and suitable representations of these within the GA framework are developed. Objectives include maximizing the number of orders that are scheduled to be delivered at the requested time and minimizing

J. B. Nixon; G. C. Dandy; A. R. Simpson

2001-01-01

249

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

250

A locally optimal handoff algorithm for cellular communications

The design of handoff algorithms for cellular communication systems based on mobile signal strength measurements is considered. The design problem is posed as an optimization to obtain the best tradeoff between the expected number of service failures and expected number of handoffs, where a service failure is defined to be the event that the signal strength falls below a level

Venugopal V. Veeravalli; Owen E. Kelly

1997-01-01

251

HIGHLY PARALLEL EVOLUTIONARY ALGORITHMS FOR GLOBAL OPTIMIZATION, SYMBOLIC INFERENCE AND

, reproduction and selection. Mutation randomly perturbs a candidate solution, recombination decomposes two disÂ tinct solutions and then randomly mixes their parts to form a novel solution, reproduction replicates. G. Degli Antoni. 1 #12; 2. Parallel Cellular Evolutionary Algorithms for Global Optimization

Neumaier, Arnold

252

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

253

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

254

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

May 26, 2014 ... value optimization [1], compressed sensing [8, 9, 16], and decomposition methods for large-scale ...... In this section, we prove that Algorithm 1 is globally convergent from remote starting points. ...... Journal of the Institute of Mathematics and Its Applications. 6(1):76–90 ... In: ISBI '09: IEEE International Sym-.

2014-05-26

255

Finding the needle in the haystack: Algorithms for conformational optimization

Algorithms are given for comformational optimization of proteins. The protein folding problems is regarded as a problem of global energy mimimization. Since proteins have hundreds of atoms, finding the lowest-energy conformation in a many-dimensional configuration space becomes a computationally demanding problem.{copyright} {ital American Institute of Physics.}

Andricioaei, I.; Straub, J.E.

1996-09-01

256

Eddy-current testing with the Expected Improvement optimization algorithm

Eddy-current testing with the Expected Improvement optimization algorithm SÂ´andor Bilicz , Emmanuel presents an inverse problem methodology in the domain of non-destructive testing, and more precisely eddy-current in the light of preliminary numerical examples obtained using synthetic data. Keywords: eddy current testing

Paris-Sud XI, UniversitÃ© de

257

Optimization and Benchmark of Cryptographic Algorithms on Network Processors

With the increasing needs of security, cryptographic functions have been exploited in network devices. Besides time consuming, security protocols are flexible in algorithm selections. Fortunately, network processors, which serve as the backbone of intelligent network devices, hold performance and flexibility at the same time. In this article, we investigate several principles that can be used with implementing and optimizing cpptographic

Zhangxi Tan; Chuang Lin; Hao Yin; Bo Li

2004-01-01

258

Optimization and benchmark of cryptographic algorithms on network processors

With the increasing needs of security, cryptographic functions have been exploited in network devices. Besides time consuming, security protocols are flexible in algorithm selections. Fortunately, network processors, which serve as the backbone of intelligent network devices, hold performance and flexibility at the same time. In this article, we investigate several principles that can be used with implementing and optimizing cryptographic

Zhangxi Tan; Chuang Lin; Yanxi Li; Yixin Jiang

2003-01-01

259

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

260

E cient Approximation and Optimization Algorithms for Computational Metrology

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

Goodrich, Michael T.

261

Optimization of classification tasks by using genetic algorithms

We present an attempt to separate between two kinds of events, using Genetic Algorithms. Events were produced by a Monte Carlo generator and characterized by the most discriminant variables. For the separation between events, two approaches are investigated. First, discriminant function parameters and neural network connection weights are optimized. In a multidimensional search approach, hyper-planes and hyper-surfaces are computed. In

Mostafa Mjahed

2010-01-01

262

Algorithms for Optimizing Production DNA Sequencing Eva Czabarka

of duplex DNA strands, this process, in fact, allows us to sequence one read length from one strand at oneAlgorithms for Optimizing Production DNA Sequencing Eva Czabarka Goran Konjevod Madhav V. Marathe sequenced, reconstructed DNA segment. At first sight, this appears to be computationally hard. We construct

Percus, Allon

263

Comparison between Genetic Algorithms and Particle Swarm Optimization

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

Russell C. Eberhart; Yuhui Shi

1998-01-01

264

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

265

Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling

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

R. J. Abrahart

2004-01-01

266

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

267

Genetic-algorithm-based path optimization methodology for spatial decision

NASA Astrophysics Data System (ADS)

In this paper, we proposed a method based on GA to solve the path-optimization problem. Unlike the traditional methods, it considers many other factors besides the road length including the task assignment and its balance, which are beyond the capability of path analysis and make this problem a Combinatorial Optimization problem. It can't be solved by a traditional graph-based algorithm. This paper proposes a new algorithm that integrates the Graph Algorithm and Genetic Algorithm together to solve this problem. The traditional Graph-Algorithm is responsible for preprocessing data and GA is responsible for the global optimization. The goal is to find the best combination of paths to meet the requirement of time, cost and the reasonable task assignment. The prototype of this problem is named the TSP (Traveling Salesman Problem) problem and known as NP-Hard Problem. However, we demonstrate how these problems are resolved by the GA without complicated programming, the result proves it's effective. The technique presented in this paper is helpful to those GIS developer working on an intelligent system to provide more effective decision-making.

Yu, Liang; Bian, Fuling

2006-10-01

268

A New Particle Swarm Optimization Algorithm for Dynamic Environments

NASA Astrophysics Data System (ADS)

Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore the search space and some child swarms to exploit promising areas found by the parent swarm. To improve the search performance, when the search areas of two child swarms overlap, the worse child swarms will be removed. Moreover, in order to quickly track the changes in the environment, all particles in a child swarm perform a random local search around the best position found by the child swarm after a change in the environment is detected. Experimental results on different dynamic environments modelled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, including FMSO, a similar particle swarm algorithm for dynamic environments, for all tested environments.

Kamosi, Masoud; Hashemi, Ali B.; Meybodi, M. R.

269

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

270

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

271

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

272

NASA Astrophysics Data System (ADS)

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

Wang, Fan

273

A scatter learning particle swarm optimization algorithm for multimodal problems.

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

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

2014-07-01

274

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

Dervis Karaboga; Bahriye Basturk

2007-01-01

275

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

276

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

277

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

278

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

279

Computational experiments for local search algorithms for binary and mixed integer optimization

In this thesis, we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also ...

Zhou, Jingting, S.M. Massachusetts Institute of Technology

2010-01-01

280

Nonconvex Compressed Sensing by Nature-Inspired Optimization Algorithms.

The l0 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

2014-08-19

281

Minimizing separable convex functions subject to simple chain constraints

We show in the present paper that it is possible to minimize a separable convex function subject to simple chain constraints by a {open_quotes}Pool Adjacent Violators{close_quotes} algorithm. Our result unifies and extends some results previously obtained in the context of statistics and inventory control. We further show that a modified version of the algorithm determines the set of all optimal solutions to the problem. Finally, we show that yet another modified version solves an integer version of the problem.

Chakravarti, M.; Best, M.; Ubhaya, V.

1994-12-31

282

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 percent, up to 100 R decreases in FTIT, and increases of as much as 12 percent 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

283

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

284

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.

285

Resource-Conscious Optimization of Cryptographic Algorithms on an Embedded Architecture

Resource-Conscious Optimization of Cryptographic Algorithms on an Embedded Architecture Wassim {wbassale,kaeli}@ece.neu.edu Abstract Cryptographic algorithms are widely used in embedded systems commonly used cryptographic algorithms. We analyze their performance on an embedded system targeting

Kaeli, David R.

286

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

287

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

288

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

289

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm

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

Kosmas, O T; Vlachos, D S; Simos, T E

2008-01-01

290

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

291

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

292

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm

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

O. T. Kosmas; D. S. Vlachos

2009-05-04

293

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

294

Optimization of experimental design in fMRI: a general framework using a genetic algorithm

Optimization of experimental design in fMRI: a general framework using a genetic algorithm Tor D uses a genetic algorithm (GA), a class of flexible search algorithms that optimize designs with respect genetic algorithms may be applied to experimental design for fMRI, and we use the framework to explore

295

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

296

Stochastic search in structural optimization - Genetic algorithms and simulated annealing

NASA Technical Reports Server (NTRS)

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

Hajela, Prabhat

1993-01-01

297

Dynamic Selection of Optimal Cryptographic Algorithms in a Runtime Environment

This paper presents the results of research conducted by the author in support of dynamic selection of optimal cryptographic algorithms in a runtime environment (DSOCARE), the author's doctoral dissertation. Based on DSOCARE framework, a first full-scale proof-of-concept prototype was developed by the author in Java and C#\\/VB. The prototype was used to perform collection, selection, and reporting functions on common

Jalal Raissi

2006-01-01

298

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

299

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.

2014-12-01

300

A Genetic Algorithm to Optimize a Tweet for Retweetability

Twitter is a popular microblogging platform. When users send out messages, other users have the ability to forward these messages to their own subgraph. Most research focuses on increasing retweetability from a node's perspective. Here, we center on improving message style to increase the chance of a message being forwarded. To this end, we simulate an artificial Twitter-like network with nodes deciding deterministically on retweeting a message or not. A genetic algorithm is used to optimize message composition, so that the reach of a message is increased. When analyzing the algorithm's runtime behavior across a set of different node types, we find that the algorithm consistently succeeds in significantly improving the retweetability of a message.

Hochreiter, Ronald

2014-01-01

301

Advanced metaheuristic algorithms for laser optimization in optical accelerator technologies

NASA Astrophysics Data System (ADS)

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

Tomizawa, Hiromitsu

2011-10-01

302

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

303

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

Jacob Robinson; Seelig Sinton; Yahya Rahmat-Samii

2002-01-01

304

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. © 2014 Wiley Periodicals, Inc. PMID:25487276

Lehtola, Susi

2015-02-15

305

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

306

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

307

Microwave-based medical diagnosis using particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

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

Modiri, Arezoo

308

Quantum-based algorithm for optimizing artificial neural networks.

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

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

2013-08-01

309

Convex analysis and ideal tensegrities

NASA Astrophysics Data System (ADS)

A theoretical framework based on convex analysis is formulated and developed to study tensegrity structures under steady-state loads. Many classical results for ideal tensegrities are rationally deduced from subdifferentiable models in a novel mechanical perspective. Novel energy-based criteria for rigidity and pre-stressability are provided, allowing to formulate numerical algorithms for computations.

Maceri, Franco; Marino, Michele; Vairo, Giuseppe

2011-11-01

310

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

311

A hybrid genetic algorithm for a class of global optimization problems with box constraints

In this paper, a new hybrid genetic algorithm is proposed, which combines the genetic algorithm with hill-climbing search steps differently from some former algorithms. The new algorithm can be widely applied to a class of global optimization problems for continuous functions with box constraints. Finally, numerical examples show that this algorithm can yield the global optimum with high efficiency.

Quan Yuan; Zhiqing He; Huinan Leng

2008-01-01

312

The proportional-integral-derivative (PID) controllers were the most popular controllers of this century because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, PID controllers are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. The computational intelligence has purposed genetic algorithms (GA) and particle swarm optimization (PSO) as opened paths

Mohammed El-Said El-Telbany

2007-01-01

313

Optimal design of link systems using successive zooming genetic algorithm

NASA Astrophysics Data System (ADS)

Link-systems have been around for a long time and are still used to control motion in diverse applications such as automobiles, robots and industrial machinery. This study presents a procedure involving the use of a genetic algorithm for the optimal design of single four-bar link systems and a double four-bar link system used in diesel engine. We adopted the Successive Zooming Genetic Algorithm (SZGA), which has one of the most rapid convergence rates among global search algorithms. The results are verified by experiment and the Recurdyn dynamic motion analysis package. During the optimal design of single four-bar link systems, we found in the case of identical input/output (IO) angles that the initial and final configurations show certain symmetry. For the double link system, we introduced weighting factors for the multi-objective functions, which minimize the difference between output angles, providing balanced engine performance, as well as the difference between final output angle and the desired magnitudes of final output angle. We adopted a graphical method to select a proper ratio between the weighting factors.

Kwon, Young-Doo; Sohn, Chang-hyun; Kwon, Soon-Bum; Lim, Jae-gyoo

2009-07-01

314

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

315

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

316

Horizontal axis wind turbine systems: optimization using genetic algorithms

NASA Astrophysics Data System (ADS)

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

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

2001-10-01

317

Multivariable optimization of liquid rocket engines using particle swarm algorithms

NASA Astrophysics Data System (ADS)

Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.

Jones, Daniel Ray

318

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

319

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

320

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

321

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

322

Order from Chaos: A Sampling of Stochastic Optimization Algorithms

NSDL National Science Digital Library

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

Joiner, David

323

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

324

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

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

K. Atashkari; N. Nariman-Zadeh; A. Pilechi; A. Jamali; X. Yao

2005-01-01

325

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

326

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

327

Optimization of reinforced concrete frame bridges by parallel genetic and memetic algorithms

This paper deals with the automated design and economic optimization of reinforced concrete frame bridges typically used in road construction. The optimization algorithms applied are (i) parallel genetic algorithms (PGA) and (ii) parallel memetic algorithms (PMA). The evaluation of solutions follows the Spanish Code for structural concrete. Stress resultants and envelopes of framed structures are computed by an internal matrix

C. Perea; M. Baitsch; D. Hartmann

328

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

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

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

329

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 case scenario. This paper presents a Genetic Algorithm for the optimization of word length for both using Genetic Algorithm (GA). The solutions found by the GA are then evaluated for power evaluation. GA

Arslan, Tughrul

330

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

Smith, Alice E.

331

Local Search Genetic Algorithm for Optimization of Highly Reliable Communications Networks

Local Search Genetic Algorithm for Optimization of Highly Reliable Communications Networks Berna Turkey berna@rorqual.cc.metu.edu.tr Abstract This paper presents a genetic algorithm (GA. Genetic algorithms (GA) have recently found their way in combinatorial optimization approaches to reliable

Smith, Alice E.

332

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

333

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

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

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

2006-01-01

334

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

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

Coello, Carlos A. Coello

335

and equipment. The paper starts with evaluation of digital measuring algorithms, and gradually introducesDesign Optimization and Performance Evaluation of the Relaying Algorithms, Relays and Protective quality measures for designing, optimizing, setting and evaluating the protective relaying algorithms

336

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

337

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

338

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

339

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

340

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

341

Design optimization of space launch vehicles using a genetic algorithm

NASA Astrophysics Data System (ADS)

The United States Air Force (USAF) continues to have a need for assured access to space. In addition to flexible and responsive spacelift, a reduction in the cost per launch of space launch vehicles is also desirable. For this purpose, an investigation of the design optimization of space launch vehicles has been conducted. Using a suite of custom codes, the performance aspects of an entire space launch vehicle were analyzed. A genetic algorithm (GA) was employed to optimize the design of the space launch vehicle. A cost model was incorporated into the optimization process with the goal of minimizing the overall vehicle cost. The other goals of the design optimization included obtaining the proper altitude and velocity to achieve a low-Earth orbit. Specific mission parameters that are particular to USAF space endeavors were specified at the start of the design optimization process. Solid propellant motors, liquid fueled rockets, and air-launched systems in various configurations provided the propulsion systems for two, three and four-stage launch vehicles. Mass properties models, an aerodynamics model, and a six-degree-of-freedom (6DOF) flight dynamics simulator were all used to model the system. The results show the feasibility of this method in designing launch vehicles that meet mission requirements. Comparisons to existing real world systems provide the validation for the physical system models. However, the ability to obtain a truly minimized cost was elusive. The cost model uses an industry standard approach, however, validation of this portion of the model was challenging due to the proprietary nature of cost figures and due to the dependence of many existing systems on surplus hardware.

Bayley, Douglas James

342

This paper introduces a new hybrid algorithmic nature in- spired approach based on the concepts of the Honey Bees Mating Opti- mization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO

Yannis Marinakis; Magdalene Marinaki; Nikolaos F. Matsatsinis

2007-01-01

343

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

344

Global optimization algorithms for semi-infinite and generalized semi-infinite programs

The goals of this thesis are the development of global optimization algorithms for semi-infinite and generalized semi-infinite programs and the application of these algorithms to kinetic model reduction. The outstanding ...

Lemonidis, Panayiotis

2008-01-01

345

Skull removal in MR images using a modified artificial bee colony optimization algorithm.

Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications. PMID:25059256

Taherdangkoo, Mohammad

2014-01-01

346

Cellular Neural-Like Algorithms with Heuristics for Solving Combinatorial Optimization Problems

We study a heuristic paradigm of neural network algorithms for solving combinatorial optimization problems in a cellular architecture. As an illustration, we present a cellular neural-like algorithm for solving approximately the maximum independent set problem.

S. M. Achasova

1997-01-01

347

In the first part of this two-paper series, we presented a conceptual algorithm for the optimal control of constrained switched systems and proved that this algorithm generates a sequence of points that converge to a ...

Vasudevan, Ram

348

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

349

Using Social Emotional Optimization Algorithm to Direct Orbits of Chaotic Systems

NASA Astrophysics Data System (ADS)

Social emotional optimization algorithm (SEOA) is a new novel population-based stochastic optimization algorithm. In SEOA, each individual simulates one natural person. All of them are communicated through cooperation and competition to increase social status. The winner with the highest status will be the final solution. In this paper, SEOA is employed to solve the directing orbits of chaotic systems, simulation results show this new variant increases the performance significantly when compared with particle swarm optimization algorithm.

Cui, Zhihua; Shi, Zhongzhi; Zeng, Jianchao

350

NASA Astrophysics Data System (ADS)

Genetic and distribution building algorithms with binary representation are analyzed. A property of convergence to the optimal solution is discussed. A novel convergence prediction method is proposed and investigated. The method is based on analysis of gene value probabilities distribution dynamics, thus it can predict gene values of the optimal solution to which the algorithm converges. The results of investigations for the optimal prediction algorithm performance are presented.

Sopov, E.; Semenkina, O.

2015-01-01

351

The Generalized Generation Gap (G3) algorithm is one of the most efficient and effective state-of-the-art real- coded genetic algorithms (RCGAs) for unconstrained global optimization. However, its performance on multimodal optimization problems is known to be poor compared to unimodal optimization problems. The G3 algorithm currently relies on crossover operations only. The objective of this paper is to augment the G3

Jason Teo

2006-01-01

352

Reducing aerodynamic vibration with piezoelectric actuators: a genetic algorithm optimization

NASA Astrophysics Data System (ADS)

Modern high performance aircraft fly at high speeds and high angles of attack. This can result in "buffet" aerodynamics, an unsteady turbulent flow that causes vibrations of the wings, tails, and body of the aircraft. This can result in decreased performance and ride quality, and fatigue failures. We are experimenting with controlling these vibrations by using piezoceramic actuators attached to the inner and outer skin of the aircraft. In this project, a tail or wing is investigated. A "generic" tail finite element model is studied in which individual actuators are assumed to exactly cover individual finite elements. Various optimizations of the orientations and power consumed by these actuators are then performed. Real coded genetic algorithms are used to perform the optimizations and a design space approximation technique is used to minimize costly finite element runs. An important result is the identification of a power consumption threshold for the entire system. Below the threshold, vibration control performance of optimized systems decreases with decreasing values of power supplied to the entire system.

Hu, Zhenning; Jakiela, Mark; Pitt, Dale M.; Burnham, Jay K.

2004-07-01

353

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

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

Nurre, Sarah G.; Carlson, Jeffrey J.

2014-01-01

354

Inner Random Restart Genetic Algorithm for Practical Delivery Schedule Optimization

NASA Astrophysics Data System (ADS)

A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires solving several tens to hundreds but less than 2 thousands cities Traveling Salesman Problems (TSP) within interactive response time (less than about 3 second), with expert-level accuracy (less than about 3% of error rate). Further, to make things more difficult, the optimization is subjects to special requirements or preferences of each various delivery sites, persons, or societies. To meet these requirements, an Inner Random Restart Genetic Algorithm (Irr-GA) is proposed and developed. This method combines meta-heuristics such as random restart and GA having different types of simple heuristics. Such simple heuristics are 2-opt and NI (Nearest Insertion) methods, each applied for gene operations. The proposed method is hierarchical structured, integrating meta-heuristics and heuristics both of which are multiple but simple. This method is elaborated so that field experts as well as field engineers can easily understand to make the solution or method easily customized and extended according to customers' needs or taste. Comparison based on the experimental results and consideration proved that the method meets the above requirements more than other methods judging from not only optimality but also simplicity, flexibility, and expandability in order for this method to be practically used.

Sakurai, Yoshitaka; Takada, Kouhei; Onoyama, Takashi; Tsukamoto, Natsuki; Tsuruta, Setsuo

355

Modulus of convexity for operator convex functions

Given an operator convex function $f(x)$, we obtain an operator-valued lower bound for $cf(x) + (1-c)f(y) - f(cx + (1-c)y)$, $c \\in [0,1]$. The lower bound is expressed in terms of the matrix Bregman divergence. A similar inequality is shown to be false for functions that are convex but not operator convex.

Isaac H. Kim

2014-07-08

356

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

357

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms Chris implemented in WEKA's standard distribution, spanning 2 ensemble meth- ods, 10 meta-methods, 27 base, Experimentation Keywords Model selection; Hyperparameter optimization; WEKA 1. INTRODUCTION Increasingly, users

Hutter, Frank

358

Left ventricle segmentation in MRI via convex relaxed distribution matching.

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

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

2013-12-01

359

Continuous Optimization A unified ant colony optimization algorithm for continuous optimization

of Complex System Simulation, Beijing Institute of System Engineering, 10 An Xiang Bei Li Rd., Beijing, China, Montes de Oca, Aydin, StÃ¼tzle, and Dorigo (2011) proposed IACOR-LS, an incremental ant colony algorithm

Libre de Bruxelles, UniversitÃ©

360

GenMin: An enhanced genetic algorithm for global optimization

NASA Astrophysics Data System (ADS)

A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the objective function. The test example given takes only a few seconds to run.

Tsoulos, Ioannis G.; Lagaris, I. E.

2008-06-01

361

NETWORK OPTIMIZATION FOR STEADY FLOW AND WATER HAMMER USING GENETIC ALGORITHMS

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

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

2005-01-01

362

Feature optimization in chemometric algorithms for explosives detection

NASA Astrophysics Data System (ADS)

This paper details the use of a genetic algorithm (GA) as a method to preselect spectral feature variables for chemometric algorithms, using spectroscopic data gathered on explosive threat targets. The GA was applied to laserinduced breakdown spectroscopy (LIBS) and ultraviolet Raman spectroscopy (UVRS) data, in which the spectra consisted of approximately 10000 and 1000 distinct spectral values, respectively. The GA-selected variables were examined using two chemometric techniques: multi-class linear discriminant analysis (LDA) and support vector machines (SVM), and the performance from LDA and SVM was fed back to the GA through a fitness function evaluation. In each case, an optimal selection of features was achieved within 20 generations of the GA, with few improvements thereafter. The GA selected chemically significant signatures, such as oxygen and hydron peaks from LIBS spectra and characteristic Raman shifts for AN, TNT, and PETN. Successes documented herein suggest that this GA approach could be useful in analyzing spectroscopic data in complex environments, where the discriminating features of desired targets are not yet fully understood.

Pinkham, Daniel W.; Bonick, James R.; Woodka, Marc D.

2012-06-01

363

An evolutionary technique based on K-Means algorithm for optimal clustering in RN

A genetic algorithm-based efficient clustering technique that utilizes the principles of K-Means algorithm is described in this paper. The algorithm called KGA-clustering, while exploiting the searching capability of K-Means, avoids its major limitation of getting stuck at locally optimal values. Its superiority over the K-Means algorithm and another genetic algorithm-based clustering method, is extensively demonstrated for several artificial and real

Sanghamitra Bandyopadhyay; Ujjwal Maulik

2002-01-01

364

NASA Astrophysics Data System (ADS)

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

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

2011-10-01

365

A Parallel Inertial Proximal Optimization Method - Optimization Online

Hilbert space the sum of a finite number of proper, lower semicontinuous convex functions ... convex programming problems and for block-separable convex optimization problems ... Hm will denote the components of a generic element x of H.

2011-07-01

366

An optimized parallel LSQR algorithm for seismic tomography

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

367

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

Lloyd S. Shapley

1971-01-01

368

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

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

2003-01-01

369

This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV). The HEV control strategy is the algorithm according to which energy is produced, used, and saved. Therefore, optimal management of the energy components is a key element for the success of a HEV. In this study, based on

Morteza Montazeri-Gh; Amir Poursamad; Babak Ghalichi

2006-01-01

370

Hybrid genetic algorithms and artificial neural networks for complex design optimization in CFD

The present paper is devoted to the study of design optimization strategies in the particular framework of complex computational fluid dynamics. Genetic algorithms are chosen as the optimization strategy, thanks to their robustness and flexibility. Two ways are explored to improve the behaviour of genetic algorithms in order to increase the efficiency of the search. First, approximated pre-evaluations based on

R. Duvigneau; M. Visonneau

2004-01-01

371

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

Jayaraman Krishnasamy; Martin Hosek; Jairo Moura

2011-01-01

372

Semi-deterministic and genetic algorithms for global optimization of microfluidic protein folding

Semi-deterministic and genetic algorithms for global optimization of microfluidic protein folding of a fast microfluidic protein folding device. The aim of the latter design is to reduce mixing times protein folding devices design. Section 3 presents three global optimization algorithms with associated

Santiago, Juan G.

373

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

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

Wenjun, Liu; Qingguo, Li

2014-01-01

374

Design Optimization of electromagnetic actuator by genetic algorithm

Keywords: finite element method, magnetic force, genetic algorithm, electromagnetic actuator. 1. Introduction ..... diversity, the association of the genetic algorithm approach with ... for solving network design problem” European

ELBEZ

2008-02-26

375

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

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

2014-01-01

376

A hybrid genetic algorithm and bacterial foraging approach for global optimization

The social foraging behavior of Escherichia coli bacteria has been used to solve optimization problems. This paper pro- poses a hybrid approach involving genetic algorithms (GA) and bacterial foraging (BF) algorithms for function optimiza- tion problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover,

Dong Hwa Kim; Ajith Abraham; Jae Hoon Cho

2007-01-01

377

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

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

West, Douglas B.

378

A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications in

A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications obtained by applying a distributed genetic algorithm to a problem of parameter op- timization in medical the performance of our system. A distributed genetic algorithm supervising this process allowed to improve of some

Lanconelli, Nico

379

Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques

Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques techniques and whose constraints are too complex for conventional genetic algorithm. The main idea is the han- dling of sub-domains of the CSP variables by the genetic algorithm. The population of the genetic

Paris-Sud XI, UniversitÃ© de

380

Optimizing core-shell nanoparticle catalysts with a genetic algorithm Nathan S. Froemming 2009 A genetic algorithm is used with density functional theory to investigate the catalytic properties to be effective for oxygen reduction. The genetic algorithm starts by creating an initial population of random

Henkelman, Graeme

381

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

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

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

2014-01-01

382

Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm

NASA Technical Reports Server (NTRS)

A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.

Holst, Terry L.; Pulliam, Thomas H.

2001-01-01

383

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

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

384

Ultra-fast fluence optimization for beam angle selection algorithms

NASA Astrophysics Data System (ADS)

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

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

2014-03-01

385

Optimized Uncertainty Quantification Algorithm Within a Dynamic Event Tree Framework

Methods for developing Phenomenological Identification and Ranking Tables (PIRT) for nuclear power plants have been a useful tool in providing insight into modelling aspects that are important to safety. These methods have involved expert knowledge with regards to reactor plant transients and thermal-hydraulic codes to identify are of highest importance. Quantified PIRT provides for rigorous method for quantifying the phenomena that can have the greatest impact. The transients that are evaluated and the timing of those events are typically developed in collaboration with the Probabilistic Risk Analysis. Though quite effective in evaluating risk, traditional PRA methods lack the capability to evaluate complex dynamic systems where end states may vary as a function of transition time from physical state to physical state . Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. A limitation of DPRA is its potential for state or combinatorial explosion that grows as a function of the number of components; as well as, the sampling of transition times from state-to-state of the entire system. This paper presents a method for performing QPIRT within a dynamic event tree framework such that timing events which result in the highest probabilities of failure are captured and a QPIRT is performed simultaneously while performing a discrete dynamic event tree evaluation. The resulting simulation results in a formal QPIRT for each end state. The use of dynamic event trees results in state explosion as the number of possible component states increases. This paper utilizes a branch and bound algorithm to optimize the solution of the dynamic event trees. The paper summarizes the methods used to implement the branch-and-bound algorithm in solving the discrete dynamic event trees.

J. W. Nielsen; Akira Tokuhiro; Robert Hiromoto

2014-06-01

386

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

Deb, Suash; Yang, Xin-She

2014-01-01

387

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

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

Yu, Zhang; Yang, Xiaomei

2013-01-01

388

Optimization of Power Systems Using Real Coded Genetic Algorithms

NASA Astrophysics Data System (ADS)

This talk highlights the recently proposed Real Coded Crossover Operator, called the Laplace Crossover (LX) of [1] and Real Coded Mutation Operator called Power Mutation (PM) of [2], wherein The performance of LX and PM is compared with Heuristic Crossover (HX) and Non-Uniform Mutation (NUM) and Makinen, Periaux and Toivanen Mutation (MPTM). The test bed is a set of 20 test problems available in global optimization literature. Various performance criterion like computational cost, success rate, solution quality, efficiency and reliability are reported using two kinds of analysis. The results show that LX-PM outperforms all other GAs considered. In this paper, the above algorithms are extended for obtaining global optimal solution of constrained optimization problems. Constraints are handled using the parameter less approach proposed by Deb and the six RCGAs described above are modified accordingly. Comparison is shown with other existing RCGAs using Simulated Binary Crossover (SBX) and Polynomial Mutation (POL) of [3], [4]. Inclusion of two operators, SBX and POL, gives rise to two more combinations namely, LX with POL and SBX with PM. Two new RCGAs namely, LX-POL and SBX-PM are proposed by taking these two operators into account. Thus, in all, nine RCGAs are used for comparative study, namely: LX-POL, LX-PM, LX-MPTM, LX-NUM, HX-PM, HX-MPTM, HX-NUM, SBX-POL and SBX-PM. A set of 25 benchmark test problems are chosen, consisting of linear/nonlinear objective function and equality/inequality constraint. Comparison is made with respect to percentage of success, the average number of function evaluations and execution of successful runs. It is observed that the overall success rate of LX-POL is better than all other RCGAs. Based on extensive analysis, it is concluded that LX-POL clearly outperform other RCGAs considered in this study. The problem of optimization of Directional Over current Relay is modeled as a nonlinear constrained optimization problem. It is required to compute the values of the decision variables called "Relays," which control the act of isolation of faulty lines from the system without disturbing the healthy lines. The objective function to be minimized is the sum of the operating times of all the primary relays, which are expected to operate in order to clear the faults of their corresponding zones. The constraints are bounds on all decision variables, complexly interrelated times of the various relays (called selectivity constraints) and restrictions on each term of the objective function to be between certain specified limits. The IEEE 3-bus, 4-bus, 6-bus, 14-bus and 30-bus systems are considered. The complexity of the problem increases as the number of line increases. Larger systems have more decision variables and constraints. All the cases of this constrained optimization problem are solved by all the RCGAs mentioned above, namely LX-PM, LXMPTM, LX-NUM, LX-POL, HX-MPTM, HX-NUM, HX-MPTM, SBX-POL, SBX-NUM. The results are compared amongst themselves as well as with the previously quoted results using Random Search Technique of [5] as described in [6]. It is concluded that all the methods of LX family are able to give satisfactory results for each of the cases. However, LX-POL outperforms all other methods for each of the cases.

Deep, Kusum

2008-10-01

389

NASA Astrophysics Data System (ADS)

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

Semwal, Girish; Rastogi, Vipul

2014-01-01

390

Solution of transient optimization problems by using an algorithm based on nonlinear programming

NASA Technical Reports Server (NTRS)

An algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.

Teren, F.

1977-01-01

391

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

392

An application of optimal vector quantization on image compression is studied. A neural network structure for obtaining the optimal codebook for the vector quantizer (VQ) is employed. This structure is based on Kohonen's self-organizing map (SOM), whose learning algorithm provides an optimal codebook for a training sequence. It is demonstrated that the SOM complies, in general, with the Max-Lloyd's conditions

Juan A. Corral; Miguel Guerrero; Pedro J. Zufiria

1994-01-01

393

This study utilizes genetic algorithm to minimize the condition number of Hermitian matrix of influence coefficient (HMIC) to reduce the computation errors in balancing procedure. Then, the optimal locations of balancing planes and sensors would be obtained as fulfilling optimization. The finite element method is used to determine the steady-state response of flexible rotor-bearing systems. The optimization improves the balancing

Tsu-Wei Lin; Yuan Kang; Chun-Chieh Wang; Chuan-Wei Chang; Chih-Pin Chiang

2005-01-01

394

In this paper, the optimal expansion of a power transmission network by addition of new connection links is addressed. Optimality is searched with respect to two objectives: the transmission reliability efficiency and the cost of the added transmission links. The multi-objective optimization problem is tackled by means of three different genetic algorithm paradigms, opportunely biased to give preference to solutions

F. Cadini; E. Zio; C. A. Petrescu

2010-01-01

395

Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

Fault diagnosis of sensor timely and accurately is very important to improve the reliable operation of systems. In the study, fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine is presented in the paper, where chaos particle swarm optimization is chosen to determine the parameters of SVM. Chaos particle swarm optimization is a kind of

Chenglin Zhao; Xuebin Sun; Songlin Sun; Ting Jiang

2011-01-01

396

Optimization of process parameters in stereolithography using genetic algorithm

NASA Astrophysics Data System (ADS)

Stereolithography is the most popular RP process in which intricate models are directly constructed from a CAD package by polymerizing a plastic monomer. The application range is still limited, because dimensional accuracy is still inferior to that of conventional machining process. The ultimate dimensional accuracy of a part built on a layer-by-layer basis depends on shrinkage which depend on many factors such as layer thickness, hatch spacing, hatch style, hatch over cure and fill cure depth. The influence of the above factors on shrinkage in X and Y directions fit to the nonlinear pattern. A particular combination of process variables that would result same shrinkage rate in both directions would enable to predict shrinkage allowance to be provided on a part and hence the CAD model could be constructed including shrinkage allowance. In this concern, the objective of the present work is set as determination of process parameters to have same shrinkage rate in both X and Y directions. A genetic algorithm (GA) is proposed to find optimal process parameters for the above objective. This approach is an analytical approach with experimental sample data and has great potential to predict process parameters for better dimensional accuracy in stereolithography process.

Chockalingam, K.; Jawahar, N.; Vijaybabu, E. R.

2003-10-01

397

Optimization on robot arm machining by using genetic algorithms

NASA Astrophysics Data System (ADS)

In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the end-effector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.

Liu, Tung-Kuan; Chen, Chiu-Hung; Tsai, Shang-En

2007-12-01

398

New Hybrid Optimization Algorithms for Machine Scheduling Problems

is with the ISyE department, University of Wisconsin-Madison, Madison, WI. 53706 USA, and ... §III develops new hybrid algorithms. §IV establishes some properties of the algorithms. ...... When interpreted in the context of machine scheduling.

2006-12-03

399

Geodesics and an Optimal Control Algorithm C. Yalc n Kaya

] presented two fast subdivision algorithms (which we call here the L&R algorithms) for the evaluation of B-spline- veloped. It is simple to implement, and works well in prac- tice, with no need for an initial guess

Kaya, Yalcin

400

Reliability Optimization of Series-Parallel Systems Using a Genetic Algorithm David W. Coit, IEEE Optimization of Series-Parallel Systems Using a Genetic Algorithm Key Words - Genetic algorithm, Combinatorial genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine

Smith, Alice E.

401

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

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

Marco Dorigo; Thomas Stützle

402

Hybrid genetic algorithm for optimization problems with permutation property

Permutation property has been recognized as a common but challenging feature in combinatorial problems. Because of their complexity, recent research has turned to genetic algorithms to address such problems. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore, in this study a hybrid genetic algorithm was

Hsiao-fan Wang; Kuang-yao Wu

2004-01-01

403

A new hybrid genetic algorithm for global optimization

In this paper a Hybrid Interval Genetic algorithm (HIG) is presented. The algorithm consists of two phases: In the first phase, interval arithmetic and especially an interval branch--and--bound algorithm is used to obtain small regions where candidate solutions lie. In this way, a population of potential solutions is initialized and initial bounds for the global minimum $f^*$ are obtained. In

D. G. Sotiropoulos; E. C. Stavropoulos; M. N. Vrahatis

1997-01-01

404

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

NASA Astrophysics Data System (ADS)

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

Bekda?, Gebrail; Nigdeli, Sinan Melih

2012-09-01

405

NASA Astrophysics Data System (ADS)

This paper combines a verified interval optimization method with the FEM for designing structures, which is denominated as the Hybrid Interval Genetic Algorithm (HIGA). This algorithm can neglect formulated equations and interval analysis, and while determining the optimum interval parameters. Furthermore, it can also maximize the design scope. In this paper, this algorithm is implemented for both a truss and frame structure. The interval optimizations include the static and dynamic responses of these structures. The results show that the algorithm which combines the IGA with the FEM can determine the feasible interval design parameters of structures with allowable objective errors.

Shiau, Ting-Nung; Kang, Chung-Hao; Liu, De-Shin; Hsu, Wei-Chun

406

Quantized phase optimization of two-dimensional Fourier kinoforms by a genetic algorithm

NASA Astrophysics Data System (ADS)

We have developed a phase optimization method of a quantized kinoform by a genetic algorithm. Because the genetic algorithm inherently deals with discrete values, the quantized phase of the kinoform can be easily estimated. The two-dimensional Fourier kinoform can utilize effectively the periodicity of the discrete Fourier transform in the genetic algorithm. This condition enables us to perform the crossover process that is one of the processes in genetic algorithm without a spatial bandwidth of the kinoform. The optimization has been performed successfully in computer simulation. The optically reconstructed image agrees well with the theoretical one.

Yoshikawa, N.; Itoh, M.; Yatagai, T.

1995-04-01

407

A design optimization tool of earth-to-air heat exchanger using a genetic algorithm

Advancement in genetic algorithm (GA) optimization tools for design applications, coupled with techniques of soft computing, have led to new possibilities in the way computers interact with the optimization process. In this paper, the concept of goal-oriented GA has been used to design a tool for evaluating and optimizing various aspects of earth-to-air heat exchanger behavior. A new optimization method

Rakesh Kumar; A. R. Sinha; B. K. Singh; U. Modhukalya

2008-01-01

408

A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

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

Liu, Wen

2014-01-01

409

Optimized switching algorithm for synchronized switch damping for multimodal excitation

NASA Astrophysics Data System (ADS)

Shunted piezoceramics can be used to dissipate vibration energy of a host structure and therefore reduce vibration amplitudes. The piezoceramic converts a portion of the mechanical energy into electric energy which is then dissipated in an electric network. One semi-active control technique is the synchronized switch damping on inductance (SSDI), which has a good damping performance and can adapt to a wide range of excitation frequencies. In the standard SSDI a switch is closed during maximum deformation for one half of the electrical period time. This results in an inversion of the electrical charge. For the rest of the half-period the switch is opened and the charge remains constant. This results in a nearly rectangular voltage signal, which is in antiphase with the deformation velocity. In case of multimodal excitation, more sophisticated switching laws are developed with the aim to extract vibration energy from higher modes (i.e. Richard). This paper describes a novel multimodal switching law for vibration damping. An observer is designed to obtain an estimation of the first two vibration modes, which are used to determine the switching times. In simulations the increase in energy dissipation is evaluated and compared to the standard SSDI technique. With the new switching algorithm an improvement in energy dissipation is observed. The theoretical results are validated by measurements carried out on a clamped-free beam. The location of the piezoceramics is chosen to optimize the electro-mechanical coupling with the first vibration mode of the beam. The modal observer is realized in a realtime environment. Measurements show a good agreement with the theoretical results.

Schwarzendahl, Sebastian M.; Han, Xu; Neubauer, Marcus; Wallaschek, Jörg

2010-04-01

410

This thesis presents efficient algorithms that give optimal or near-optimal solutions for problems with non-linear objective functions that arise in discrete, continuous and robust optimization. First, we present a general ...

Mittal, Shashi, Ph. D. Massachusetts Institute of Technology

2011-01-01

411

A near optimal guidance algorithm for aero-assisted orbit transfer

NASA Technical Reports Server (NTRS)

The paper presents a near optimal guidance algorithm for aero-assited orbit plane change, based on minimizing the energy loss during the atmospheric portion of the maneuver. The guidance algorithm makes use of recent results obtained from energy state approximations and singular perturbation analysis of optimal heading change for a hypersonic gliding vehicle. This earlier work ignored the terminal constraint on altitude needed to insure that the vehicle exits that atmosphere. Thus, the resulting guidance algorithm was only appropriate for maneuvering reentry vehicle guidance. In the context of singular perturbation theory, a constraint on final altitude gives rise to a difficult terminal boundary layer problem, which cannot be solved in closed form. This paper will demonstrate the near optimality of a predictive/corrective guidance algorithm for the terminal maneuver. Comparisons are made to numerically optimized trajectories for a range or orbit plane angles.

Calise, Anthony J.; Bae, Gyoung H.

1988-01-01

412

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

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

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

2013-01-01

413

The motivation for this work has been the use of tools, such as genetic algorithms and fuzzy sets, to address the various issues that are involved in an engineering design optimization problem. In order to address the variety, generality...

Vijayakumar, Bhuvaneshwaran

2001-01-01

414

This paper describes an intuitive way of defining geometry design variables for solving structural topology optimization problems using a genetic algorithm (GA). The geometry representation scheme works by defining a ...

Tai, Kang

415

Serial and Parallel Genetic Algorithms as Function Optimizers V. Scott Gordon

Serial and Parallel Genetic Algorithms as Function Optimizers V. Scott Gordon Department of Computer Science Colorado State University Fort Collins, Colorado 80523 Darrell Whitley Department of Computer Science Colorado State University Fort Collins, Colorado 80523 Abstract Parallel genetic

Whitley, Darrell

416

Many articles in “in silico” drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational\\u000a search, or docking studies. Some of these articles described GA applications to quantitative structure–activity relationships\\u000a (QSAR) modeling in combination with regression and\\/or classification techniques. We reviewed the implementation of GA in drug\\u000a design QSAR and specifically its performance in the optimization of robust

Michael Fernandez; Julio Caballero; Leyden Fernandez; Akinori Sarai

2011-01-01

417

FPGA Implementation of Optimal Filtering Algorithm for TileCal ROD System

Traditionally, Optimal Filtering Algorithm has been implemented using general purpose programmable DSP chips. Alternatively, new FPGAs provide a highly adaptable and flexible system to develop this algorithm. TileCal ROD is a multi-channel system, where similar data arrives at very high sampling rates and is subject to simultaneous tasks. It include different FPGAs with high I/O and with parallel structures that provide a benefit at a data analysis. The Optical Multiplexer Board is one of the elements presents in TileCal ROD System. It has FPGAs devices that present an ideal platform for implementing Optimal Filtering Algorithm. Actually this algorithm is performing in the DSPs included at ROD Motherboard. This work presents an alternative to implement Optimal Filtering Algorithm.

Torres, J; Castillo, V; Cuenca, C; Ferrer, A; Fullana, E; González, V; Higón, E; Poveda, J; Ruiz-Martinez, A; Salvachúa, B; Sanchis, E; Solans, C; Valero, A; Valls, J A

2008-01-01

418

Gear pair design optimization by Genetic Algorithm and FEA

Multiple, often conflicting objectives arise naturally in most real-world optimization. Gear is a mechanical device that transfers the rotating motion and power from one part of a machine to another. Searching for best gear is a very hard problem. Gear optimization can be divided into two categories, namely, single gear pair or Gear train optimization. The problem of gear pairs

S. Padmanabhan; S. Ganesan; M. Chandrasekaran; V. Srinivasa Raman

2010-01-01

419

The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization

We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which\\u000a selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA’s selection method is relatively\\u000a unusual in comparison with current well known multiobjective evolutionary algorithms, which tend to use counts based on the\\u000a degree to which solutions dominate others in the population.

David W. Corne; Joshua D. Knowles; Martin J. Oates

420

Data mining with an ant colony optimization algorithm

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

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

2002-01-01

421

Lower bound and an optimal algorithm for leader election in faulty asynchronous general networks

& nt, s for the degree of MASTER OF SCIENCE August, 1993 Major Subject: Electrical Engineering LOWER HOUND AND AN OPTIMAL ALGORITHM FOR LEADER ELECTION IN FAULTY ASYNCHRONOUS GL'NL'RAL NETWORKS A Thesis by MOHAMMED AFROZ LATEEF Approved... as to style and content by: Ilosame Abu-Amara (Chair of Committee) Pierce I'. Cantrell (Mensber) Donald K. Friesen (Member) Ali Abur (Member) Alton . Patton (Head of epartn&ent, ) August 1993 111 ABSTRACT Lower Bound and an Optimal Algorithm...

Lateef, Mohammed Afroz

1993-01-01

422

Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms

NASA Technical Reports Server (NTRS)

The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

Benford, Andrew; Tinker, Michael L.

2004-01-01

423

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

INVESTIGATION OF GENETIC ALGORITHM DESIGN REPRESENTATION FOR MULTI-OBJECTIVE TRUSS OPTIMIZATION A Thesis by SOUMYA SUNDAR PATHI 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 August 2006 Major Subject: Civil Engineering INVESTIGATION OF GENETIC ALGORITHM DESIGN REPRESENTATION FOR MULTI-OBJECTIVE TRUSS OPTIMIZATION A Thesis by SOUMYA SUNDAR PATHI...

Pathi, Soumya Sundar

2006-10-30

424

ERIC Educational Resources Information Center

This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and…

Leite, Walter L.; Huang, I-Chan; Marcoulides, George A.

2008-01-01

425

An optimal multisensor source location algorithm for passive sonar with moving source or sensors

An underwater acoustic source location algorithm, which is a globally optimal joint estimator of source position, course, and speed, in a maximum a posteriori probability (MAP) sense, is described. The algorithm accommodates multiple sensor arrays which need not be planar and which may themselves have motion. System motion is accommodated and restrictions on integration times are for practical purposes removed.

J. Grindon

1981-01-01

426

Enhanced stochastic optimization algorithm for finding ef-fective multi-target therapeutics

the performance of the proposed algorithm based on various drug response functions, and compared it with the GurEnhanced stochastic optimization algorithm for finding ef- fective multi-target therapeutics Byung-target therapeutics is often more effective compared to monotherapies, and combinatory drugs are commonly used

Yoon, Byung-Jun

427

Near-Optimal Embedding by Genetic Algorithms for Time Series Prediction

NASA Astrophysics Data System (ADS)

A new method for time series forecasting by fuzzy inference systems based on non-uniform attractor embedding and genetic algorithms is proposed in this paper. A near optimal set of time lags is identified by genetic algorithms. The parameter characterizing the spreading of the embedded attractor in the delay coordinate space is used as a fitness function for every set of time lags.

Ragulskiene, Jurate; Lukoseviciute, Kristina; Ragulskis, Minvydas

2009-09-01

428

A niche hybrid genetic algorithm for global optimization of continuous multimodal functions

A niche hybrid genetic algorithm (NHGA) is proposed in this paper to solve continuous multimodal optimization problems more efficiently, accurately and reliably. It provides a new architecture of hybrid algorithms, which organically merges the niche techniques and Nelder–Mead's simplex method into GAs. In the new architecture, the simplex search is first performed in the potential niches, which likely contain a

Lingyun Wei; Mei Zhao

2005-01-01

429

Automatic system and energetic efficiency optimization algorithm for solar panels on mobile systems

The system described in this paper is aiming to solve the non-optimal energy conversion in solar panels due to the angle bias of the panel surface orientation related with radiation source. Because the device is intended to function on a mobile system with unpredictable moves, the orientation algorithm works in an adaptive manner. The mathematical background of the algorithm use

A. Vasile; A. Drumea; C. Neacsu; M. Angel; D. A. Stoichescu

2009-01-01

430

An Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem

An Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem Alena Shmygelska, Rosal, the two dimensional hydrophobic-polar (2D HP) protein folding problem. We introduce an ant colony algorithm closely approaches that of specialised, state-of-the methods for 2D HP protein folding. 1

Hoos, Holger H.

431

Fault diagnosis analysis with support vector regression and particle swarm optimization algorithm

The fault diagnosis model with support vector regression (SVR) and particle swarm optimization algorithm (POSA) for is proposed. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The impact factor of fault behaviors is discussed. With the ability of strong self-learning and faster convergence, this fault detection method

WenJie Tian; JiCheng Liu

2010-01-01

432

Optimization Design for PID Parameter of Mobile Robot Based on Genetic Algorithm

Focusing on the control requirements for position and speed of mobile robot, PID controller parameters of mobile robot based on genetic algorithm are designed and optimized. In this paper, the performance index for the time integral of absolute error value is adopted as the minimum object function to choose parameters, and the global searching capability of genetic algorithm is used

Mingyou Bai; Haiying Wang; Rui Xu; Jinlin Jia

2009-01-01

433

Quasi-Optimal Algorithms for the Control Loops of the Fermilab Energy Saver Satellite Refrigerator

The Cryogenic System of the Satellite Refrigerator for the Energy Saver Accelerator Ring comprises 12 interrelated closed loops and several open loops. A quasi-optimal algorithm to control the Cryogenic System, under different modes operation, is described. The constraints imposed to define these algorithms and the process followed to characterize the functional parameters are described. A report on the results obtained

M. Martin; J. Gannon; J. McCarthy; C. Rode

1981-01-01

434

An efficient algorithm for optimal reservoir utilization in probabilistic production costing

An efficient and accurate algorithm is presented which determines optimal storage reservoir utilization (pumped hydro) in probabilistic production cost models with multiple storage, thermal, and limited-energy units. The algorithm exploits the special nature of the production cost function, which is piecewise linear with respect to the reservoir utilization levels. It achieves the same accuracy as previously developed, less efficient, approaches

A. J. Conejo; M. C. Caramanis; J. A. Bloom

1990-01-01

435

Optimization of a Small Passive Wind Turbine Generator with Multiobjective Genetic Algorithms

Algorithms (MOGAs) are used for the design of a small wind turbine generator (WTG) coupled to a DC bus choice of the system design variables associated with the wind turbine generator sizing (especiallyOptimization of a Small Passive Wind Turbine Generator with Multiobjective Genetic Algorithms A

Paris-Sud XI, UniversitÃ© de

436

Efficient approximation and optimization algorithms for computational metrology

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

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

1997-01-01

437

SOS! An algorithm and software for the stochastic optimization of stimuli.

The characteristics of the stimuli used in an experiment critically determine the theoretical questions the experiment can address. Yet there is relatively little methodological support for selecting optimal sets of items, and most researchers still carry out this process by hand. In this research, we present SOS, an algorithm and software package for the stochastic optimization of stimuli. SOS takes its inspiration from a simple manual stimulus selection heuristic that has been formalized and refined as a stochastic relaxation search. The algorithm rapidly and reliably selects a subset of possible stimuli that optimally satisfy the constraints imposed by an experimenter. This allows the experimenter to focus on selecting an optimization problem that suits his or her theoretical question and to avoid the tedious task of manually selecting stimuli. We detail how this optimization algorithm, combined with a vocabulary of constraints that define optimal sets, allows for the quick and rigorous assessment and maximization of the internal and external validity of experimental items. In doing so, the algorithm facilitates research using factorial, multiple/mixed-effects regression, and other experimental designs. We demonstrate the use of SOS with a case study and discuss other research situations that could benefit from this tool. Support for the generality of the algorithm is demonstrated through Monte Carlo simulations on a range of optimization problems faced by psychologists. The software implementation of SOS and a user manual are provided free of charge for academic purposes as precompiled binaries and MATLAB source files at http://sos.cnbc.cmu.edu. PMID:22351612

Armstrong, Blair C; Watson, Christine E; Plaut, David C

2012-09-01

438

Service oriented modeling and simulation are hot issues in the field of modeling and simulation, and there is need to call service resources when simulation task workflow is running. How to optimize the service resource allocation to ensure that the task is complete effectively is an important issue in this area. In military modeling and simulation field, it is important to improve the probability of success and timeliness in simulation task workflow. Therefore, this paper proposes an optimization algorithm for multipath service resource parallel allocation, in which multipath service resource parallel allocation model is built and multiple chains coding scheme quantum optimization algorithm is used for optimization and solution. The multiple chains coding scheme quantum optimization algorithm is to extend parallel search space to improve search efficiency. Through the simulation experiment, this paper investigates the effect for the probability of success in simulation task workflow from different optimization algorithm, service allocation strategy, and path number, and the simulation result shows that the optimization algorithm for multipath service resource parallel allocation is an effective method to improve the probability of success and timeliness in simulation task workflow. PMID:24963506

Zhang, Hongjun; Zhang, Rui; Li, Yong; Zhang, Xuliang

2014-01-01

439

A P2P Network Topology Optimized Algorithm Based on Minimum Maximum K-Means Principle

The most popular routing algorithm in p2p network is that every node keeps a route table which records a certain number of other nodes. Such algorithm selects neighbor nodes at random, and this reduces routing efficiency. In this paper a new algorithm of optimizing p2p overlay network topology is proposed, which is based on minimum maximum k-means principle. According to

Yi Ma; Zhenhua Tan; Guiran Chang; Xiaoxing Gao

2009-01-01

440

\\u000a In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the permutation flowshop sequencing problem with the makespan criterion. A new crossover\\u000a operator, here we call it the PTL crossover operator, is presented. In addition, the DPSO algorithm is hybridized with a simple local search algorithm based on an insert neighborhood to further improve the solution

Quan-Ke Pan; M. Fatih Tasgetiren; Yun-Chia Liang

2007-01-01

441

Quantum computing is applied to genetic algorithm (GA) to develop a class of quantum-inspired genetic algorithm (QGA) characterized by certain principles of quantum mechanisms for numerical optimization. Furthermore, a framework of hybrid QGA, named RQGA, is proposed by reasonably combining the Q-bit search of quantum algorithm in micro-space and classic genetic search of real-coded GA (RGA) in macro-space to achieve

Ling Wang; Fang Tang; Hao Wu

2005-01-01

442

A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization

This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749–768; T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, Multiobjective optimization of radio-to-fiber repeater

Kazi Shah Nawaz Ripon; Sam Kwong; K. F. Man

2007-01-01

443

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

W. C. Stirling; D. R. Morrell

1991-01-01

444

Generalized derivatives and generalized convexities

We give a survey of the contributions of the speaker and of his co-authors in the use of nonsmooth analysis for the study of generalized convexities such as quasiconvexity, pseudoconvexity, invexity. One line of though corresponds to the use of generalized directional derivatives, as in S. Komlosi. Another track consists in using a subdifferential. This could be done in an axiomatic way, but we use essentially three classical instances; the Clarke subdifferential, the contingent subdifferential and the Frechet subdifferential. For algorithmic purposes, variants of Plastra`s subdifferential can be use.

Penot, J.P.; Quang, P.H.; Sach, P.H.

1994-12-31

445

DC optimization approach to robust controls: the optimal scaling value problem

The optimal scaling problem (OSP) for constant scaling in output feedback control is an inherently difficult nonconvex problem for which in general existing local search algorithms can at best locate a local solution. However, it can be restated as a problem of globally minimizing a convex function under DC constraints, i.e., constraints that can be expressed in terms of differences

H. D. Tuan; S. Hosoe; H. Tuy

2000-01-01

446

A HYBRID GENETIC ALGORITHM APPROACH FOR OSPF WEIGHT SETTING PROBLEM

In this paper, a method based on hybrid genetic algorithm (GA), is presented to optimize administrative weights for OSPF routing. This method can be seen as an alternative to the local- search method in (1) or another GA-based method in (8,10). However, the GA as well as the objective function we use are different. Instead of minimizing a convex cost

Eueung Mulyana; Ulrich Killat

2002-01-01

447

A DECOMPOSITION ALGORITHM FOR NESTED RESOURCE ALLOCATION PROBLEMS

, portfolio selection, energy optimization, sample allocation in stratified sampling, capital budgeting, mass continuous or integer variables. No assumption of strict convexity or differentiability is needed. The method method achieves a higher performance than previous algorithms, addressing all problems with up to one

Jaillet, Patrick

448

Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment planning.

Zarepisheh, Masoud; Li, Nan [Department of Radiation Medicine and Applied Sciences and Center for Advanced Radiotherapy Technologies, University of California San Diego, La Jolla, California 92037-0843 (United States)] [Department of Radiation Medicine and Applied Sciences and Center for Advanced Radiotherapy Technologies, University of California San Diego, La Jolla, California 92037-0843 (United States); Long, Troy; Romeijn, H. Edwin [Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109-2117 (United States)] [Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109-2117 (United States); Tian, Zhen; Jia, Xun; Jiang, Steve B., E-mail: Steve.Jiang@UTSouthwestern.edu [Department of Radiation Medicine and Applied Sciences and Center for Advanced Radiotherapy Technologies, University of California San Diego, La Jolla, California 92037-0843 and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390-8542 (United States)

2014-06-15

449

Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines

This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization , or SMO . Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a

John C. Platt

1998-01-01

450

Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization

J. Vesterstrom; R. Thomsen

2004-01-01

451

Time-Jerk Synthetic Optimal Trajectory Planning of Robot Based on Fuzzy Genetic Algorithm

A new approach based on fuzzy genetic algorithm is developed to find the time-jerk synthetic optimal trajectory of robot with a joint space scheme using cubic splines. In order to get the optimal trajectory, cubic splines are employed and derived under the constraint condition of velocity and acceleration of the first and last point, which assure overall continuity of velocity

Ming Cong; Xiaofei Xu; P. Xu

2008-01-01

452

A homotopy algorithm for digital optimal projection control GASD-HADOC

NASA Technical Reports Server (NTRS)

The linear-quadratic-gaussian (LQG) compensator was developed to facilitate the design of control laws for multi-input, multi-output (MIMO) systems. The compensator is computed by solving two algebraic equations for which standard closed-loop solutions exist. Unfortunately, the minimal dimension of an LQG compensator is almost always equal to the dimension of the plant and can thus often violate practical implementation constraints on controller order. This deficiency is especially highlighted when considering control-design for high-order systems such as flexible space structures. This deficiency motivated the development of techniques that enable the design of optimal controllers whose dimension is less than that of the design plant. A homotopy approach based on the optimal projection equations that characterize the necessary conditions for optimal reduced-order control. Homotopy algorithms have global convergence properties and hence do not require that the initializing reduced-order controller be close to the optimal reduced-order controller to guarantee convergence. However, the homotopy algorithm previously developed for solving the optimal projection equations has sublinear convergence properties and the convergence slows at higher authority levels and may fail. A new homotopy algorithm for synthesizing optimal reduced-order controllers for discrete-time systems is described. Unlike the previous homotopy approach, the new algorithm is a gradient-based, parameter optimization formulation and was implemented in MATLAB. The results reported may offer the foundation for a reliable approach to optimal, reduced-order controller design.

Collins, Emmanuel G., Jr.; Richter, Stephen; Davis, Lawrence D.

1993-01-01

453

On layout optimization of the microwave diplexor filter using genetic algorithms

An original application of genetics algorithms in the on layout optimization of the microwave filters is presented. Based on a resonant coupling irises topology, a Ka-band diplexor filter on silicon membrane substrate is tuned in order to improve its performances. The optimization process uses the numerical results given by Sonnet software and the overall process is piloted by a genetic

A. Takacs; A. Serbanescu; G. Leu; H. Aubert; P. Pons; T. Parra; R. Plana

2004-01-01

454

Optimization of wind turbine energy and power factor with an evolutionary computation algorithm

Optimization of wind turbine energy and power factor with an evolutionary computation algorithm 2009 Keywords: Wind turbine Power factor Power output Power quality Data mining Neural network Dynamic computation approach for optimization of power factor and power output of wind turbines is discussed. Data

Kusiak, Andrew

455

HYBRIDISATION OF NEURAL NETWORKS AND GENETIC ALGORITHMS FOR TIME-OPTIMAL CONTROL

presents the use of neural networks and genetic algorithms in time-optimal control of a closed-loop robotic system. Radial-basis function networks are used in conjunction with PID controllers in an independent interests in robotics during the past decade. Time-optimality can lead to an overall improvement

Coello, Carlos A. Coello

456

An Optimal Fuzzy Self-Tuning PID Controller for Robot Manipulators via Genetic Algorithm

This paper deals with the problem of optimizing a fuzzy self-tuning PID controller for robot manipulators. Fuzzy PID controllers have been developed and applied in many fields in the last fifteen years. However, there is no systematic method to design Membership Functions (MFs) for these controllers. We propose a simple method based on Genetic Algorithms (GA) to find optimal input

J. L. Meza; R. Soto; J. Arriaga

2009-01-01

457

Genetic Algorithm and Simulated Annealing for Optimal Robot Arm PID Control

This paper describes the use of genetic algorithm (GA) and simulated annealing (SA) for optimizing the parameters of PID controllers for a 6-DOF robot arm. A GA and a SA are designed to optimal-tune the parameters of the PID controller of each joint for a single step response and for the tracking of other specified trajectories. The GA and the

D. P. Kwok; Fang Sheng

1994-01-01

458

NLPLSX: A Fortran Implementation of an SQP Algorithm for Least-Squares Optimization

NLPLSX: A Fortran Implementation of an SQP Algorithm for Least-Squares Optimization with Very Many://www.klaus-schittkowski.de Date: December, 2009 Abstract The Fortran subroutine NLPLSX solves constrained least squares prob- lems, Fortran codes 1 #12;1 Introduction Nonlinear least squares optimization is extremely important in many

Schittkowski, Klaus

459

In this survey we discuss different state-of-the-art approaches of combining exact algorithms and metaheuristics to solve combinatorial optimization problems. Some of these hybrids mainly aim at providing optimal solutions in shorter time, while others primarily focus on getting better heuristic solutions. The two main categories in which we divide the approaches are collaborative versus integrative combinations. We further classify the

Jakob Puchinger; Günther R. Raidl

2005-01-01

460

The learning curve of Bayes optimal classificationalgorithm when learning a perceptronfrom noisy random training examples is calculatedexactly in the limit of large trainingsample size and large instance space dimensionusing methods of statistical mechanics.It is shown that under certain assumptions,in this "thermodynamic" limit, the probabilityof misclassification of Bayes optimal algorithmis less than that of a canonical stochasticlearning algorithm, by a factor

Manfred Opper; David Haussler

1991-01-01

461

Comment on: An Accelerated Learning Algorithm for Multilayer Perceptrons: Optimization Layer by

1 Comment on: An Accelerated Learning Algorithm for Multilayer Perceptrons: Optimization Layer by Layer B.Ph. van Milligen, V. Tribaldos, J.A. Jim enez and C. Santa Cruz Abstract| The present letter analyzes the performance of the neural network training method known as Optimization Layer by Layer 1 . We

van Milligen, Boudewijn

462

Computational and statistical tradeoffs via convex relaxation

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

Chandrasekaran, Venkat; Jordan, Michael I.

2013-01-01

463

Computational and statistical tradeoffs via convex relaxation.

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

Chandrasekaran, Venkat; Jordan, Michael I

2013-03-26

464

A sparse superlinearly convergent SQP with applications to two-dimensional shape optimization.

Discretization of optimal shape design problems leads to very large nonlinear optimization problems. For attaining maximum computational efficiency, a sequential quadratic programming (SQP) algorithm should achieve superlinear convergence while preserving sparsity and convexity of the resulting quadratic programs. Most classical SQP approaches violate at least one of the requirements. We show that, for a very large class of optimization problems, one can design SQP algorithms that satisfy all these three requirements. The improvements in computational efficiency are demonstrated for a cam design problem.

Anitescu, M.

1998-04-15

465

Hybridizing Genetic Algorithms with Hill-Climbing Methods for Global Optimization: Two Possible Ways

Two methods of hybridizing genetic algorithms (GA) with hill-climbing for global optimization are investigated. The first one involves two interwoven levels of optimization-evolution (GA) and individual learning (hill-climbing)-which cooperate in the global optimization process. The second one consists of modifying a GA by the introduction of new genetic operators or by the alteration of traditional ones in such a way

Hugues Bersini; Jean-michel Renders

1994-01-01

466

Electromagnetic simulation software (EMSS) has many advantages such as friendly interface, modeling convenience and reliable simulation results. However, they are inadequate in optimization design. Genetic algorithm (GA) optimizers are robust, stochastic search methods. They are particularly effective to find an approximate global maximum\\/minimum in a high-dimension, multimodal function domain in a near-optimal manner. Combining EMSS and GA is expected to

L. Z. You; W. B. Dou

2012-01-01

467

Improved Quantum Genetic Algorithm and Its Application in Nutritional Diet Optimization

An improved quantum genetic algorithm (IQGA) is proposed to avoid declining of the searching ability for multi-peak function optimization and multi-genes chromosome encoding problem. Improvements include adjusting initialization way of chromosome's genes, changing elitist strategy and introducing partial population disaster strategy. Experimental results on continuous multi-peak function optimization and actual nutritional diet optimization show that IQGA is superior to traditional

Youbo Lv; Dong Li

2008-01-01

468

Use of Algorithm of Changes for Optimal Design of Heat Exchanger

NASA Astrophysics Data System (ADS)

For economic reasons, the optimal design of heat exchanger is required. Design of heat exchanger is usually based on the iterative process. The design conditions, equipment geometries, the heat transfer and friction factor correlations are totally involved in the process. Using the traditional iterative method, many trials are needed for satisfying the compromise between the heat exchange performance and the cost consideration. The process is cumbersome and the optimal design is often depending on the design engineer's experience. Therefore, in the recent studies, many researchers, reviewed in [1], applied the genetic algorithm (GA) [2] for designing the heat exchanger. The results outperformed the traditional method. In this study, the alternative approach, algorithm of changes, is proposed for optimal design of shell-tube heat exchanger [3]. This new method, algorithm of changes based on I Ching (???), is developed originality by the author. In the algorithms, the hexagram operations in I Ching has been generalized to binary string case and the iterative procedure which imitates the I Ching inference is also defined. On the basis of [3], the shell inside diameter, tube outside diameter, and baffles spacing were treated as the design (or optimized) variables. The cost of the heat exchanger was arranged as the objective function. Through the case study, the results show that the algorithm of changes is comparable to the GA method. Both of method can find the optimal solution in a short time. However, without interchanging information between binary strings, the algorithm of changes has advantage on parallel computation over GA.

Tam, S. C.; Tam, H. K.; Chio, C. H.; Tam, L. M.

2010-05-01

469

GENETIC ALGORITHMS APPLIED TO REAL TIME MULTIOBJECTIVE OPTIMIZATION PROBLEMS

-to-air missions, air defense suppression missions, reconnaissance, antiÂtactical ballistic missile, and air refueling. This software automatically plans military moves and actions in a rule-based manner. It also optimization method with some additional rules. To find solutions to this kind of optimization problem, one

Coello, Carlos A. Coello

470

Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

In trying to solve multiobjective optimization problems, many traditional methods scalarizethe objective vector into a single objective. In those cases, the obtained solution is highlysensitive to the weight vector used in the scalarization process and demands the user to haveknowledge about the underlying problem. Moreover, in solving multiobjective problems, designersmay be interested in a set of Pareto-optimal points, instead of

N. Srinivas; Kalyanmoy Deb

1994-01-01

471

Optimizations for sampling-based motion planning algorithms

Sampling-basedalgorithms solve the motion planning problem by successively solving several separate suproblems of reduced complexity. As a result, the efficiency of the sampling-based algorithm depends on the complexity ...

Bialkowski, Joshua John

2014-01-01

472

The Use of Genetic Algorithms in Multilayer Mirror Optimization

Department of Physics and Astronomy Brigham Young University, Provo, UT 84602 #12;June 10, 1999, GA.doc, page 2 Abstract We have applied the genetic algorithm to extreme ultraviolet (XUV) multilayer mirror

Hart, Gus

473

Performance of Digital Subscriber Line Spectrum Optimization Algorithms

--Dynamic spectrum management FEXT--Far-end crosstalk FTP--File Transfer Protocol GUI--Graphical user interface IEEE balancing algorithm PC--Personal computer PSD--Power spectrum density RFI--Radio frequency interference RT--R

Kramer, Gerhard

474

Implementation and comparison of PSO-based algorithms for multi-modal optimization problems

NASA Astrophysics Data System (ADS)

This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybrid algorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration.

Sriyanyong, Pichet; Lu, Haiyan

2013-10-01

475

Using genetic algorithms to search for an optimal portfolio strategy and test market efficiency

NASA Astrophysics Data System (ADS)

In this numerical experiment we used genetic algorithms to search for an optimal portfolio investment strategy. The algorithm involves having a ``manager'' who divides his capital among various ``experts'' each of whom has a simple fixed investment strategy. The expert strategies act like population of genes which experiencing selection, mutation and crossover during evolution process. The genetic algorithm was run on actual portfolio with stock data (DowJones 30 stocks). We found that the genetic algorithm overwhelmingly selected optimal strategy that closely resembles a simple buy and hold portfolio, that is, evenly distribute the capital among all stocks. This study shows that market is very efficient, and one possible practical way to gauge market efficiency is to measure the difference between an optimal portfolio return and a simple buy and hold portfolio return.

Xi, Haowen; Mandere, Edward

2008-03-01

476

Dynamic programming algorithm optimization for spoken word recognition

This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm

HIROAKI SAKOE; SEIBI CHIBA

1978-01-01

477

Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

NASA Astrophysics Data System (ADS)

Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

Hentschel, Alexander; Sanders, Barry C.

2011-12-01

478

Efficient algorithm for optimizing adaptive quantum metrology processes.

Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence. PMID:22182087

Hentschel, Alexander; Sanders, Barry C

2011-12-01

479

An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

Alexander Hentschel; Barry C. Sanders

2011-04-19

480

Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue. PMID:25145955

Chang, Joshua; Paydarfar, David

2014-12-01

481

In this research, an approach in combination with support vector regression (SVR), genetic algorithm (GA), and optimal technical analysis is proposed to explore stock dynamism of multi-nations under different economical environments. First, we apply full search algorithm to select the optimal number of trading days used to calculate the technical indicator values. Genetic algorithm is then used to search the

Deng-Yiv Chiu; Shin-Yi Chian

2010-01-01

482

Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. PMID:25603158

Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng

2015-01-01

483

Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. PMID:25603158

Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng

2015-01-01

484

NASA Astrophysics Data System (ADS)

We have applied genetic algorithm optimization for the design of laser pulses to control dissociation process in the ground electronic state of HF molecule, within the mathematical framework of optimal control theory. In order to design the experimentally feasible laser fields, we coded the small set of selected field parameters in the GA parameter space. Two types of pulses, one with fixed frequency components and the other having non-deterministic components have been designed. Optimized laser field obtained using this approach, possesses simple time and frequency structures. We show that the fields having non-deterministic frequency components lead to greater dissociation probability compared to the ones having deterministic frequency components.

Bondarchuk, Sergey V.; Minaev, Boris F.

2011-11-01

485

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

486

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

2014-01-01

487

Optimization of a cell counting algorithm for mobile point-of-care testing platforms.

In a point-of-care (POC) setting, it is critically important to reliably count the number of specific cells in a blood sample. Software-based cell counting, which is far faster than manual counting, while much cheaper than hardware-based counting, has emerged as an attractive solution potentially applicable to mobile POC testing. However, the existing software-based algorithm based on the normalized cross-correlation (NCC) method is too time- and, thus, energy-consuming to be deployed for battery-powered mobile POC testing platforms. In this paper, we identify inefficiencies in the NCC-based algorithm and propose two synergistic optimization techniques that can considerably reduce the runtime and, thus, energy consumption of the original algorithm with negligible impact on counting accuracy. We demonstrate that an AndroidTM smart phone running the optimized algorithm consumes 11.5× less runtime than the original algorithm. PMID:25195851

Ahn, DaeHan; Kim, Nam Sung; Moon, SangJun; Park, Taejoon; Son, Sang Hyuk

2014-01-01

488

NASA Astrophysics Data System (ADS)

Laser machining is a promising non-contact process for effective machining of difficult-to-process advanced engineering materials. Increasing interest in the use of lasers for various machining operations can be attributed to its several unique advantages, like high productivity, non-contact processing, elimination of finishing operations, adaptability to automation, reduced processing cost, improved product quality, greater material utilization, minimum heat-affected zone and green manufacturing. To achieve the best desired machining performance and high quality characteristics of the machined components, it is extremely important to determine the optimal values of the laser machining process parameters. In this paper, fireworks algorithm and cuckoo search (CS) algorithm are applied for single as well as multi-response optimization of two laser machining processes. It is observed that although almost similar solutions are obtained for both these algorithms, CS algorithm outperforms fireworks algorithm with respect to average computation time, convergence rate and performance consistency.

Goswami, D.; Chakraborty, S.

2014-11-01

489

Evolutionary algorithms are parallel computing algorithms and simulated annealing algorithm is a sequential computing algorithm. This paper inserts simulated annealing into evolutionary computations and successful developed a hybrid Self-Adaptive Evolutionary Strategy $\\mu+\\lambda$ method and a hybrid Self-Adaptive Classical Evolutionary Programming method. Numerical results on more than 40 benchmark test problems of global optimization show that the hybrid methods presented in this paper are very effective. Lennard-Jones potential energy minimization is another benchmark for testing new global optimization algorithms. It is studied through the amyloid fibril constructions by this paper. To date, there is little molecular structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins.This region belongs to the N-terminal unstructured region (1-123) of prion proteins, the structure of which has proved hard to determine using NMR spectroscopy or X-ray crystallography ...

Zhang, Jiapu

2010-01-01

490

CONTINUOUS CONVEX SETS AND ZERO DUALITY GAP FOR ...

euclidean spaces, like Gale & Klee's boundary rays and asymptotes of ... gardless of the value of the constraints and respectively of the objective ... Key words and phrases. constrained optimization, recession analysis, convex programs,.

2011-11-12

491

Geodesic Convexity and Chordal Graphs

A convexity on a flnite set X is a family C of subsets of X (each such set called a convex set), which is closed under intersection and which contains both X and the empty set. The pair (X;C) is called a convexity space. A (flnite) graph convexity space is a pair (G;C), formed by a flnite connected graph G

Ignacio M. Pelayo

492

NASA Astrophysics Data System (ADS)

Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.

Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei

2014-04-01

493

Optimization algorithms in boiling water reactor lattice design

Given the highly complex nature of neutronics and reactor physics, efficient methods of optimizing are necessary to effectively design the core reloading pattern and operate a nuclear reactor. The current popular methods ...

Burns, Chad (Chad D.), III

2013-01-01

494

NASA Astrophysics Data System (ADS)

The personalized urban multi-criteria quasi-optimum path problem (PUMQPP) is a branch of multi-criteria shortest path problems (MSPPs) and it is classified as a NP-hard problem. To solve the PUMQPP, by considering dependent criteria in route selection, there is a need for approaches that achieve the best compromise of possible solutions/routes. Recently, invasive weed optimization (IWO) algorithm is introduced and used as a novel algorit