Improved simple optimization (SOPT algorithm for unconstrained non-linear optimization problems
Directory of Open Access Journals (Sweden)
J. Thomas
2016-09-01
Full Text Available In the recent years, population based meta-heuristic are developed to solve non-linear optimization problems. These problems are difficult to solve using traditional methods. Simple optimization (SOPT algorithm is one of the simple and efficient meta-heuristic techniques to solve the non-linear optimization problems. In this paper, SOPT is compared with some of the well-known meta-heuristic techniques viz. Artificial Bee Colony algorithm (ABC, Particle Swarm Optimization (PSO, Genetic Algorithm (GA and Differential Evolutions (DE. For comparison, SOPT algorithm is coded in MATLAB and 25 standard test functions for unconstrained optimization having different characteristics are run for 30 times each. The results of experiments are compared with previously reported results of other algorithms. Promising and comparable results are obtained for most of the test problems. To improve the performance of SOPT, an improvement in the algorithm is proposed which helps it to come out of local optima when algorithm gets trapped in it. In almost all the test problems, improved SOPT is able to get the actual solution at least once in 30 runs.
Directory of Open Access Journals (Sweden)
M. Kandasamy
2014-12-01
Full Text Available A comparison study of Firefly Algorithm (FA and Bacterial Foraging Algorithm (BFO optimization is carried out by applying them to a Non Linear pH neutralization process. In process control engineering, the Proportional, Derivative, Integral controller tuning parameters are deciding the performance of the controller to ensure the good performance of the plant. The FA and BFO algorithms are applied to obtain the optimum values of controller parameters. The performance indicators such as servo response and regulatory response tests are carried out to evaluate the efficiency of the heuristic algorithm based controllers. The error minimization criterion such as Integral Absolute Error (IAE, Integral Square Error (ISE, Integral Time Square Error (ITSE, Integral Time Absolute Error (ITAE and Time domain specifications – rise time, Peak Overshoot and settling time are considered for the study of the performance of the controllers. The study indicates that, FA tuned PID controller provides marginally better set point tracking, load disturbance rejection, time domain specifications and error minimization for the Non Linear pH neutralization process compared to BFO tuned PID controller.
A general non-linear optimization algorithm for lower bound limit analysis
DEFF Research Database (Denmark)
Krabbenhøft, Kristian; Damkilde, Lars
2003-01-01
The non-linear programming problem associated with the discrete lower bound limit analysis problem is treated by means of an algorithm where the need to linearize the yield criteria is avoided. The algorithm is an interior point method and is completely general in the sense that no particular...... finite element discretization or yield criterion is required. As with interior point methods for linear programming the number of iterations is affected only little by the problem size. Some practical implementation issues are discussed with reference to the special structure of the common lower bound...... load optimization problem. and finally the efficiency and accuracy of the method is demonstrated by means of examples of plate and slab structures obeying different non-linear yield criteria. Copyright (C) 2002 John Wiley Sons. Ltd....
An integer optimization algorithm for robust identification of non-linear gene regulatory networks
Directory of Open Access Journals (Sweden)
Chemmangattuvalappil Nishanth
2012-09-01
Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters
DEFF Research Database (Denmark)
Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard
2015-01-01
compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...
Yang, Qin; Zou, Hong-Yan; Zhang, Yan; Tang, Li-Juan; Shen, Guo-Li; Jiang, Jian-Hui; Yu, Ru-Qin
2016-01-15
Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods.
An algorithm for earthwork allocation considering non-linear factors
Institute of Scientific and Technical Information of China (English)
WANG Ren-chao; LIU Jin-fei
2008-01-01
For solving the optimization model of earthwork allocation considering non-linear factors, a hybrid al-gorithm combined with the ant algorithm (AA) and particle swarm optimization (PSO) is proposed in this pa-per. Then the proposed method and the LP method are used respectively in solving a linear allocation model of a high rockfill dam project. Results obtained by these two methods are compared each other. It can be conclu-ded that the solution got by the proposed method is extremely approximate to the analytic solution of LP method. The superiority of the proposed method over the LP method in solving a non-linear allocation model is illustrated by a non-linear case. Moreover, further researches on improvement of the algorithm and the allocation model are addressed.
Optimal non-linear health insurance.
Blomqvist, A
1997-06-01
Most theoretical and empirical work on efficient health insurance has been based on models with linear insurance schedules (a constant co-insurance parameter). In this paper, dynamic optimization techniques are used to analyse the properties of optimal non-linear insurance schedules in a model similar to one originally considered by Spence and Zeckhauser (American Economic Review, 1971, 61, 380-387) and reminiscent of those that have been used in the literature on optimal income taxation. The results of a preliminary numerical example suggest that the welfare losses from the implicit subsidy to employer-financed health insurance under US tax law may be a good deal smaller than previously estimated using linear models.
Non-linear Frequency Scaling Algorithm for FMCW SAR Data
Meta, A.; Hoogeboom, P.; Ligthart, L.P.
2006-01-01
This paper presents a novel approach for processing data acquired with Frequency Modulated Continuous Wave (FMCW) dechirp-on-receive systems by using a non-linear frequency scaling algorithm. The range frequency non-linearity correction, the Doppler shift induced by the continuous motion and the ran
Non-linear scalable TFETI domain decomposition based contact algorithm
Dobiáš, J.; Pták, S.; Dostál, Z.; Vondrák, V.; Kozubek, T.
2010-06-01
The paper is concerned with the application of our original variant of the Finite Element Tearing and Interconnecting (FETI) domain decomposition method, called the Total FETI (TFETI), to solve solid mechanics problems exhibiting geometric, material, and contact non-linearities. The TFETI enforces the prescribed displacements by the Lagrange multipliers, so that all the subdomains are 'floating', the kernels of their stiffness matrices are known a priori, and the projector to the natural coarse grid is more effective. The basic theory and relationships of both FETI and TFETI are briefly reviewed and a new version of solution algorithm is presented. It is shown that application of TFETI methodology to the contact problems converts the original problem to the strictly convex quadratic programming problem with bound and equality constraints, so that the effective, in a sense optimal algorithms is to be applied. Numerical experiments show that the method exhibits both numerical and parallel scalabilities.
Algorithms for non-linear M-estimation
DEFF Research Database (Denmark)
Madsen, Kaj; Edlund, O; Ekblom, H
1997-01-01
a sequence of estimation problems for linearized models is solved. In the testing we apply four estimators to ten non-linear data fitting problems. The test problems are also solved by the Generalized Levenberg-Marquardt method and standard optimization BFGS method. It turns out that the new method...
Robust C subroutines for non-linear optimization
DEFF Research Database (Denmark)
Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun
2004-01-01
This report presents a package of robust and easy-to-use C subroutines for solving unconstrained and constrained non-linear optimization problems. The intention is that the routines should use the currently best algorithms available. All routines have standardized calls, and the user does not have...... by changing 1 to 0. The present report is a new and updated version of a previous report NI-91-03 with the same title, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated from Fortran to C. The reason for writing the present report is that some...... of the C subroutines have been replaced by more effective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modi ed to some extent. For a description of the original Fortran subroutines see the report [17]. The software...
Non-linear DSGE Models and The Optimized Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
This paper improves the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which i) incorporates information from new observables and ii) has a small optimization step that minimizes...... the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and this filter is therefore much more efficient than the standard particle filter....
Non-linear theory of elasticity and optimal design
Ratner, LW
2003-01-01
In order to select an optimal structure among possible similar structures, one needs to compare the elastic behavior of the structures. A new criterion that describes elastic behavior is the rate of change of deformation. Using this criterion, the safe dimensions of a structure that are required by the stress distributed in a structure can be calculated. The new non-linear theory of elasticity allows one to determine the actual individual limit of elasticity/failure of a structure using a simple non-destructive method of measurement of deformation on the model of a structure while presently it
On the algebraic representation of certain optimal non-linear binary codes
Greferath, Marcus
2011-01-01
This paper investigates some optimal non-linear codes, in particular cyclic codes, by considering them as (non-linear) codes over Z_4. We use the Fourier transform as well as subgroups of the unit group of a group ring to analyse these codes. In particular we find a presentation of Best's (10, 40, 4) code as a coset of a subgroup in the unit group of a ring, and derive a simple decoding algorithm from this presentation. We also apply this technique to analyse Julin's (12, 144, 4) code and the (12, 24, 12) Hadamard code, as well as to construct a (14, 56, 6) binary code.
Non-linear and signal energy optimal asymptotic filter design
Directory of Open Access Journals (Sweden)
Josef Hrusak
2003-10-01
Full Text Available The paper studies some connections between the main results of the well known Wiener-Kalman-Bucy stochastic approach to filtering problems based mainly on the linear stochastic estimation theory and emphasizing the optimality aspects of the achieved results and the classical deterministic frequency domain linear filters such as Chebyshev, Butterworth, Bessel, etc. A new non-stochastic but not necessarily deterministic (possibly non-linear alternative approach called asymptotic filtering based mainly on the concepts of signal power, signal energy and a system equivalence relation plays an important role in the presentation. Filtering error invariance and convergence aspects are emphasized in the approach. It is shown that introducing the signal power as the quantitative measure of energy dissipation makes it possible to achieve reasonable results from the optimality point of view as well. The property of structural energy dissipativeness is one of the most important and fundamental features of resulting filters. Therefore, it is natural to call them asymptotic filters. The notion of the asymptotic filter is carried in the paper as a proper tool in order to unify stochastic and non-stochastic, linear and nonlinear approaches to signal filtering.
Robust non-gradient C subroutines for non-linear optimization
DEFF Research Database (Denmark)
Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun
2004-01-01
This report presents a package of robust and easy-to-use C subroutines for solving unconstrained and constrained non-linear optimization problems, where gradient information is not required. The intention is that the routines should use the currently best algorithms available. All routines have...... subroutines are obtained by changing 0 to 1. The present report is a new and updated version of a previous report NI-91-04 with the title Non-gradient c Subroutines for Non- Linear Optimization, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated...... from Fortran to C. The reason for writing the present report is that some of the C subroutines have been replaced by more e ective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modified to some extent...
Performance analysis of Non Linear Filtering Algorithms for underwater images
Padmavathi, Dr G; Kumar, Mr M Muthu; Thakur, Suresh Kumar
2009-01-01
Image filtering algorithms are applied on images to remove the different types of noise that are either present in the image during capturing or injected in to the image during transmission. Underwater images when captured usually have Gaussian noise, speckle noise and salt and pepper noise. In this work, five different image filtering algorithms are compared for the three different noise types. The performances of the filters are compared using the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The modified spatial median filter gives desirable results in terms of the above two parameters for the three different noise. Forty underwater images are taken for study.
[Non-linear rectification of sensor based on immune genetic algorithm].
Lu, Lirong; Zhou, Jinyang; Niu, Xiaodong
2014-08-01
A non-linear rectification based on immune genetic algorithm (IGA) is proposed in this paper, for the shortcoming of the non-linearity rectification. This algorithm introducing the biologic immune mechanism into the genetic algorithm can restrain the disadvantages that the poor precision, slow convergence speed and early maturity of the genetic algorithm. Computer simulations indicated that the algorithm not only keeps population diversity, but also increases the convergent speed, precision and the stability greatly. The results have shown the correctness and effectiveness of the method.
[Non-linear rectification of sensor based on immune genetic Algorithm].
Lu, Lirong; Zhou, Jinyang; Niu, Xiaodong
2014-08-01
A non-linear rectification based on immune genetic algorithm (IGA) is proposed in this paper, for the shortcoming of the non-linearity rectification. This algorithm introducing the biologic immune mechanism into the genetic algorithm can restrain the disadvantages that the poor precision, slow convergence speed and early maturity of the genetic algorithm. Computer simulations indicated that the algorithm not only keeps population diversity, but also increases the convergent speed, precision and the stability greatly. The results have shown the correctness and effectiveness of the method.
THE APPLICATION OF GENETIC ALGORITHM IN NON-LINEAR INVERSION OF ROCK MECHANICS PARAMETERS
Institute of Scientific and Technical Information of China (English)
赵晓东
1998-01-01
The non-linear inversion of rock mechanics parameters based on genetic algorithm ispresented. The principle and step of genetic algorithm is also given. A brief discussion of thismethod and an application example is presented at the end of this paper. From the satisfied re-sult, quick, convenient and practical new approach is developed to solve this kind of problems.
Royston, T. J.; Singh, R.
1996-07-01
While significant non-linear behavior has been observed in many vibration mounting applications, most design studies are typically based on the concept of linear system theory in terms of force or motion transmissibility. In this paper, an improved analytical strategy is presented for the design optimization of complex, active of passive, non-linear mounting systems. This strategy is built upon the computational Galerkin method of weighted residuals, and incorporates order reduction and numerical continuation in an iterative optimization scheme. The overall dynamic characteristics of the mounting system are considered and vibratory power transmission is minimized via adjustment of mount parameters by using both passive and active means. The method is first applied through a computational example case to the optimization of basic passive and active, non-linear isolation configurations. It is found that either active control or intentionally introduced non-linearity can improve the mount's performance; but a combination of both produces the greatest benefit. Next, a novel experimental, active, non-linear isolation system is studied. The effect of non-linearity on vibratory power transmission and active control are assessed via experimental measurements and the enhanced Galerkin method. Results show how harmonic excitation can result in multiharmonic vibratory power transmission. The proposed optimization strategy offers designers some flexibility in utilizing both passive and active means in combination with linear and non-linear components for improved vibration mounts.
Robust C subroutines for non-linear optimization
DEFF Research Database (Denmark)
Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun
2004-01-01
to worry about special parameters controlling the iterations. For convenience we include an option for numerical checking of the user s implementation of the gradient. Note that another report [3] presents a collection of robust subroutines for both unconstrained and constrained optimization...... by changing 1 to 0. The present report is a new and updated version of a previous report NI-91-03 with the same title, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated from Fortran to C. The reason for writing the present report is that some...... of the C subroutines have been replaced by more effective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modi ed to some extent. For a description of the original Fortran subroutines see the report [17]. The software...
Identification of a Non-Linear Landing Gear Model Using Nature-Inspired Optimization
Directory of Open Access Journals (Sweden)
Felipe A.C. Viana
2008-01-01
Full Text Available This work deals with the application of a nature-inspired optimization technique to solve an inverse problem represented by the identification of an aircraft landing gear model. The model is described in terms of the landing gear geometry, internal volumes and areas, shock absorber travel, tire type, and gas and oil characteristics of the shock absorber. The solution to this inverse problem can be obtained by using classical gradient-based optimization methods. However, this is a difficult task due to the existence of local minima in the design space and the requirement of an initial guess. These aspects have motivated the authors to explore a nature-inspired approach using a method known as LifeCycle Model. In the present formulation two nature-based methods, namely the Genetic Algorithms and the Particle Swarm Optimization were used. An optimization problem is formulated in which the objective function represents the difference between the measured characteristics of the system and its model counterpart. The polytropic coefficient of the gas and the damping parameter of the shock absorber are assumed as being unknown: they are considered as design variables. As an illustration, experimental drop test data, obtained under zero horizontal speed, were used in the non-linear landing gear model updating of a small aircraft.
Optimal experimental design for non-linear models theory and applications
Kitsos, Christos P
2013-01-01
This book tackles the Optimal Non-Linear Experimental Design problem from an applications perspective. At the same time it offers extensive mathematical background material that avoids technicalities, making it accessible to non-mathematicians: Biologists, Medical Statisticians, Sociologists, Engineers, Chemists and Physicists will find new approaches to conducting their experiments. The book is recommended for Graduate Students and Researchers.
Madsen, Niels K; Godtliebsen, Ian H; Christiansen, Ove
2017-04-07
Vibrational coupled-cluster (VCC) theory provides an accurate method for calculating vibrational spectra and properties of small to medium-sized molecules. Obtaining these properties requires the solution of the non-linear VCC equations which can in some cases be hard to converge depending on the molecule, the basis set, and the vibrational state in question. We present and compare a range of different algorithms for solving the VCC equations ranging from a full Newton-Raphson method to approximate quasi-Newton models using an array of different convergence-acceleration schemes. The convergence properties and computational cost of the algorithms are compared for the optimization of VCC states. This includes both simple ground-state problems and difficult excited states with strong non-linearities. Furthermore, the effects of using tensor-decomposed solution vectors and residuals are investigated and discussed. The results show that for standard ground-state calculations, the conjugate residual with optimal trial vectors algorithm has the shortest time-to-solution although the full Newton-Raphson method converges in fewer macro-iterations. Using decomposed tensors does not affect the observed convergence rates in our test calculations as long as the tensors are decomposed to sufficient accuracy.
Zhang, Chao; Ren, Pinyi; Peng, Jingbo; Wei, Guo; Du, Qinghe; Wang, Yichen
2011-01-01
In this paper, we propose an optimal relay power allocation of an Amplify-and-Forward relay networks with non-linear power amplifiers. Based on Bussgang Linearization Theory, we depict the non-linear amplifying process into a linear system, which lets analyzing system performance easier. To obtain spatial diversity, we design a complete practical framework of a non-linear distortion aware receiver. Consider a total relay power constraint, we propose an optimal power allocation scheme to maxim...
Field computation in non-linear magnetic media using particle swarm optimization
Energy Technology Data Exchange (ETDEWEB)
Adly, A.A. E-mail: amradlya@intouch.com; Abd-El-Hafiz, S.K
2004-05-01
This paper presents an automated particle swarm optimization approach using which field computations may be carried out in devices involving non-linear magnetic media. Among the advantages of the proposed approach are its ability to handle complex geometries and its computational efficiency. The proposed approach has been implemented and computations were carried out for an electromagnet subject to different DC excitation conditions. These computations showed good agreement with the results obtained by the finite-element approach.
A Non-linear Scaling Algorithm Based on chirp-z Transform for Squint Mode FMCW-SAR
Directory of Open Access Journals (Sweden)
Yu Bin-bin
2012-03-01
Full Text Available A non-linear scaling chirp-z imaging algorithm for squint mode Frequency Modulated Continuous Wave Synthetic Aperture Radar (FMCW-SAR is presented to solve the problem of the focus accuracy decline. Based on the non-linear characteristics in range direction for the echo signal in Doppler domain, a non-linear modulated signal is introduced to perform a non-linear scaling based on chirp-z transform. Then the error due to range compression and range migration correction can be reduced, therefore the range resolution of radar image is improved. By using the imaging algorithm proposed, the imaging performances for point targets, compared with that from the original chirp-z algorithm, are demonstrated to be improved in range resolution and image contrast, and to be maintained the same in azimuth resolution.
Yukita, Kazuto; Kato, Shinya; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Kawashima, Yasuhiro
Recently, the independent power producers (IPPs) and the distributed power generations (DGs) are increase on by the electric power system with the power system deregulation. And the power system becomes more complicated. It is necessary to carry out the electric power demand forecasting in order to the power system is operated for the high economical and the high-efficient. For the improvement of electric power demand forecasting, many methods, such as the methods using fuzzy theory, neural network and SDP data, are proposed. In this paper, we proposed the method using STROGANOFF (STructured Re-presentation on Genetic Algorithms for Non-linear Function Fitting) that approximate the value of predictive to the future data by the past data is obtained. Also, the weather condition was considered for the forecasting that is improvement, and the daily peak load forecasting in next day on Chubu district in Japan was carried out, and the effectiveness of proposed method was examined.
Directory of Open Access Journals (Sweden)
M. Pattnaik
2013-08-01
Full Text Available In this paper the concept of fuzzy Non-Linear Programming Technique is applied to solve an economic order quantity (EOQ model under restricted space. Since various types of uncertainties and imprecision are inherent in real inventory problems they are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by usual probabilistic models. The questions how to define inventory optimization tasks in such environment how to interpret optimal solutions arise. This paper allows the modification of the Single item EOQ model in presence of fuzzy decision making process where demand is related to the unit price and the setup cost varies with the quantity produced/Purchased. This paper considers the modification of objective function and storage area in the presence of imprecisely estimated parameters. The model is developed for the problem by employing different modeling approaches over an infinite planning horizon. It incorporates all concepts of a fuzzy arithmetic approach, the quantity ordered and the demand per unit compares both fuzzy non linear and other models. Investigation of the properties of an optimal solution allows developing an algorithm whose validity is illustrated through an example problem and ugh MATLAB (R2009a version software, the two and three dimensional diagrams are represented to the application. Sensitivity analysis of the optimal solution is also studied with respect to changes in different parameter values and to draw managerial insights of the decision problem.
Directory of Open Access Journals (Sweden)
Pattnaik Monalisha
2015-01-01
Full Text Available In this paper, the concept of fuzzy Non-Linear Programming Technique is applied to solve an economic order quantity (EOQ model for restricted budget and space. Since various types of uncertainties and imprecision are inherent in real inventory problems, they are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by the usual probabilistic models. The questions are how to define inventory optimization tasks in such environment and how to interpret the optimal solutions. This paper allow the modification of the Single item EOQ model in presence of fuzzy decision making process where demand is related to the unit price, and the setup cost varies with the quantity produced/Purchased. The modification of objective function, budget, and storage area in the presence of imprecisely estimated parameters are considered. The model is developed by employing different approaches over an infinite planning horizon. It incorporates all the concepts of a fuzzy arithmetic approach and comparative analysis with other non linear models. Investigation of the properties of an optimal solution allows developing an algorithm whose validity is illustrated by an example problem, and two and three dimensional diagrams are represented to this application through MATL(R2009a software. Sensitivity analysis of the optimal solution is studied with respect to the changes of different parameter values for obtaining managerial insights of the decision problem.
Umbarkar, A. J.; Balande, U. T.; Seth, P. D.
2017-06-01
The field of nature inspired computing and optimization techniques have evolved to solve difficult optimization problems in diverse fields of engineering, science and technology. The firefly attraction process is mimicked in the algorithm for solving optimization problems. In Firefly Algorithm (FA) sorting of fireflies is done by using sorting algorithm. The original FA is proposed with bubble sort for ranking the fireflies. In this paper, the quick sort replaces bubble sort to decrease the time complexity of FA. The dataset used is unconstrained benchmark functions from CEC 2005 [22]. The comparison of FA using bubble sort and FA using quick sort is performed with respect to best, worst, mean, standard deviation, number of comparisons and execution time. The experimental result shows that FA using quick sort requires less number of comparisons but requires more execution time. The increased number of fireflies helps to converge into optimal solution whereas by varying dimension for algorithm performed better at a lower dimension than higher dimension.
Institute of Scientific and Technical Information of China (English)
ZHANG Ren; HONG Mei; SUN Zhao-bo; NIU Sheng-jie; ZHU Wei-jun; MIN Jin-zhong; WAN Qi-lin
2006-01-01
Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF(empirical orthogonal function) temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results.A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.
Elenchezhiyan, M; Prakash, J
2015-09-01
In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme.
DEFF Research Database (Denmark)
Kjems, Ulrik; Storther, Stephen C.; Anderson, Jon
1999-01-01
This paper addresses the problem of neuro-anatomical registration across individuals for functional [15O]water PET activation studies. A new algorithm for 3D non-linear structural registration (warping) of MR scans is presented. The method performs a hierarchically scaled search for a displacement...
DEFF Research Database (Denmark)
Kjems, Ulrik; Storther, Stephen C.; Anderson, Jon
1999-01-01
This paper addresses the problem of neuro-anatomical registration across individuals for functional [15O]water PET activation studies. A new algorithm for 3D non-linear structural registration (warping) of MR scans is presented. The method performs a hierarchically scaled search for a displacemen...
Pitschner, H F; Berkowitsch, A
2001-01-01
Symbolic dynamics as a non linear method and computation of the normalized algorithmic complexity (C alpha) was applied to basket-catheter mapping of atrial fibrillation (AF) in the right human atrium. The resulting different degrees of organisation of AF have been compared to conventional classification of Wells. Short time temporal and spatial distribution of the C alpha during AF and effects of propafenone on this distribution have been investigated in 30 patients. C alpha was calculated for a moving window. Generated C alpha was analyzed within 10 minutes before and after administration of propafenone. The inter-regional C alpha distribution was statistically analyzed. Inter-regional C alpha differences were found in all patients (p complexity areas according to individual patterns. A significant C alpha increase in cranio-caudal direction was confirmed inter-individually (p complexity.
Optimal Reservoir Operation for Hydropower Generation using Non-linear Programming Model
Arunkumar, R.; Jothiprakash, V.
2012-05-01
Hydropower generation is one of the vital components of reservoir operation, especially for a large multi-purpose reservoir. Deriving optimal operational rules for such a large multi-purpose reservoir serving various purposes like irrigation, hydropower and flood control are complex, because of the large dimension of the problem and the complexity is more if the hydropower production is not an incidental. Thus optimizing the operations of a reservoir serving various purposes requires a systematic study. In the present study such a large multi-purpose reservoir, namely, Koyna reservoir operations are optimized for maximizing the hydropower production subject to the condition of satisfying the irrigation demands using a non-linear programming model. The hydropower production from the reservoir is analysed for three different dependable inflow conditions, representing wet, normal and dry years. For each dependable inflow conditions, various scenarios have been analyzed based on the constraints on the releases and the results are compared. The annual power production, combined monthly power production from all the powerhouses, end of month storage levels, evaporation losses and surplus are discussed. From different scenarios, it is observed that more hydropower can be generated for various dependable inflow conditions, if the restrictions on releases are slightly relaxed. The study shows that Koyna dam is having potential to generate more hydropower.
Levy, Daniel; Roos, Jason; Robinson, Jace; Carpenter, William; Martin, Richard; Taylor, Clark; Sugrue, Joseph; Terzuoli, Andrew
2016-06-01
Multiple sensors are used in a variety of geolocation systems. Many use Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) measurements to estimate the most likely location of a signal. When an object does not emit an RF signal, Angle of Arrival (AOA) measurements using optical or infrared frequencies become more feasible than TDOA or RSS measurements. AOA measurements can be created from any sensor platform with any sort of optical sensor, location and attitude knowledge to track passive objects. Previous work has created a non-linear optimization (NLO) method for calculating the most likely estimate from AOA measurements. Two new modifications to the NLO algorithm are created and shown to correct AOA measurement errors by estimating the inherent bias and time-drift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. One method corrects the sensor bias in post processing while treating the NLO method as a module. The other method directly corrects the sensor bias within the NLO algorithm by incorporating the bias parameters as a state vector in the estimation process. These two methods are analyzed using various Monte-Carlo simulations to check the general performance of the two modifications in comparison to the original NLO algorithm.
Non-linear stochastic optimal control of acceleration parametrically excited systems
Wang, Yong; Jin, Xiaoling; Huang, Zhilong
2016-02-01
Acceleration parametrical excitations have not been taken into account due to the lack of physical significance in macroscopic structures. The explosive development of microtechnology and nanotechnology, however, motivates the investigation of the acceleration parametrically excited systems. The adsorption and desorption effects dramatically change the mass of nano-sized structures, which significantly reduces the precision of nanoscale sensors or can be reasonably utilised to detect molecular mass. This manuscript proposes a non-linear stochastic optimal control strategy for stochastic systems with acceleration parametric excitation based on stochastic averaging of energy envelope and stochastic dynamic programming principle. System acceleration is approximately expressed as a function of system displacement in a short time range under the conditions of light damping and weak excitations, and the acceleration parametrically excited system is shown to be equivalent to a constructed system with an additional displacement parametric excitation term. Then, the controlled system is converted into a partially averaged Itô equation with respect to the total system energy through stochastic averaging of energy envelope, and the optimal control strategy for the averaged system is derived from solving the associated dynamic programming equation. Numerical results for a controlled Duffing oscillator indicate the efficacy of the proposed control strategy.
Optimization of elstomeric micro-fluidic valve dimensions using non-linear finite element methods
Directory of Open Access Journals (Sweden)
H Khawaja
2016-04-01
Full Text Available We use a nonlinear finite element (FE method model to compare,optimize and determine the limits for useful geometries of microfluidicvalves in elastomer polydimethylsiloxane (PDMS. Simulations havebeen performed with the aim of finding the optimal shape, size andlocation of pressurization that minimizes the pressure required to operatethe valve. One important constraint governing the design parameters isthat the stresses should be within elastic limits, so that the componentremains safe from any type of structural failure. To obtain reliable results,non-linear stress analysis was performed using the Mooney-Rivlin 9parameter approximation which is based on the Hyper Elastic MaterialModel. A 20 noded brick element was used for the development of FEmodel. Mesh sensitivity analysis was also performed to assess the qualityof the results. The simulations were performed with commerciallyavailable FE modeling software, developed by ANSYS Inc. to determinethe effect of varying different geometric parameters on the performanceof micro-fluidic valves.The aim of this work is to determine the geometry of the channel crosssectionthat would result in the largest deflection for the least appliedpressure, i.e. to minimize the pressure needed to operate the valve.
A new non-linear vortex lattice method:Applications to wing aerodynamic optimizations
Institute of Scientific and Technical Information of China (English)
Oliviu S? ugar Gabor; Andreea Koreanschi; Ruxandra Mihaela Botez
2016-01-01
This paper presents a new non-linear formulation of the classical Vortex Lattice Method (VLM) approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity, and due to its low computational cost, it represents a very good tool to perform rapid and accurate wing design and optimization procedures. The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections, according to strip theory, and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface, calculated with a fully three-dimensional vortex lifting law. The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment, as well as in predicting the wing drag. The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.
A new non-linear vortex lattice method: Applications to wing aerodynamic optimizations
Directory of Open Access Journals (Sweden)
Oliviu Şugar Gabor
2016-10-01
Full Text Available This paper presents a new non-linear formulation of the classical Vortex Lattice Method (VLM approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity, and due to its low computational cost, it represents a very good tool to perform rapid and accurate wing design and optimization procedures. The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections, according to strip theory, and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface, calculated with a fully three-dimensional vortex lifting law. The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment, as well as in predicting the wing drag. The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.
Non-Linearity Analysis of Depth and Angular Indexes for Optimal Stereo SLAM
Directory of Open Access Journals (Sweden)
David Schleicher
2010-04-01
Full Text Available In this article, we present a real-time 6DoF egomotion estimation system for indoor environments using a wide-angle stereo camera as the only sensor. The stereo camera is carried in hand by a person walking at normal walking speeds 3–5 km/h. We present the basis for a vision-based system that would assist the navigation of the visually impaired by either providing information about their current position and orientation or guiding them to their destination through different sensing modalities. Our sensor combines two different types of feature parametrization: inverse depth and 3D in order to provide orientation and depth information at the same time. Natural landmarks are extracted from the image and are stored as 3D or inverse depth points, depending on a depth threshold. This depth threshold is used for switching between both parametrizations and it is computed by means of a non-linearity analysis of the stereo sensor. Main steps of our system approach are presented as well as an analysis about the optimal way to calculate the depth threshold. At the moment each landmark is initialized, the normal of the patch surface is computed using the information of the stereo pair. In order to improve long-term tracking, a patch warping is done considering the normal vector information. Some experimental results under indoor environments and conclusions are presented.
Njafa, Jean-pierre Tchapet
2016-01-01
In this paper we present a model of Quantum Associative Memory (QAM) which can be a helpful tool for physicians without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow fever and dengue) which have several similar symptoms. The memory can distinguish single infection from multi-infection. The two algorithms used for the Quantum Associative Memory are improve models of original linear algorithm made by Ventura for Quantum Associative Memory and the non-linear quantum search algorithm of Abrams and Lloyd. From the simulation results given, it appears that the efficiency of recognition is good when a particular symptom of a disease with the similar symptoms are inserted as linear algorithm is the main algorithm. The non-linear algorithm allows to confirm the diagnosis or to give some advices to the physician. So our QAM which have a graphical user interface for desktop and smartphone is a sensitive, low-cost diagnostic tools that enable rapid and ac...
Ramcharan, A. M.; Kemanian, A.; Richard, T.
2013-12-01
The largest terrestrial carbon pool is soil, storing more carbon than present in above ground biomass (Jobbagy and Jackson, 2000). In this context, soil organic carbon has gained attention as a managed sink for atmospheric CO2 emissions. The variety of models that describe soil carbon cycling reflects the relentless effort to characterize the complex nature of soil and the carbon within it. Previous works have laid out the range of mathematical approaches to soil carbon cycling but few have compared model structure performance in diverse agricultural scenarios. As interest in increasing the temporal and spatial scale of models grows, assessing the performance of different model structures is essential to drawing reasonable conclusions from model outputs. This research will address this challenge using the Evolutionary Algorithm Borg-MOEA to optimize the functionality of carbon models in a multi-objective approach to parameter estimation. Model structure performance will be assessed through analysis of multi-objective trade-offs using experimental data from twenty long-term carbon experiments across the globe. Preliminary results show a successful test of this proof of concept using a non-linear soil carbon model structure. Soil carbon dynamics were based on the amount of carbon inputs to the soil and the degree of organic matter saturation of the soil. The degree of organic matter saturation of the soil was correlated with the soil clay content. Six parameters of the non-linear soil organic carbon model were successfully optimized to steady-state conditions using Borg-MOEA and datasets from five agricultural locations in the United States. Given that more than 50% of models rely on linear soil carbon decomposition dynamics, a linear model structure was also optimized and compared to the non-linear case. Results indicate linear dynamics had a significantly lower optimization performance. Results show promise in using the Evolutionary Algorithm Borg-MOEA to assess
Ernest II, Charles Steven
2013-01-01
Despite the growing promise of pharmaceutical research, inferior experimentation or interpretation of data can inhibit breakthrough molecules from finding their way out of research institutions and reaching patients. This thesis provides evidence that better characterization of pre-clinical and clinical data can be accomplished using non-linear mixed effect modeling (NLMEM) and more effective experiments can be conducted using optimal design (OD). To demonstrate applicability of NLMEM and OD...
Energy Technology Data Exchange (ETDEWEB)
Zhu, Z. W., E-mail: zhuzhiwen@tju.edu.cn [Department of Mechanics, Tianjin University, 300072, Tianjin (China); Tianjin Key Laboratory of Non-linear Dynamics and Chaos Control, 300072, Tianjin (China); Zhang, W. D., E-mail: zhangwenditju@126.com; Xu, J., E-mail: xujia-ld@163.com [Department of Mechanics, Tianjin University, 300072, Tianjin (China)
2014-03-15
The non-linear dynamic characteristics and optimal control of a giant magnetostrictive film (GMF) subjected to in-plane stochastic excitation were studied. Non-linear differential items were introduced to interpret the hysteretic phenomena of the GMF, and the non-linear dynamic model of the GMF subjected to in-plane stochastic excitation was developed. The stochastic stability was analysed, and the probability density function was obtained. The condition of stochastic Hopf bifurcation and noise-induced chaotic response were determined, and the fractal boundary of the system's safe basin was provided. The reliability function was solved from the backward Kolmogorov equation, and an optimal control strategy was proposed in the stochastic dynamic programming method. Numerical simulation shows that the system stability varies with the parameters, and stochastic Hopf bifurcation and chaos appear in the process; the area of the safe basin decreases when the noise intensifies, and the boundary of the safe basin becomes fractal; the system reliability improved through stochastic optimal control. Finally, the theoretical and numerical results were proved by experiments. The results are helpful in the engineering applications of GMF.
Pando, V.; García-Laguna, J.; San-José, L. A.
2012-11-01
In this article, we integrate a non-linear holding cost with a stock-dependent demand rate in a maximising profit per unit time model, extending several inventory models studied by other authors. After giving the mathematical formulation of the inventory system, we prove the existence and uniqueness of the optimal policy. Relying on this result, we can obtain the optimal solution using different numerical algorithms. Moreover, we provide a necessary and sufficient condition to determine whether a system is profitable, and we establish a rule to check when a given order quantity is the optimal lot size of the inventory model. The results are illustrated through numerical examples and the sensitivity of the optimal solution with respect to changes in some values of the parameters is assessed.
Computational Modelling and Optimal Control of Ebola Virus Disease with non-Linear Incidence Rate
Takaidza, I.; Makinde, O. D.; Okosun, O. K.
2017-03-01
The 2014 Ebola outbreak in West Africa has exposed the need to connect modellers and those with relevant data as pivotal to better understanding of how the disease spreads and quantifying the effects of possible interventions. In this paper, we model and analyse the Ebola virus disease with non-linear incidence rate. The epidemic model created is used to describe how the Ebola virus could potentially evolve in a population. We perform an uncertainty analysis of the basic reproductive number R 0 to quantify its sensitivity to other disease-related parameters. We also analyse the sensitivity of the final epidemic size to the time control interventions (education, vaccination, quarantine and safe handling) and provide the cost effective combination of the interventions.
A comparison of Eulerian and Lagrangian transport and non-linear reaction algorithms
Benson, David A.; Aquino, Tomás; Bolster, Diogo; Engdahl, Nicholas; Henri, Christopher V.; Fernàndez-Garcia, Daniel
2017-01-01
When laboratory-measured chemical reaction rates are used in simulations at the field-scale, the models typically overpredict the apparent reaction rates. The discrepancy is primarily due to poorer mixing of chemically distinct waters at the larger scale. As a result, realistic field-scale predictions require accurate simulation of the degree of mixing between fluids. The Lagrangian particle-tracking (PT) method is a now-standard way to simulate the transport of conservative or sorbing solutes. The method's main advantage is the absence of numerical dispersion (and its artificial mixing) when simulating advection. New algorithms allow particles of different species to interact in nonlinear (e.g., bimolecular) reactions. Therefore, the PT methods hold a promise of more accurate field-scale simulation of reactive transport because they eliminate the masking effects of spurious mixing due to advection errors inherent in grid-based methods. A hypothetical field-scale reaction scenario is constructed and run in PT and Eulerian (finite-volume/finite-difference) simulators. Grid-based advection schemes considered here include 1st- to 3rd-order spatially accurate total-variation-diminishing flux-limiting schemes, both of which are widely used in current transport/reaction codes. A homogeneous velocity field in which the Courant number is everywhere unity, so that the chosen Eulerian methods incur no error when simulating advection, shows that both the Eulerian and PT methods can achieve convergence in the L1 (integrated concentration) norm, but neither shows stricter pointwise convergence. In this specific case with a constant dispersion coefficient and bimolecular reaction A + B → P , the correct total amount of product is 0.221MA0, where MA0 is the original mass of reactant A. When the Courant number drops, the grid-based simulations can show remarkable errors due to spurious over- and under-mixing. In a heterogeneous velocity field (keeping the same constant and
Optimizing BAO measurements with non-linear transformations of Lyman-alpha forest
Wang, Xinkang; Seljak, Uros
2014-01-01
We explore the effect of applying a non-linear transformation to the Lyman-$\\alpha$ forest transmitted flux $F=e^{-\\tau}$ and the ability of analytic models to predict the resulting clustering amplitude. Both the large-scale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signal-to-noise ratio on large scales using Taylor expansion up to third order. We achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore analytic model for the large-scale biasing of the Ly$\\alpha$ forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to large-scale density fluctuations, especially ...
Balkaya, Çağlayan; Ekinci, Yunus Levent; Göktürkler, Gökhan; Turan, Seçil
2017-01-01
3D non-linear inversion of total field magnetic anomalies caused by vertical-sided prismatic bodies has been achieved by differential evolution (DE), which is one of the population-based evolutionary algorithms. We have demonstrated the efficiency of the algorithm on both synthetic and field magnetic anomalies by estimating horizontal distances from the origin in both north and east directions, depths to the top and bottom of the bodies, inclination and declination angles of the magnetization, and intensity of magnetization of the causative bodies. In the synthetic anomaly case, we have considered both noise-free and noisy data sets due to two vertical-sided prismatic bodies in a non-magnetic medium. For the field case, airborne magnetic anomalies originated from intrusive granitoids at the eastern part of the Biga Peninsula (NW Turkey) which is composed of various kinds of sedimentary, metamorphic and igneous rocks, have been inverted and interpreted. Since the granitoids are the outcropped rocks in the field, the estimations for the top depths of two prisms representing the magnetic bodies were excluded during inversion studies. Estimated bottom depths are in good agreement with the ones obtained by a different approach based on 3D modelling of pseudogravity anomalies. Accuracy of the estimated parameters from both cases has been also investigated via probability density functions. Based on the tests in the present study, it can be concluded that DE is a useful tool for the parameter estimation of source bodies using magnetic anomalies.
Non-linear optimization of track layouts in loop-sorting-systems
DEFF Research Database (Denmark)
Sørensen, Søren Emil; Hansen, Michael R.; Ebbesen, Morten K.
2013-01-01
Optimization used for enhancing geometric structures iswell known. Applying obstacles to the shape optimization problemis on the other hand not very common. It requires a fast contact search algorithmand an exact continuous formulation to solve the problem robustly. This paper focuses on combinin...
Non-linear Global Optimization using Interval Arithmetic and Constraint Propagation
DEFF Research Database (Denmark)
Kjøller, Steffen; Kozine, Pavel; Madsen, Kaj;
2006-01-01
In this Chapter a new branch-and-bound method for global optimization is presented. The method combines the classical interval global optimization method with constraint propagation techniques. The latter is used for including solutions of the necessary condition f'(x)=0. The constraint propagation...
Constrained Optimal Stochastic Control of Non-Linear Wave Energy Point Absorbers
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Chen, Jian-Bing; Kramer, Morten
2014-01-01
The paper deals with the stochastic optimal control of a wave energy point absorber with strong nonlinear buoyancy forces using the reactive force from the electric generator on the absorber as control force. The considered point absorber has only one degree of freedom, heave motion, which is used...... presented in the paper. The effect of nonlinear buoyancy force – in comparison to linear buoyancy force – and constraints of the controller on the power outtake of the device have been studied in details and supported by numerical simulations....
Sharqawy, Mostafa H.
2016-12-01
Pore network models (PNM) of Berea and Fontainebleau sandstones were constructed using nonlinear programming (NLP) and optimization methods. The constructed PNMs are considered as a digital representation of the rock samples which were based on matching the macroscopic properties of the porous media and used to conduct fluid transport simulations including single and two-phase flow. The PNMs consisted of cubic networks of randomly distributed pores and throats sizes and with various connectivity levels. The networks were optimized such that the upper and lower bounds of the pore sizes are determined using the capillary tube bundle model and the Nelder-Mead method instead of guessing them, which reduces the optimization computational time significantly. An open-source PNM framework was employed to conduct transport and percolation simulations such as invasion percolation and Darcian flow. The PNM model was subsequently used to compute the macroscopic properties; porosity, absolute permeability, specific surface area, breakthrough capillary pressure, and primary drainage curve. The pore networks were optimized to allow for the simulation results of the macroscopic properties to be in excellent agreement with the experimental measurements. This study demonstrates that non-linear programming and optimization methods provide a promising method for pore network modeling when computed tomography imaging may not be readily available.
Vergnole, Sébastien; Lévesque, Daniel; Lamouche, Guy
2010-05-10
We evaluate various signal processing methods to handle the non-linearity in wavenumber space exhibited by most laser sources for swept-source optical coherence tomography. The following methods are compared for the same set of experimental data: non-uniform discrete Fourier transforms with Vandermonde matrix or with Lomb periodogram, resampling with linear interpolation or spline interpolation prior to fast-Fourier transform (FFT), and resampling with convolution prior to FFT. By selecting an optimized Kaiser-Bessel window to perform the convolution, we show that convolution followed by FFT is the most efficient method. It allows small fractional oversampling factor between 1 and 2, thus a minimal computational time, while retaining an excellent image quality. (c) 2010 Optical Society of America.
Ender, I A; Flegontova, E Yu; Gerasimenko, A B
2016-01-01
An algorithm for sequential calculation of non-isotropic matrix elements of the collision integral which are necessary for the solution of the non-linear Boltzmann equation by moment method is proposed. Isotropic matrix elements that we believe are known, are starting ones. The procedure is valid for any interaction law and any mass ratio of the colliding particles.
Geiges, A.; Nowak, W.; Rubin, Y.
2013-12-01
Stochastic models of sub-surface systems generally suffer from parametric and conceptual uncertainty. To reduce the model uncertainty, model parameters are calibrated using additional collected data. These data often come from costly data acquisition campaigns that need to be optimized to collect the data with the highest data utility (DU) or value of information. In model-based approaches, the DU is evaluated based on the uncertain model itself and is therefore uncertain as well. Additionally, for non-linear models, data utility depends on the yet unobserved measurement values and can only be estimated as an expected value over an assumed distribution of possible measurement values. Both factors introduce uncertainty into the optimization of field campaigns. We propose and investigate a sequential interaction scheme between campaign optimization, data collection and model calibration. The field campaign is split in individual segments. Each segment consists of optimization, segment-wise data collection, and successive model calibration or data assimilation. By doing so, (1) the expected data utility for the newly collected data is replaced by their actual one, (2) the calibration restricts both conceptual and parametric model uncertainty, and thus (3) the distribution of possible future data values for the subsequent campaign segments also changes. Hence, the model to describe the real system improves successively with each collected data segment, and so does the estimate of the yet remaining data requirements to achieve the overall investigation goals. We will show that using the sequentially improved model for the optimal design (OD) of the remaining field campaign leads to superior and more targeted designs.However, this traditional sequential OD optimizes small data segments one-by-one. In such a strategy, possible mutual dependencies with the possible data values and the optimization of data values collection in later segments are neglected. This allows a
Group Leaders Optimization Algorithm
Daskin, Anmer
2010-01-01
Complexity of global optimization algorithms makes implementation of the algorithms difficult and leads the algorithms to require more computer resources for the optimization process. The ability to explore the whole solution space without increasing the complexity of algorithms has a great importance to not only get reliable results but so also make the implementation of these algorithms more convenient for higher dimensional and complex-real world problems in science and engineering. In this paper, we present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique that is designed into a group architecture similar to the architecture of Cooperative Coevolutionary Algorithms. Therefore, we present the implementation method and the experimental results for the single and multidimensional optimization test problems and a scientific real world problem, the energies and the geometric structures of Lennard-Jones clusters.
Optimization algorithms and applications
Arora, Rajesh Kumar
2015-01-01
Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc
Directory of Open Access Journals (Sweden)
Xudong Yin
2014-02-01
Full Text Available The authors propose to implement conditional non-linear optimal perturbation related to model parameters (CNOP-P through an ensemble-based approach. The approach was first used in our earlier study and is improved to be suitable for calculating CNOP-P. Idealised experiments using the Lorenz-63 model are conducted to evaluate the performance of the improved ensemble-based approach. The results show that the maximum prediction error after optimisation has been multiplied manifold compared with the initial-guess prediction error, and is extremely close to, or greater than, the maximum value of the exhaustive attack method (a million random samples. The calculation of CNOP-P by the ensemble-based approach is capable of maintaining a high accuracy over a long prediction time under different constraints and initial conditions. Further, the CNOP-P obtained by the approach is applied to sensitivity analysis of the Lorenz-63 model. The sensitivity analysis indicates that when the prediction time is set to 0.2 time units, the Lorenz-63 model becomes extremely insensitive to one parameter, which leaves the other two parameters to affect the uncertainty of the model. Finally, a serial of parameter estimation experiments are performed to verify sensitivity analysis. It is found that when the three parameters are estimated simultaneously, the insensitive parameter is estimated much worse, but the Lorenz-63 model can still generate a very good simulation thanks to the relatively accurate values of the other two parameters. When only two sensitive parameters are estimated simultaneously and the insensitive parameter is left to be non-optimised, the outcome is better than the case when the three parameters are estimated simultaneously. With the increase of prediction time and observation, however, the model sensitivity to the insensitive parameter increases accordingly and the insensitive parameter can also be estimated successfully.
Energy Technology Data Exchange (ETDEWEB)
Welsch, Dominic Markus
2010-03-10
The High-Energy Storage Ring (HESR) is part of the upcoming Facility for Antiproton and Ion Research (FAIR) which is planned as a major extension to the present facility of the Helmholtzzentrum fuer Schwerionenforschung (GSI) in Darmstadt. The HESR will provide antiprotons in the momentum range from 1.5 to 15 GeV/c for the internal target experiment PANDA. The demanding requirements of PANDA in terms of beam quality and luminosity together with a limited production rate of antiprotons call for a long beam life time and a minimum of beam loss. Therefore, an effective closed orbit correction and a sufficiently large dynamic aperture of the HESR are crucial. With this thesis I present my work on both of these topics. The expected misalignments of beam guiding magnets have been estimated and used to simulate the closed orbit in the HESR. A closed orbit correction scheme has been developed for different ion optical settings of the HESR and numerical simulations have been performed to validate the scheme. The proposed closed orbit correction method which uses the orbit response matrix has been benchmarked at the Cooler Synchrotron COSY of the Forschungszentrum Juelich. A chromaticity correction scheme for the HESR consisting of sextupole magnets has been developed to reduce tune spread and thus to minimize the emittance growth caused by betatron resonances. The chromaticity correction scheme has been optimized through dynamic aperture calculations. The estimated field errors of the HESR dipole and quadrupole magnets have been included in the non-linear beam dynamics studies. Investigations concerning their optimization have been carried out. The ion optical settings of the HESR have been improved using dynamic aperture calculations and the technique of frequency map analysis. The related diffusion coefficient was also used to predict long-term stability based on short-term particle tracking. With a reasonable reduction of the quadrupole magnets field errors and a
Directory of Open Access Journals (Sweden)
Jia Hui Ong
2016-07-01
Full Text Available Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluation
Directory of Open Access Journals (Sweden)
K. Lenin
2013-03-01
Full Text Available Reactive Power Optimization is a complex combinatorial optimization problem involving non-linear function having multiple local minima, non-linear and discontinuous constrains. This paper presents Attractive and repulsive Particle Swarm Optimization (ARPSO and Random Virus Algorithm (RVA in trying to overcome the Problem of premature convergence. RVA and ARPSO is applied to Reactive Power Optimization problem and is evaluated on standard IEEE 30Bus System. The results show that RVA prevents premature convergence to high degree but still keeps a rapid convergence. It gives best solution when compared to Attractive and repulsive Particle Swarm Optimization (ARPSO and Particle Swarm Optimization (PSO.
Nature-inspired optimization algorithms
Yang, Xin-She
2014-01-01
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning
非线性自适应拥塞控制算法研究%Study of Non-Linear Adaptive Congestion Control Algorithm
Institute of Scientific and Technical Information of China (English)
范训礼; 郑锋; Lin GUAN
2011-01-01
研究丢弃概率的变化率与队列长度稳定性间的关系,分析ARED算法及REM算法的丢弃概率计算函数,采用非线性化函数计算丢弃概率,提出一种非线性自适应拥塞控制算法(NLACCA),根据队列长度与目标队列长度中值的偏离程度动态地调整丢弃概率的变化率,从而减小队列长度波动,提高算法稳定性.在NS-2上进行的大量实验结果表明,该算法具有队列长度抖动性小、平均时延低、丢包数少等特点.%This paper studies the relationship between the changing rate of drop probability and the queue stability, and specifically researches computing function of dropping probability of Adaptive Random Early Detection(ARED) algorithm and Random Exponent Marking(REM) algorithm respectively. As a result, a Non-Linear Adaptive Congestion Control Algorithm(NLACCA) is proposed. Based on the Active Queue Management(AQM) scheme, which provides a non-linear adaptation to the dropping probability function of the ARED, NLACCA enables the dropping probability gradient to vary along with the deviation that is between the instantaneous queue length and the target queue length. NLACCA can not only reduce the jitter of the target queue length, but also improve the stability of the algorithm. Simulation results demonstrate that the NLAC CA algorithm outperforms in most scenarios, such as the jitter of queue length, delay, and packets dropped.
The study of cuckoo optimization algorithm for production planning problem
Akbarzadeh, Afsane; Shadkam, Elham
2015-01-01
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and common problems for production planning problem to optimize. In this study, one of the mathematical models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is efficient method to solve continues non linear problem. Moreover, mentioned models of production planning solved with Genetic algorithm and Lingo software and the results will compared. The Cucko...
Fonville, Judith M; Bylesjö, Max; Coen, Muireann; Nicholson, Jeremy K; Holmes, Elaine; Lindon, John C; Rantalainen, Mattias
2011-10-31
Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.
Non-linear canonical correlation
van der Burg, Eeke; de Leeuw, Jan
1983-01-01
Non-linear canonical correlation analysis is a method for canonical correlation analysis with optimal scaling features. The method fits many kinds of discrete data. The different parameters are solved for in an alternating least squares way and the corresponding program is called CANALS. An
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2013-01-01
and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2011-01-01
the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2011-01-01
the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...
Energy Technology Data Exchange (ETDEWEB)
Fonville, Judith M., E-mail: j.fonville07@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Bylesjoe, Max, E-mail: max.bylesjo@almacgroup.com [Almac Diagnostics, 19 Seagoe Industrial Estate, Craigavon BT63 5QD (United Kingdom); Coen, Muireann, E-mail: m.coen@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Nicholson, Jeremy K., E-mail: j.nicholson@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Holmes, Elaine, E-mail: elaine.holmes@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Lindon, John C., E-mail: j.lindon@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Rantalainen, Mattias, E-mail: rantalai@stats.ox.ac.uk [Department of Statistics, Oxford University, 1 South Parks Road, Oxford OX1 3TG (United Kingdom)
2011-10-31
Highlights: {yields} Non-linear modeling of metabonomic data using K-OPLS. {yields} automated optimization of the kernel parameter by simulated annealing. {yields} K-OPLS provides improved prediction performance for exemplar spectral data sets. {yields} software implementation available for R and Matlab under GPL v2 license. - Abstract: Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and
Lehtonen, J V; Denessiouk, K; May, A C; Johnson, M S
1999-02-15
We have developed a generic tool for the automatic identification of regions of local structural similarity in unrelated proteins having different folds, as well as for defining more global similarities that result from homologous protein structures. The computer program GENFIT has evolved from the genetic algorithm-based three-dimensional protein structure comparison program GA_FIT. GENFIT, however, can locate and superimpose regions of local structural homology regardless of their position in a pair of structures, the fold topology, or the chain direction. Furthermore, it is possible to restrict the search to a volume centered about a region of interest (e.g., catalytic site, ligand-binding site) in two protein structures. We present a number of examples to illustrate the function of the program, which is a parallel processing implementation designed for distribution to multiple machines over a local network or to run on a single multiprocessor computer.
Efficient Non Linear Loudspeakers
DEFF Research Database (Denmark)
Petersen, Bo R.; Agerkvist, Finn T.
2006-01-01
Loudspeakers have traditionally been designed to be as linear as possible. However, as techniques for compensating non linearities are emerging, it becomes possible to use other design criteria. This paper present and examines a new idea for improving the efficiency of loudspeakers at high levels...... by changing the voice coil layout. This deliberate non-linear design has the benefit that a smaller amplifier can be used, which has the benefit of reducing system cost as well as reducing power consumption....
Bobály, Balázs; Randazzo, Giuseppe Marco; Rudaz, Serge; Guillarme, Davy; Fekete, Szabolcs
2017-01-20
The goal of this work was to evaluate the potential of non-linear gradients in hydrophobic interaction chromatography (HIC), to improve the separation between the different homologous species (drug-to-antibody, DAR) of commercial antibody-drug conjugates (ADC). The selectivities between Brentuximab Vedotin species were measured using three different gradient profiles, namely linear, power function based and logarithmic ones. The logarithmic gradient provides the most equidistant retention distribution for the DAR species and offers the best overall separation of cysteine linked ADC in HIC. Another important advantage of the logarithmic gradient, is its peak focusing effect for the DAR0 species, which is particularly useful to improve the quantitation limit of DAR0. Finally, the logarithmic behavior of DAR species of ADC in HIC was modelled using two different approaches, based on i) the linear solvent strength theory (LSS) and two scouting linear gradients and ii) a new derived equation and two logarithmic scouting gradients. In both cases, the retention predictions were excellent and systematically below 3% compared to the experimental values. Copyright © 2016 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Eusebio Eduardo Hernández Martinez
2013-01-01
Full Text Available In robotics, solving the direct kinematics problem (DKP for parallel robots is very often more difficult and time consuming than for their serial counterparts. The problem is stated as follows: given the joint variables, the Cartesian variables should be computed, namely the pose of the mobile platform. Most of the time, the DKP requires solving a non‐linear system of equations. In addition, given that the system could be non‐convex, Newton or Quasi‐Newton (Dogleg based solvers get trapped on local minima. The capacity of such kinds of solvers to find an adequate solution strongly depends on the starting point. A well‐known problem is the selection of such a starting point, which requires a priori information about the neighbouring region of the solution. In order to circumvent this issue, this article proposes an efficient method to select and to generate the starting point based on probabilistic learning. Experiments and discussion are presented to show the method performance. The method successfully avoids getting trapped on local minima without the need for human intervention, which increases its robustness when compared with a single Dogleg approach. This proposal can be extended to other structures, to any non‐linear system of equations, and of course, to non‐linear optimization problems.
Efficient Non Linear Loudspeakers
DEFF Research Database (Denmark)
Petersen, Bo R.; Agerkvist, Finn T.
2006-01-01
Loudspeakers have traditionally been designed to be as linear as possible. However, as techniques for compensating non linearities are emerging, it becomes possible to use other design criteria. This paper present and examines a new idea for improving the efficiency of loudspeakers at high levels...... by changing the voice coil layout. This deliberate non-linear design has the benefit that a smaller amplifier can be used, which has the benefit of reducing system cost as well as reducing power consumption.......Loudspeakers have traditionally been designed to be as linear as possible. However, as techniques for compensating non linearities are emerging, it becomes possible to use other design criteria. This paper present and examines a new idea for improving the efficiency of loudspeakers at high levels...
Non-linear Manifold Learning Algorithm Based on Intensity of Points%基于点密集度的非线性流形学习算法
Institute of Scientific and Technical Information of China (English)
黄淑萍
2012-01-01
This paper presents a non-linear manifold learning algorithm based on intensity of sample points. It proposes an effective intensity parameter of sample points, which constraints the low-dimensional embedding of uneven data well. There is a better embedding result than LLE. The experimental results on the artificial and face datasets show that the new algorithm yields a better embedding and classification result.%提出一种样本点密集度的非线性流形学习算法．该算法提出了一个有效的数据点密集参数，能够很好地对非均匀数据的低维嵌入进行约束，其嵌效结果明显优于LLE算法．在人工和人脸数据集上的实验结果表明，新算法产生了较好的嵌入及分类结果．
DEFF Research Database (Denmark)
Stolpe, Mathias; Bendsøe, Martin P.
2007-01-01
This paper present some initial results pertaining to a search for globally optimal solutions to a challenging benchmark example proposed by Zhou and Rozvany. This means that we are dealing with global optimization of the classical single load minimum compliance topology design problem with a fixed...... finite element discretization and with discrete design variables. Global optimality is achieved by the implementation of some specially constructed convergent nonlinear branch and cut methods, based on the use of natural relaxations and by applying strengthening constraints (linear valid inequalities...
Energy Technology Data Exchange (ETDEWEB)
Toselli, G. [ENEA, Centro Ricerche Ezio Clementel, Bologna, (Italy). Dipt. Innovazione; Mirco, A. M. [Bologna Univ., Bologna (Italy). Dipt. di Matematica
1999-07-01
In this technical report the thesis of doctor's degree in Mathematics of A.M. Mirco is reported; it has been developed at ENEA research centre 'E. Clementel' in Bologna (Italy) in the frame of a collaboration between the section MACO (Applied Physics Division - Innovation Department) of ENEA at Bologna and the Department of Mathematics of the mathematical, physical and natural sciences faculty of Bologna University. Substantially, studies and research work, developed in these last years at MACO section, are here presented; they have led to the development of a constitutive model, based on Hill potential theory, for the treatment, in plastic field, of metal material anisotropy induced by previous workings and to the construction of the corresponding FEM algorithm for the non-linear structural analysis NOSA, oriented in particular to the numerical simulation of metal forming. Subsequently, an algorithm extension (proper object of the thesis), which has given, beyond a more rigorous formalization, also significant improvements. [Italian] In questo rapporto tecnico viene riportata la tesi di laurea in matematica di A. M. Mirco, tesi svolta presso il centro ricerche E. Clementel dell'ENEA di Bologna nell'ambito di un accordo di collaborazione fra la sezione MACO (Divisione Fisica Applicata - Dipartimento di Innovazione) dell'ENEA di Bologna ed il Dipartimento di matematica della facolta' di scienze matematiche, fisiche e naturali dell'universita' degli studi di Bologna. Sostanzialmente, vengono presentati gli studi ed il lavoro di ricerca, svolti in questi ultimi anni presso la sezione MACO, che hanno portato allo sviluppo di un modello costitutivo, basato sulla teoria del potenziale di Hill, per il trattamento in campo plastico, dell'anisotropia indotta da lavorazioni precedenti per un materiale metallico ed alla costruzione del corrispondente algoritmo basato sul metodo degli elementi finiti per il codice di analisi
Scenarios for solving a non-linear transportation problem in multi-agent systems
DEFF Research Database (Denmark)
Brehm, Robert; Top, Søren; Mátéfi-Tempfli, Stefan
2017-01-01
We introduce and provide an evaluation on two scenarios and related algorithms for implementation of a multi-agent system to solve a type of non-linear transportation problem using distributed optimization algorithms based on dual decomposition and consensus. The underlying fundamental optimization...
Microgenetic optimization algorithm for optimal wavefront shaping
Anderson, Benjamin R; Gunawidjaja, Ray; Eilers, Hergen
2015-01-01
One of the main limitations of utilizing optimal wavefront shaping in imaging and authentication applications is the slow speed of the optimization algorithms currently being used. To address this problem we develop a micro-genetic optimization algorithm ($\\mu$GA) for optimal wavefront shaping. We test the abilities of the $\\mu$GA and make comparisons to previous algorithms (iterative and simple-genetic) by using each algorithm to optimize transmission through an opaque medium. From our experiments we find that the $\\mu$GA is faster than both the iterative and simple-genetic algorithms and that both genetic algorithms are more resistant to noise and sample decoherence than the iterative algorithm.
New Optimization Algorithms in Physics
Hartmann, Alexander K
2004-01-01
Many physicists are not aware of the fact that they can solve their problems by applying optimization algorithms. Since the number of such algorithms is steadily increasing, many new algorithms have not been presented comprehensively until now. This presentation of recently developed algorithms applied in physics, including demonstrations of how they work and related results, aims to encourage their application, and as such the algorithms selected cover concepts and methods from statistical physics to optimization problems emerging in theoretical computer science.
Energy Technology Data Exchange (ETDEWEB)
Delbos, F.
2004-11-01
Reflexion tomography allows the determination of a subsurface velocity model from the travel times of seismic waves. The introduction of a priori information in this inverse problem can lead to the resolution of a constrained non-linear least-squares problem. The goal of the thesis is to improve the resolution techniques of this optimization problem, whose main difficulties are its ill-conditioning, its large scale and an expensive cost function in terms of CPU time. Thanks to a detailed study of the problem and to numerous numerical experiments, we justify the use of a sequential quadratic programming method, in which the tangential quadratic programs are solved by an original augmented Lagrangian method. We show the global linear convergence of the latter. The efficiency and robustness of the approach are demonstrated on several synthetic examples and on two real data cases. (author)
Antenna optimization using Particle Swarm Optimization algorithm
Directory of Open Access Journals (Sweden)
Golubović Ružica M.
2006-01-01
Full Text Available We present the results for two different antenna optimization problems that are found using the Particle Swarm Optimization (PSO algorithm. The first problem is finding the maximal forward gain of a Yagi antenna. The second problem is finding the optimal feeding of a broadside antenna array. The optimization problems have 6 and 20 optimization variables, respectively. The preferred values of the parameters of the PSO algorithm are found for presented problems. The results show that the preferred parameters of PSO are somewhat different for optimization problems with different number of dimensions of the optimization space. The results that are found using the PSO algorithm are compared with the results that are found using other optimization algorithms, in order to estimate the efficiency of the PSO.
Optimal Mixing Evolutionary Algorithms
Thierens, D.; Bosman, P.A.N.; Krasnogor, N.
2011-01-01
A key search mechanism in Evolutionary Algorithms is the mixing or juxtaposing of partial solutions present in the parent solutions. In this paper we look at the efficiency of mixing in genetic algorithms (GAs) and estimation-of-distribution algorithms (EDAs). We compute the mixing probabilities of
DEFF Research Database (Denmark)
Asif, Rameez
2016-01-01
We have evaluated that in-line non-linear compensation schemes decrease the complexity of digital backward propagation and enhance the transmission performance of 40/112/224 Gbit/s mixed line rate network. Multiple bit rates, i.e. 40/112/224 Gbit/s and modulation formats (i.e. DP-QPSK and DP-16QA...
A non-linear UAV altitude PSO-PD control
Orlando, Calogero
2015-12-01
In this work, a nonlinear model based approach is presented for the altitude stabilization of a hexarotor unmanned aerial vehicle (UAV). The mathematical model and control of the hexacopter airframe is presented. To stabilize the system along the vertical direction, a Proportional Derivative (PD) control is taken into account. A particle swarm optimization (PSO) approach is used in this paper to select the optimal parameters of the control algorithm taking into account different objective functions. Simulation sets are performed to carry out the results for the non-linear system to show how the PSO tuned PD controller leads to zero the error of the position along Z earth direction.
Genetic algorithm optimization of entanglement
Navarro-Munoz, J C; Rosu, H C; Navarro-Munoz, Jorge C.
2006-01-01
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
Controller reconfiguration for non-linear systems
Kanev, S.; Verhaegen, M.
2000-01-01
This paper outlines an algorithm for controller reconfiguration for non-linear systems, based on a combination of a multiple model estimator and a generalized predictive controller. A set of models is constructed, each corresponding to a different operating condition of the system. The interacting m
Non-Linear Logging Parameters Inversion
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
The non-linear logging parameters inversion is based on the field theory, information optimization and predication theory. It uses seismic charaoters,geological model and logging data as a restriction to inverse 2D, 3D logging parameters data volume. Using this method,
Huttunen, Jani; Kokkola, Harri; Mielonen, Tero; Esa Juhani Mononen, Mika; Lipponen, Antti; Reunanen, Juha; Vilhelm Lindfors, Anders; Mikkonen, Santtu; Erkki Juhani Lehtinen, Kari; Kouremeti, Natalia; Bais, Alkiviadis; Niska, Harri; Arola, Antti
2016-07-01
In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during
Gravitation search algorithm: Application to the optimal IIR filter design
Directory of Open Access Journals (Sweden)
Suman Kumar Saha
2014-01-01
Full Text Available This paper presents a global heuristic search optimization technique known as Gravitation Search Algorithm (GSA for the design of 8th order Infinite Impulse Response (IIR, low pass (LP, high pass (HP, band pass (BP and band stop (BS filters considering various non-linear characteristics of the filter design problems. This paper also adopts a novel fitness function in order to improve the stop band attenuation to a great extent. In GSA, law of gravity and mass interactions among different particles are adopted for handling the non-linear IIR filter design optimization problem. In this optimization technique, searcher agents are the collection of masses and interactions among them are governed by the Newtonian gravity and the laws of motion. The performances of the GSA based IIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA and standard Particle Swarm Optimization (PSO. Extensive simulation results affirm that the proposed approach using GSA outperforms over its counterparts not only in terms of quality output, i.e., sharpness at cut-off, smaller pass band ripple, higher stop band attenuation, but also the fastest convergence speed with assured stability.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
Directory of Open Access Journals (Sweden)
Hongwei Li
2013-12-01
Full Text Available In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding and is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature limitation, as well as the corresponding network heat loss.
Combinatorial optimization theory and algorithms
Korte, Bernhard
2002-01-01
Combinatorial optimization is one of the youngest and most active areas of discrete mathematics, and is probably its driving force today. This book describes the most important ideas, theoretical results, and algorithms of this field. It is conceived as an advanced graduate text, and it can also be used as an up-to-date reference work for current research. The book includes the essential fundamentals of graph theory, linear and integer programming, and complexity theory. It covers classical topics in combinatorial optimization as well as very recent ones. The emphasis is on theoretical results and algorithms with provably good performance. Some applications and heuristics are mentioned, too.
Constrained Multiobjective Biogeography Optimization Algorithm
Directory of Open Access Journals (Sweden)
Hongwei Mo
2014-01-01
Full Text Available 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.
Constrained multiobjective biogeography optimization algorithm.
Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping
2014-01-01
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.
Lmfit: Non-Linear Least-Square Minimization and Curve-Fitting for Python
Newville, Matthew; Stensitzki, Till; Allen, Daniel B.; Rawlik, Michal; Ingargiola, Antonino; Nelson, Andrew
2016-06-01
Lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. Lmfit builds on and extends many of the optimization algorithm of scipy.optimize, especially the Levenberg-Marquardt method from optimize.leastsq. Its enhancements to optimization and data fitting problems include using Parameter objects instead of plain floats as variables, the ability to easily change fitting algorithms, and improved estimation of confidence intervals and curve-fitting with the Model class. Lmfit includes many pre-built models for common lineshapes.
Institute of Scientific and Technical Information of China (English)
刘灿昌; 李红艳
2011-01-01
提出非线性的分阶最优控制策略,并将其应用于悬臂梁非线性振动的压电减振控制.建立悬臂梁非线性压电减振系统动力学模型,导出减振系统的非线性动力学运动微分方程.将梁振动挠度和压电驱动器的控制电压同时展开为小参数形式,利用摄动法实现非线性压电控制微分方程的线性化.通过空间解耦,得到状态空间方程.设计非线性分阶控制器,对该减振系统进行分阶最优控制.%A non-linear grade optimal control scheme is proposed and used in the piezoelectric vibration reduction control of non-linear cantilever beam. The dynamic model of non-linear vibration reduction beam with piezoelectric damper is built. The non-linear dynamic differential equations of the vibration reduction system are derived. The vibrational deflection and the piezoelectricity driver control voltage of beam are expanded as the small parametric form. The differential equation is linearized into a set of linear equations by means of perturbation. The state space equations are obtained by decoupling in the space coordinates. The vibration reduction system is controlled by the non-linear grade controllers.
Genetic Algorithm for Optimization: Preprocessor and Algorithm
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
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.
Directory of Open Access Journals (Sweden)
Diana M. Ortiz
2011-01-01
Full Text Available Estrategias de evolución es una técnica bio-inspirada, eficiente y robusta para resolver problemas de optimización donde el espacio de soluciones es no restringido. Sin embargo, esta suposición es irreal en muchos casos porque el espacio de soluciones es limitado por fronteras complejas en la forma de restricciones tanto lineales como no lineales. En este artículo de investigación, se propone una modificación al algoritmo original de estrategias de evolución para optimizar problemas donde el espacio de soluciones es limitado usando restricciones complejas. El método propuesto es basado en el uso de una función de penalización la cual es cero dentro de la región factible, e igual al máximo valor dentro de la región factible cuando se considera un punto que es no factible. La aproximación propuesta es probada usando seis problemas de prueba bien conocidos. En todos los casos, esta aproximación encontró un punto óptimo igual o menor que los valores reportados en la literatura.Evolution Strategies is a bio-inspired, robust, and efficient technique for solving optimization problems where the solution space is unrestricted. However, this assumption is unreal in many cases because the solution space is limited by complex boundaries in the form of linear and non-linear restrictions. In this paper, a modification of the original algorithm of Evolution Strategies for optimizing problems where the solution space is bounded using complex restrictions is proposed. The proposed method is based on the use of a penalization function which is zero inside of the feasible region and equal to the maximum value inside of the feasible region when an unfeasible point is considered. The proposed approach is proved using six benchmark problems. In all cases, our approach found an optimal point equal or lower than the values reported in the literature.
Noorizadeh, H; Sobhan Ardakani, S; Ahmadi, T; Mortazavi, S S; Noorizadeh, M
2013-02-01
Genetic algorithm (GA) and partial least squares (PLS) and kernel PLS (KPLS) techniques were used to investigate the correlation between immobilized liposome chromatography partitioning (log Ks) and descriptors for 65 drug compounds. The models were validated using leave-group-out cross validation LGO-CV. The results indicate that GA-KPLS can be used as an alternative modelling tool for quantitative structure-property relationship (QSPR) studies.
Fast Programming Algorithm to Find Non-Linear Feedback Shift Register%快速寻找非线性反馈移位寄存器的编程算法
Institute of Scientific and Technical Information of China (English)
叶炜晨; 陈克非
2014-01-01
Non-linear feedback shift register (NLFSR) is a common device to generate pseudo-random sequences in stream cipher. However, there is still no effective mathematical algorithm to find NLFSRs for a given period or output sequence. This paper provides a quick method to find NLFSRs. This method is a programming algorithm based on compute unified device architecture (CUDA) and parallel computing, and can quickly find NLFSRs for the given period or output sequence. This method has very good performance on both simple and complex NLFSRs. With this new method, people can easily get a large amount of experimental data about NLFSRs. It will be a great help for the future research on the mathematical algorithm to find NLFSRs.%在流密码中，非线性反馈移位寄存器（non-linear feedback shift register，NLFSR）是一种常用的安全性较高的伪随机序列生成器。目前仍然没有一种普遍有效的数学算法，能够根据给定的序列或者序列周期，直接推导出NLFSR。提出了一种快速寻找NLFSR的编程算法。该算法基于统一计算架构（compute unified device architecture，CUDA）和并行计算来实现，计算速度快，尤其适用于处理高次数的复杂NLFSR。并且该算法可以快速大规模地计算出NLFSR，为未来研究寻找NLFSR的数学算法提供了大量的实验数据。
Optimization of LMS Algorithm for System Identification
Prasad, Saurabh R.; Godbole, Bhalchandra B.
2017-01-01
An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical scenario. So most feasible choice of the adaptive filtering algorithm is the LMS algorithm includin...
DEFF Research Database (Denmark)
Andersen, Steffen; Harrison, Glenn W.; Hole, Arne Risa
2012-01-01
We develop an extension of the familiar linear mixed logit model to allow for the direct estimation of parametric non-linear functions defined over structural parameters. Classic applications include the estimation of coefficients of utility functions to characterize risk attitudes and discountin...
Iterated non-linear model predictive control based on tubes and contractive constraints.
Murillo, M; Sánchez, G; Giovanini, L
2016-05-01
This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.
NEW HMM ALGORITHM FOR TOPOLOGY OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Zuo Kongtian; Zhao Yudong; Chen Liping; Zhong Yifang; Huang Yuying
2005-01-01
A new hybrid MMA-MGCMMA (HMM) algorithm for solving topology optimization problems is presented. This algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value existing in the calculation. This algorithm can improve calculation efficiency and accelerate convergence compared with simplex MMA or MGCMMA algorithms, which is proven with an example.
Directory of Open Access Journals (Sweden)
Mohammad Reza Shahriari
2016-12-01
Full Text Available In this paper, we present a non-linear binary programing for optimizing a specific cost in cellular manufacturing system in a controlled production condition. The system parameters are determined by the continuous distribution functions. The aim of the presented model is to optimize the total cost of imposed sub-contractors to the manufacturing system by determining how to allocate the machines and parts to each seller. In this system, DM could control the occupation level of each machine in the system. For solving the presented model, we used the electromagnetic meta-heuristic algorithm and Taguchi method for determining the optimal algorithm parameters.
Applications of Kalman filters based on non-linear functions to numerical weather predictions
Directory of Open Access Journals (Sweden)
G. Galanis
2006-10-01
Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.
APPLICATION OF GENETIC ALGORITHMS FOR ROBUST PARAMETER OPTIMIZATION
Directory of Open Access Journals (Sweden)
N. Belavendram
2010-12-01
Full Text Available Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own specific phenotypic trait expressed as a ‘genetic chromosome’. Different genes enable individuals with different fitness levels to reproduce according to natural reproductive gene theory. This reproduction is established in terms of selection, crossover and mutation of reproducing genes. The resulting child generation of individuals has a better fitness level akin to natural selection, namely evolution. Populations evolve towards the fittest individuals. Such a mechanism has a parallel application in parameter optimization. Factors in a parameter design can be expressed as a genetic analogue in a pool of sub-optimal random solutions. Allowing this pool of sub-optimal solutions to evolve over several generations produces fitter generations converging to a pre-defined engineering optimum. In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. A comparison of the full factorial design against a GA method shows that the GA method is about 1200 times faster in finding a comparable solution.
DEFF Research Database (Denmark)
Du, Yigang
without iteration steps. The ASA is implemented in combination with Field II and extended to simulate the pulsed ultrasound fields. The simulated results from a linear array transducer are made by the ASA based on Field II, and by a released non-linear simulation program- Abersim, respectively....... The calculation speed of the ASA is increased approximately by a factor of 140. For the second harmonic point spread function the error of the full width is 1.5% at -6 dB and 6.4% at -12 dB compared to Abersim. To further investigate the linear and non-linear ultrasound fields, hydrophone measurements.......3% relative to the measurement from a 1 inch diameter transducer. A preliminary study for harmonic imaging using synthetic aperture sequential beamforming (SASB) has been demonstrated. A wire phantom underwater measurement is made by an experimental synthetic aperture real-time ultrasound scanner (SARUS...
Algorithms for optimizing drug therapy
Directory of Open Access Journals (Sweden)
Martin Lene
2004-07-01
Full Text Available Abstract Background Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet. Methods One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease- and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface and JavaScript (program logic. Results Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs
Directory of Open Access Journals (Sweden)
Abbas v Y.A. Bayati
2010-01-01
Full Text Available Problem statement: The scaled hybrid Conjugate Gradient (CG algorithm which usually used for solving non-linear functions was presented and was compared with two standard well-Known NAG routines, yielding a new fast comparable algorithm. Approach: We proposed, a new hybrid technique based on the combination of two well-known scaled (CG formulas for the quadratic model in unconstrained optimization using exact line searches. A global convergence result for the new technique was proved, when the Wolfe line search conditions were used. Results: Computational results, for a set consisting of 1915 combinations of (unconstrained optimization test problems/dimensions were implemented in this research making a comparison between the new proposed algorithm and the other two similar algorithms in this field. Conclusion: Our numerical results showed that this new scaled hybrid CG-algorithm substantially outperforms Andrei-sufficient descent condition (CGSD algorithm and the well-known Andrei standard sufficient descent condition from (ACGA algorithm.
Fire Evacuation using Ant Colony Optimization Algorithm
National Research Council Canada - National Science Library
Kanika Singhal; Shashank Sahu
2016-01-01
... planning.The objective of the algorithm is to minimizes the entire rescue time of all evacuees.The ant colony optimization algorithm is used to solve the complications of shortest route planning. Presented paper gives a comparative overview of various emergency scenarios using ant colony optimization algorithm.
Non-linear effects in transition edge sensors for X-ray detection
Energy Technology Data Exchange (ETDEWEB)
Bandler, S.R. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States)]. E-mail: sbandler@milkyway.gsfc.nasa.gov; Figueroa-Feliciano, E. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Iyomoto, N. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Kelley, R.L. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Kilbourne, C.A. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Murphy, K.D. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Porter, F.S. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Saab, T. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Sadleir, J. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States)
2006-04-15
In a microcalorimeter that uses a transition-edge sensor to detect energy depositions, the small signal energy resolution improves with decreasing heat capacity. This improvement remains true up to the point where non-linear and saturation effects become significant. This happens when the energy deposition causes a significant change in the sensor resistance. Not only does the signal size become a non-linear function of the energy deposited, but also the noise becomes non-stationary over the duration of the pulse. Algorithms have been developed that can calculate the optimal performance given this non-linear behavior that typically requires significant processing and calibration work-both of which are impractical for space missions. We have investigated the relative importance of the various non-linear effects, with the hope that a computationally simple transformation can overcome the largest of the non-linear and non-stationary effects, producing a highly linear 'gain' for pulse-height versus energy, and close to the best energy resolution at all energies when using a Wiener filter.
Optimal Multistage Algorithm for Adjoint Computation
Energy Technology Data Exchange (ETDEWEB)
Aupy, Guillaume; Herrmann, Julien; Hovland, Paul; Robert, Yves
2016-01-01
We reexamine the work of Stumm and Walther on multistage algorithms for adjoint computation. We provide an optimal algorithm for this problem when there are two levels of checkpoints, in memory and on disk. Previously, optimal algorithms for adjoint computations were known only for a single level of checkpoints with no writing and reading costs; a well-known example is the binomial checkpointing algorithm of Griewank and Walther. Stumm and Walther extended that binomial checkpointing algorithm to the case of two levels of checkpoints, but they did not provide any optimality results. We bridge the gap by designing the first optimal algorithm in this context. We experimentally compare our optimal algorithm with that of Stumm and Walther to assess the difference in performance.
Modelling Loudspeaker Non-Linearities
DEFF Research Database (Denmark)
Agerkvist, Finn T.
2007-01-01
This paper investigates different techniques for modelling the non-linear parameters of the electrodynamic loudspeaker. The methods are tested not only for their accuracy within the range of original data, but also for the ability to work reasonable outside that range, and it is demonstrated...... that polynomial expansions are rather poor at this, whereas an inverse polynomial expansion or localized fitting functions such as the gaussian are better suited for modelling the Bl-factor and compliance. For the inductance the sigmoid function is shown to give very good results. Finally the time varying...
Genetic algorithms and fuzzy multiobjective optimization
Sakawa, Masatoshi
2002-01-01
Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...
Institute of Scientific and Technical Information of China (English)
赵丽; 刘旭波; 涂海宁; 王运宵
2012-01-01
According to the characteristics of tire mold character, select the cylindrical section method as the processing path planning algorithms. The application of cylindrical section algorithm is analyzed in detail. The verification results show that the cylindrical cross-section method for the tire mold's non-linear character processing have some advantages such as the simpler calculation, more concise processing program, higher precision. The cylindrical cross-section method has good adaptability to the processing of the parts like tire mold.%根据轮胎模具文字特点,选择圆柱截面法作为轮胎模具非线性文字加工的路径规划算法,详细分析了圆柱截面法的算法过程.验证结果表明,圆柱截面法加工轮胎表面文字具有计算更简单、工件加工程序更简洁及精度更高等优点.对轮胎模具这种类型的工件加工来说,圆柱截面法具有很好的工程适应性.
Metaheuristic Optimization: Algorithm Analysis and Open Problems
2012-01-01
Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majority of metaheuristic algorithms, while efficiency analysis is equally challenging. In this paper, we i...
Directory of Open Access Journals (Sweden)
Ahmet Demir
2017-01-01
Full Text Available In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, intelligent optimization techniques based on Artificial Intelligence are widely used for optimization problems. The objective of this paper is to provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions for enabling readers to have idea about the potential of intelligent optimization techniques. At this point, two recently developed intelligent optimization algorithms, Vortex Optimization Algorithm (VOA and Cognitive Development Optimization Algorithm (CoDOA, have been used to solve some multidisciplinary optimization problems provided in the source book Thomas' Calculus 11th Edition and the obtained results have compared with classical optimization solutions.
Optimal Reactive Power Dispatch using Improved Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Hamid Falaghi
2014-12-01
Full Text Available Reactive power dispatch plays a key role in secure and economic operation of power systems. Optimal reactive power dispatch (ORPD is a non-linear optimization problem which includes both continues and discrete variables. Due to complex characteristics, heuristic and evolutionary based optimization approaches have become effective tools to solve the ORPD problem. In this paper, a new optimization approach based on improved differential evolution (IDE has been proposed to solve the ORPD problem. IDE is an improved version of differential evolution optimization algorithm in which new solutions are produced in respect to global best solution. In the proposed approach, IDE determines the optimal combination of control variables including generator voltages, transformer taps and setting of VAR compensation devices to obtain minimum real power losses. In order to demonstrate the applicability and efficiency of the proposed IDE based approach, it has been tested on the IEEE 14 and 57-bus test systems and obtained results are compared with those obtained using other existing methods. Simulation results show that the proposed approach is superior to the other existing methods.
Energy Technology Data Exchange (ETDEWEB)
Chavarette, Fabio Roberto [Department of Mechanical Design, State University of Campinas, 13083-970 Campinas, SP (Brazil); State University of Sao Paulo at Rio Claro, C.P. 178, 13500-230 Rio Claro, SP (Brazil); Balthazar, Jose Manoel [Department of Mechanical Design, State University of Campinas, 13083-970 Campinas, SP (Brazil); State University of Sao Paulo at Rio Claro, C.P. 178, 13500-230 Rio Claro, SP (Brazil)], E-mail: jmbaltha@rc.unesp.br; Rafikov, Marat [Universidade Regional do Noroeste do Estado do Rio Grande do Sul 98700-000, C.P. 560, Ijui, RS (Brazil); Hermini, Helder Anibal [Department of Mechanical Design, State University of Campinas, 13083-970 Campinas, SP (Brazil)
2009-02-28
In this paper, we have studied the plasmatic membrane behavior using an electric circuit developed by Hodgkin and Huxley in 1952 and have dealt with the variation of the amount of time related to the potassium and sodium conductances in the squid axon. They developed differential equations for the propagation of electric signals; the dynamics of the Hodgkin-Huxley model have been extensively studied both from the view point of its their biological implications and as a test bed for numerical methods, which can be applied to more complex models. Recently, an irregular chaotic movement of the action potential of the membrane was observed for a number of techniques of control with the objective to stabilize the variation of this potential. This paper analyzes the non-linear dynamics of the Hodgkin-Huxley mathematical model, and we present some modifications in the governing equations of the system in order to make it a non-ideal one (taking into account that the energy source has a limited power supply). We also developed an optimal linear control design for the action potential of membranes. Here, we discuss the conditions that allow the use of control linear feedback for this kind of non-linear system.
Function Optimization Based on Quantum Genetic Algorithm
Ying Sun; Hegen Xiong
2014-01-01
Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded c...
Parallel algorithms for unconstrained optimizations by multisplitting
Energy Technology Data Exchange (ETDEWEB)
He, Qing [Arizona State Univ., Tempe, AZ (United States)
1994-12-31
In this paper a new parallel iterative algorithm for unconstrained optimization using the idea of multisplitting is proposed. This algorithm uses the existing sequential algorithms without any parallelization. Some convergence and numerical results for this algorithm are presented. The experiments are performed on an Intel iPSC/860 Hyper Cube with 64 nodes. It is interesting that the sequential implementation on one node shows that if the problem is split properly, the algorithm converges much faster than one without splitting.
Memetic firefly algorithm for combinatorial optimization
Fister, Iztok; Fister, Iztok; Brest, Janez
2012-01-01
Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.
Simulated annealing algorithm for optimal capital growth
Luo, Yong; Zhu, Bo; Tang, Yong
2014-08-01
We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.
OPTIMIZED STRAPDOWN CONING CORRECTION ALGORITHM
Institute of Scientific and Technical Information of China (English)
黄磊; 刘建业; 曾庆化
2013-01-01
Traditional coning algorithms are based on the first-order coning correction reference model .Usually they reduce the algorithm error of coning axis (z) by increasing the sample numbers in one iteration interval .But the increase of sample numbers requires the faster output rates of sensors .Therefore ,the algorithms are often lim-ited in practical use .Moreover ,the noncommutivity error of rotation usually exists on all three axes and the in-crease of sample numbers has little positive effect on reducing the algorithm errors of orthogonal axes (x ,y) . Considering the errors of orthogonal axes cannot be neglected in the high-precision applications ,a coning algorithm with an additional second-order coning correction term is developed to further improve the performance of coning algorithm .Compared with the traditional algorithms ,the new second-order coning algorithm can effectively reduce the algorithm error without increasing the sample numbers .Theoretical analyses validate that in a coning environ-ment with low frequency ,the new algorithm has the better performance than the traditional time-series and fre-quency-series coning algorithms ,while in a maneuver environment the new algorithm has the same order accuracy as the traditional time-series and frequency-series algorithms .Finally ,the practical feasibility of the new coning al-gorithm is demonstrated by digital simulations and practical turntable tests .
Efficiency Improvements in Meta-Heuristic Algorithms to Solve the Optimal Power Flow Problem
Reddy, S. Surender; Bijwe, P. R.
2016-12-01
This paper proposes the efficient approaches for solving the Optimal Power Flow (OPF) problem using the meta-heuristic algorithms. Mathematically, OPF is formulated as non-linear equality and inequality constrained optimization problem. The main drawback of meta-heuristic algorithm based OPF is the excessive execution time required due to the large number of power flows needed in the solution process. The proposed efficient approaches uses the lower and upper bounds of objective function values. By using this approach, the number of power flows to be performed are reduced substantially, resulting in the solution speed up. The efficiently generated objective function bounds can result in the faster solutions of meta-heuristic algorithms. The original advantages of meta-heuristic algorithms, such as ability to handle complex non-linearities, discontinuities in the objective function, discrete variables handling, and multi-objective optimization, etc., are still available in the proposed efficient approaches. The proposed OPF formulation includes the active and reactive power generation limits, Valve Point Loading (VPL) and Prohibited Operating Zones (POZs) effects of generating units. The effectiveness of proposed approach is examined on IEEE 30, 118 and 300 bus test systems, and the simulation results confirm the efficiency and superiority of the proposed approaches over the other meta-heuristic algorithms. The proposed efficient approach is generic enough to use with any type of meta-heuristic algorithm based OPF.
Galvan-Sosa, M.; Portilla, J.; Hernandez-Rueda, J.; Siegel, J.; Moreno, L.; Ruiz de la Cruz, A.; Solis, J.
2014-02-01
Femtosecond laser pulse temporal shaping techniques have led to important advances in different research fields like photochemistry, laser physics, non-linear optics, biology, or materials processing. This success is partly related to the use of optimal control algorithms. Due to the high dimensionality of the solution and control spaces, evolutionary algorithms are extensively applied and, among them, genetic ones have reached the status of a standard adaptive strategy. Still, their use is normally accompanied by a reduction of the problem complexity by different modalities of parameterization of the spectral phase. Exploiting Rabitz and co-authors' ideas about the topology of quantum landscapes, in this work we analyze the optimization of two different problems under a deterministic approach, using a multiple one-dimensional search (MODS) algorithm. In the first case we explore the determination of the optimal phase mask required for generating arbitrary temporal pulse shapes and compare the performance of the MODS algorithm to the standard iterative Gerchberg-Saxton algorithm. Based on the good performance achieved, the same method has been applied for optimizing two-photon absorption starting from temporally broadened laser pulses, or from laser pulses temporally and spectrally distorted by non-linear absorption in air, obtaining similarly good results which confirm the validity of the deterministic search approach.
Institute of Scientific and Technical Information of China (English)
芦会彬; 薄翠梅; 杨世品
2015-01-01
In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing (ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.
Optimized QoS Routing Algorithm
Institute of Scientific and Technical Information of China (English)
石明洪; 王思兵; 白英彩
2004-01-01
QoS routing is one of the key technologies for providing guaranteed service in IP networks. The paper focuses on the optimization problem for bandwidth constrained QoS routing, and proposes an optimal algorithm based on the global optimization of path bandwidth and hop counts. The main goal of the algorithm is to minimize the consumption of network resource, and at the same time to minimize the network congestion caused by irrational path selection. The simulation results show that our algorithm has lower call blocking rate and higher throughput than traditional algorithms.
Tetris Agent Optimization Using Harmony Search Algorithm
Directory of Open Access Journals (Sweden)
Victor II M. Romero
2011-01-01
Full Text Available Harmony Search (HS algorithm, a relatively recent meta-heuristic optimization algorithm based on the music improvisation process of musicians, is applied to one of today's most appealing problems in the field of Computer Science, Tetris. Harmony Search algorithm was used as the underlying optimization algorithm to facilitate the learning process of an intelligent agent whose objective is to play the game of Tetris in the most optimal way possible, that is, to clear as many rows as possible. The application of Harmony Search algorithm to Tetris is a good illustration of the involvement of optimization process to decision-making problems. Experiment results show that Harmony Search algorithm found the best possible solution for the problem at hand given a random sequence of Tetrominos.
An Efficient Algorithm for Unconstrained Optimization
Directory of Open Access Journals (Sweden)
Sergio Gerardo de-los-Cobos-Silva
2015-01-01
Full Text Available This paper presents an original and efficient PSO algorithm, which is divided into three phases: (1 stabilization, (2 breadth-first search, and (3 depth-first search. The proposed algorithm, called PSO-3P, was tested with 47 benchmark continuous unconstrained optimization problems, on a total of 82 instances. The numerical results show that the proposed algorithm is able to reach the global optimum. This work mainly focuses on unconstrained optimization problems from 2 to 1,000 variables.
de Jong, Roelof
2005-07-01
This program incorporates a number of tests to analyse the count rate dependent non-linearity seen in NICMOS spectro-photometric observations. In visit 1 we will observe a few fields with stars of a range in luminosity in NGC1850 with NICMOS in NIC1 in F090M, F110W and F160W and NIC2 F110W, F160W, and F180W. We will repeat the observations with flatfield lamp on, creating artificially high count-rates, allowing tests of NICMOS linearity as function of count rate. To access the effect of charge trapping and persistence, we first take darks {so there is not too much charge already trapped}, than take exposures with the lamp off, exposures with the lamp on, and repeat at the end with lamp off. Finally, we continue with taking darks during occultation. In visit 2 we will observe spectro-photometric standard P041C using the G096 and G141 grisms in NIC3, and repeat the lamp off/on/off test to artificially create a high background. In visits 3&4 we repeat photometry measurements of faint standard stars SNAP-2 and WD1657+343, on which the NICMOS non-linearity was originally discovered using grism observations. These measurements are repeated, because previous photometry was obtained with too short exposure times, hence substantially affected by charge trapping non-linearity. Measurements will be made with NIC1: Visit 5 forms the persistence test of the program. The bright star GL-390 {used in a previous persistence test} will iluminate the 3 NICMOS detectors in turn for a fixed time, saturating the center many times, after which a series of darks will be taken to measure the persistence {i.e. trapped electrons and the decay time of the traps}. To determine the wavelength dependence of the trap chance, exposures of the bright star in different filters will be taken, as well as one in the G096 grism with NIC3. Most exposures will be 128s long, but two exposures in the 3rd orbit will be 3x longer, to seperate the effects of count rate versus total counts of the trap
Optimal Fungal Space Searching Algorithms.
Asenova, Elitsa; Lin, Hsin-Yu; Fu, Eileen; Nicolau, Dan V; Nicolau, Dan V
2016-10-01
Previous experiments have shown that fungi use an efficient natural algorithm for searching the space available for their growth in micro-confined networks, e.g., mazes. This natural "master" algorithm, which comprises two "slave" sub-algorithms, i.e., collision-induced branching and directional memory, has been shown to be more efficient than alternatives, with one, or the other, or both sub-algorithms turned off. In contrast, the present contribution compares the performance of the fungal natural algorithm against several standard artificial homologues. It was found that the space-searching fungal algorithm consistently outperforms uninformed algorithms, such as Depth-First-Search (DFS). Furthermore, while the natural algorithm is inferior to informed ones, such as A*, this under-performance does not importantly increase with the increase of the size of the maze. These findings suggest that a systematic effort of harvesting the natural space searching algorithms used by microorganisms is warranted and possibly overdue. These natural algorithms, if efficient, can be reverse-engineered for graph and tree search strategies.
Drilling Path Optimization Based on Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Guangyu; ZHANG Weibo; DU Yuexiang
2006-01-01
This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, based on the mathematical algorithm model, the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution. And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator, the improved operator can satisfy the need of integer coding in drilling path optimization. The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize, fast convergence speed, and better global convergence characteristics, hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.
An Algorithmic Framework for Multiobjective Optimization
Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.
2013-01-01
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
An algorithmic framework for multiobjective optimization.
Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P
2013-01-01
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.
An Algorithmic Framework for Multiobjective Optimization
Directory of Open Access Journals (Sweden)
T. Ganesan
2013-01-01
Full Text Available 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.
An optimization algorithm of collaborative indoor locating
Directory of Open Access Journals (Sweden)
SHI Ying
2014-06-01
Full Text Available Based on triangular centroid locating algorithm,this paper will use the idea of collaboration to indoor locating system.On account of the condition which has two nodes to be located in the test environment,we have designed a circular type optimization algorithm.Verified simulation results show that the circular type optimization algorithm,compared with the triangular centroid locating algorithm,can decrease the average error by 11.62%,decrease the maximum error by 7.74% and decrease the minimum error by 22.66%.The maximum value of the optimize degree of the circular type optimization algorithm is 28.63%,and the minimum value of that is 0.05%.
An Optimal Online Algorithm for Halfplane Intersection
Institute of Scientific and Technical Information of China (English)
WU Jigang; JI Yongchang; CHEN Guoliang
2000-01-01
The intersection of N halfplanes is a basic problem in computational geometry and computer graphics. The optimal offiine algorithm for this problem runs in time O(N log N). In this paper, an optimal online algorithm which runs also in time O(N log N) for this problem is presented. The main idea of the algorithm is to give a new definition for the left side of a given line, to assign the order for the points of a convex polygon, and then to use binary search method in an ordered vertex set. The data structure used in the algorithm is no more complex than array.
Distributed Algorithms for Time Optimal Reachability Analysis
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
. We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general....
Optimizing neural network forecast by immune algorithm
Institute of Scientific and Technical Information of China (English)
YANG Shu-xia; LI Xiang; LI Ning; YANG Shang-dong
2006-01-01
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.
A Novel Particle Swarm Optimization Algorithm for Global Optimization.
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.
Chemical optimization algorithm for fuzzy controller design
Astudillo, Leslie; Castillo, Oscar
2014-01-01
In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application
Efficient heuristic algorithm used for optimal capacitor placement in distribution systems
Energy Technology Data Exchange (ETDEWEB)
Segura, Silvio; Rider, Marcos J. [Department of Electric Energy Systems, University of Campinas, Campinas, Sao Paulo (Brazil); Romero, Ruben [Faculty of Engineering of Ilha Solteira, Paulista State University, Ilha Solteira, Sao Paulo (Brazil)
2010-01-15
An efficient heuristic algorithm is presented in this work in order to solve the optimal capacitor placement problem in radial distribution systems. The proposal uses the solution from the mathematical model after relaxing the integrality of the discrete variables as a strategy to identify the most attractive bus to add capacitors to each step of the heuristic algorithm. The relaxed mathematical model is a non-linear programming problem and is solved using a specialized interior point method. The algorithm still incorporates an additional strategy of local search that enables the finding of a group of quality solutions after small alterations in the optimization strategy. Proposed solution methodology has been implemented and tested in known electric systems getting a satisfactory outcome compared with metaheuristic methods. The tests carried out in electric systems known in specialized literature reveal the satisfactory outcome of the proposed algorithm compared with metaheuristic methods. (author)
Spaceborne SAR Imaging Algorithm for Coherence Optimized.
Directory of Open Access Journals (Sweden)
Zhiwei Qiu
Full Text Available This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR research and application.
Algorithmic Differentiation for Calculus-based Optimization
Walther, Andrea
2010-10-01
For numerous applications, the computation and provision of exact derivative information plays an important role for optimizing the considered system but quite often also for its simulation. This presentation introduces the technique of Algorithmic Differentiation (AD), a method to compute derivatives of arbitrary order within working precision. Quite often an additional structure exploitation is indispensable for a successful coupling of these derivatives with state-of-the-art optimization algorithms. The talk will discuss two important situations where the problem-inherent structure allows a calculus-based optimization. Examples from aerodynamics and nano optics illustrate these advanced optimization approaches.
Distributed Algorithms for Time Optimal Reachability Analysis
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
Time optimal reachability analysis is a novel model based technique for solving scheduling and planning problems. After modeling them as reachability problems using timed automata, a real-time model checker can compute the fastest trace to the goal states which constitutes a time optimal schedule....... We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general....
Schrodinger Equation As a General Optimization Algorithm
Huang, Xiaofei
2009-01-01
One of the greatest scientific achievements of physics in the 20th century is the discovery of quantum mechanics. The Schrodinger equation is the most fundamental equation in quantum mechanics describing the time-based evolution of the quantum state of a physical system. It has been found that the time-independent version of the equation can be derived from a general optimization algorithm. Instead of arguing for a new interpretation and possible deeper principle for quantum mechanics, this paper elaborates a few points of the equation as a general global optimization algorithm. Benchmarked against randomly generated hard optimization problems, this paper shows that the algorithm significantly outperformed a classic local optimization algorithm. The former found a solution in one second with a single trial better than the best one found by the latter around one hour after one hundred thousand trials.
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
Heuristic Algorithm in Optimal Discrete Structural Designs
Directory of Open Access Journals (Sweden)
Alongkorn Lamom
2008-01-01
Full Text Available This study proposes a Heuristic Algorithm for Material Size Selection (HAMSS. It is developed to handle discrete structural optimization problems. The proposed algorithm (HAMSS, Simulated Annealing Algorithm (SA and the conventional design algorithm obtained from a structural steel design software are studied with three selected examples. The HAMSS, in fact, is the adaptation from the traditional SA. Although the SA is one of the easiest optimization algorithms available, a huge number of function evaluations deter its use in structural optimizations. To obtain the optimum answers by the SA, possible answers are first generated randomly. Many of these possible answers are rejected because they do not pass the constraints. To effectively handle this problem, the behavior of optimal structural design problems is incorporated into the algorithm. The new proposed algorithm is called the HAMSS. The efficiency comparison between the SA and the HAMSS is illustrated in term of number of finite element analysis cycles. Results from the study show that HAMSS can significantly reduce the number of structural analysis cycles while the optimized efficiency is not different.
Global Optimality of the Successive Maxbet Algorithm.
Hanafi, Mohamed; ten Berge, Jos M. F.
2003-01-01
It is known that the Maxbet algorithm, which is an alternative to the method of generalized canonical correlation analysis and Procrustes analysis, may converge to local maxima. Discusses an eigenvalue criterion that is sufficient, but not necessary, for global optimality of the successive Maxbet algorithm. (SLD)
DYNAMIC LABELING BASED FPGA DELAY OPTIMIZATION ALGORITHM
Institute of Scientific and Technical Information of China (English)
吕宗伟; 林争辉; 张镭
2001-01-01
DAG-MAP is an FPGA technology mapping algorithm for delay optimization and the labeling phase is the algorithm's kernel. This paper studied the labeling phase and presented an improved labeling method. It is shown through the experimental results on MCNC benchmarks that the improved method is more effective than the original method while the computation time is almost the same.
Neural Generalized Predictive Control of a non-linear Process
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1998-01-01
qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...
Belief Propagation Algorithm for Portfolio Optimization Problems.
Shinzato, Takashi; Yasuda, Muneki
2015-01-01
The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.
Belief Propagation Algorithm for Portfolio Optimization Problems.
Directory of Open Access Journals (Sweden)
Takashi Shinzato
Full Text Available The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.
Institute of Scientific and Technical Information of China (English)
Hasan ABBASI NOZARI; Hamed DEHGHAN BANADAKI; Mohammad MOKHTARE; Somaveh HEKMATI VAHED
2012-01-01
This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system.A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT),which is an incremental tree-based learning algorithm.The proposed NF models are compared with other known intelligent identifiers,namely multilayer perceptron (MLP) and radial basis function (RBF).Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system.Experimental results show the effectiveness of our proposed NF modelling approach.
Simulation of non-linear ultrasound fields
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Fox, Paul D.; Wilhjelm, Jens E.
2002-01-01
An approach for simulating non-linear ultrasound imaging using Field II has been implemented using the operator splitting approach, where diffraction, attenuation, and non-linear propagation can be handled individually. The method uses the Earnshaw/Poisson solution to Burgcrs' equation for the non......-linear ultrasound imaging in 3D using filters or pulse inversion for any kind of transducer, focusing, apodization, pulse emission and scattering phantom. This is done by first simulating the non-linear emitted field and assuming that the scattered field is weak and linear. The received signal is then the spatial...
Asynchronous Parallel Evolutionary Algorithms for Constrained Optimizations
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function op-timization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation,and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages ,such as the sim-plicity of its structure ,the higher accuracy of its results, the wide range of its applications ,and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous paral-lel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm.
Wolf Pack Algorithm for Unconstrained Global Optimization
Directory of Open Access Journals (Sweden)
Hu-Sheng Wu
2014-01-01
Full Text Available The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA. Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.
Algorithms for optimal dyadic decision trees
Energy Technology Data Exchange (ETDEWEB)
Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory
2009-01-01
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.
An algorithm for online optimization of accelerators
Energy Technology Data Exchange (ETDEWEB)
Huang, Xiaobiao [SLAC National Accelerator Lab., Menlo Park, CA (United States); Corbett, Jeff [SLAC National Accelerator Lab., Menlo Park, CA (United States); Safranek, James [SLAC National Accelerator Lab., Menlo Park, CA (United States); Wu, Juhao [SLAC National Accelerator Lab., Menlo Park, CA (United States)
2013-10-01
We developed a general algorithm 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. Moreover, 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.
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use of global optimisation algorithms to solve optimal control problems, wh
Heterogeneous architecture to process swarm optimization algorithms
Directory of Open Access Journals (Sweden)
Maria A. Dávila-Guzmán
2014-01-01
Full Text Available Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution.
Glowworm swarm optimization theory, algorithms, and applications
Kaipa, Krishnanand N
2017-01-01
This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intellige...
Space mapping optimization algorithms for engineering design
DEFF Research Database (Denmark)
Koziel, Slawomir; Bandler, John W.; Madsen, Kaj
2006-01-01
A simple, efficient optimization algorithm based on space mapping (SM) is presented. It utilizes input SM to reduce the misalignment between the coarse and fine models of the optimized object over a region of interest, and output space mapping (OSM) to ensure matching of response and first......-order derivatives between the mapped coarse model and the fine model at the current iteration point. We also consider an enhanced version in which the input SM coefficients are frequency dependent. The performance of our new algorithms is comparable with the recently published SMIS algorithm when applied...... to a benchmark problem. In comparison with SMIS, the models presented are simple and have a small number of parameters that need to be extracted. The new algorithm is applied to the optimization of coupled-line band-pass filter....
Modified evolutionary algorithm for global optimization
Institute of Scientific and Technical Information of China (English)
郭崇慧; 陆玉昌; 唐焕文
2004-01-01
A modification of evolutionary programming or evolution strategies for n-dimensional global optimization is proposed. Based on the ergodicity and inherent-randomness of chaos, the main characteristic of the new algorithm which includes two phases is that chaotic behavior is exploited to conduct a rough search of the problem space in order to find the promising individuals in Phase Ⅰ. Adjustment strategy of step-length and intensive searches in Phase Ⅱ are employed.The population sequences generated by the algorithm asymptotically converge to global optimal solutions with probability one. The proposed algorithm is applied to several typical test problems. Numerical results illustrate that this algorithm can more efficiently solve complex global optimization problems than evolutionary programming and evolution strategies in most cases.
Novel multi-objective optimization algorithm
Institute of Scientific and Technical Information of China (English)
Jie Zeng; Wei Nie
2014-01-01
Many multi-objective evolutionary algorithms (MOEAs) can converge to the Pareto optimal front and work wel on two or three objectives, but they deteriorate when faced with many-objective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values (instead of the Pareto-dominance relation to select candidate solutions), have been regarded as promising schemes that yield more satisfactory re-sults than wel-known algorithms, such as non-dominated sort-ing genetic algorithm (NSGA-II) and strength Pareto evolution-ary algorithm (SPEA2). However, they can suffer from having a slow convergence speed. This paper proposes a new indicator-based multi-objective optimization algorithm, namely, the multi-objective shuffled frog leaping algorithm based on the ε indicator (ε-MOSFLA). This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capa-bility of global search and quick convergence as an evolutionary strategy and a simple and effective ε-indicator as a fitness as-signment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show thatε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as wel as the speed of convergence.
Ant colony search algorithm for optimal reactive power optimization
Directory of Open Access Journals (Sweden)
Lenin K.
2006-01-01
Full Text Available The paper presents an (ACSA Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical test bus system. The proposed approach is tested and compared to genetic algorithm (GA, Adaptive Genetic Algorithm (AGA.
Gems of combinatorial optimization and graph algorithms
Skutella, Martin; Stiller, Sebastian; Wagner, Dorothea
2015-01-01
Are you looking for new lectures for your course on algorithms, combinatorial optimization, or algorithmic game theory? Maybe you need a convenient source of relevant, current topics for a graduate student or advanced undergraduate student seminar? Or perhaps you just want an enjoyable look at some beautiful mathematical and algorithmic results, ideas, proofs, concepts, and techniques in discrete mathematics and theoretical computer science? Gems of Combinatorial Optimization and Graph Algorithms is a handpicked collection of up-to-date articles, carefully prepared by a select group of international experts, who have contributed some of their most mathematically or algorithmically elegant ideas. Topics include longest tours and Steiner trees in geometric spaces, cartograms, resource buying games, congestion games, selfish routing, revenue equivalence and shortest paths, scheduling, linear structures in graphs, contraction hierarchies, budgeted matching problems, and motifs in networks. This ...
Optimization in engineering models and algorithms
Sioshansi, Ramteen
2017-01-01
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...
Algorithms for worst-case tolerance optimization
DEFF Research Database (Denmark)
Schjær-Jacobsen, Hans; Madsen, Kaj
1979-01-01
New algorithms are presented for the solution of optimum tolerance assignment problems. The problems considered are defined mathematically as a worst-case problem (WCP), a fixed tolerance problem (FTP), and a variable tolerance problem (VTP). The basic optimization problem without tolerances...... is solved by a double-iterative algorithm in which the inner iteration is performed by the FTP- algorithm. The application of the algorithm is demonstrated by means of relatively simple numerical examples. Basic properties, such as convergence properties, are displayed based on the examples....
Optimal Hops-Based Adaptive Clustering Algorithm
Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong
This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.
Optimal sliding guidance algorithm for Mars powered descent phase
Wibben, Daniel R.; Furfaro, Roberto
2016-02-01
Landing on large planetary bodies (e.g. Mars) with pinpoint accuracy presents a set of new challenges that must be addressed. One such challenge is the development of new guidance algorithms that exhibit a higher degree of robustness and flexibility. In this paper, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) optimal sliding guidance (OSG) scheme is applied to the Mars powered descent phase. This guidance algorithm has been specifically designed to combine techniques from both optimal and sliding control theories to generate an acceleration command based purely on the current estimated spacecraft state and desired final target state. Consequently, OSG yields closed-loop trajectories that do not need a reference trajectory. The guidance algorithm has its roots in the generalized ZEM/ZEV feedback guidance and its mathematical equations are naturally derived by defining a non-linear sliding surface as a function of the terms Zero-Effort-Miss and Zero-Effort-Velocity. With the addition of the sliding mode and using Lyapunov theory for non-autonomous systems, one can formally prove that the developed OSG law is globally finite-time stable to unknown but bounded perturbations. Here, the focus is on comparing the generalized ZEM/ZEV feedback guidance with the OSG law to explicitly demonstrate the benefits of the sliding mode augmentation. Results show that the sliding guidance provides a more robust solution in off-nominal scenarios while providing similar fuel consumption when compared to the non-sliding guidance command. Further, a Monte Carlo analysis is performed to examine the performance of the OSG law under perturbed conditions.
Processing Approach of Non-linear Adjustment Models in the Space of Non-linear Models
Institute of Scientific and Technical Information of China (English)
LI Chaokui; ZHU Qing; SONG Chengfang
2003-01-01
This paper investigates the mathematic features of non-linear models and discusses the processing way of non-linear factors which contributes to the non-linearity of a nonlinear model. On the basis of the error definition, this paper puts forward a new adjustment criterion, SGPE.Last, this paper investigates the solution of a non-linear regression model in the non-linear model space and makes the comparison between the estimated values in non-linear model space and those in linear model space.
Optimization Algorithms for Fully Automatic Optimizing Cross-cut Saw
Institute of Scientific and Technical Information of China (English)
LI Xiaochun; DING Qingxin; ZHAO Honglin; SUN Guangbin; XI Jiaxing
2010-01-01
The optimization of boards by grades plays an important role in the production for cross cutting boards, and the outturn rate and utilization of boards are directly affected by the optimization results of boards by grades. At present, the OptiCut series fully automatic optimizing cross-cut saw(FAOCCS) from Germany Weinig Group occupies the main markets in the world, but no report about the relative theories on the optimization technology and its algorithms is available. There exist some disadvantages in woodworking machinery and equipment used for cross cutting boards in China, for example, low sawing precision, outturn rate of boards and productivity, and difficulty in making statistics on the sawing results. Three optimization modes are presented for the optimization algorithms for FAOCCS, namely, optimization of fixed length, optimization of finger-jointed lumber and mixed optimization. Mathematical models are then established for these three optimization modes, and the corresponding software for realizing the optimization is prepared. Finally, Synthetic evaluation on the established mathematical models is presented through three practical examples. The results of synthetic evaluation indicate that FAOCCS using the optimization modes may raise the outturn rate of boards approximately 8% and the productivity obviously, and allows accurate statistics on the cross cut products of boards. The mathematical models of above three optimization modes are useful for increasing the outturn rate and utilization ratio of boards.
Non-Linear Approximation of Bayesian Update
Litvinenko, Alexander
2016-06-23
We develop a non-linear approximation of expensive Bayesian formula. This non-linear approximation is applied directly to Polynomial Chaos Coefficients. In this way, we avoid Monte Carlo sampling and sampling error. We can show that the famous Kalman Update formula is a particular case of this update.
Non-linear finite element modeling
DEFF Research Database (Denmark)
Mikkelsen, Lars Pilgaard
The note is written for courses in "Non-linear finite element method". The note has been used by the author teaching non-linear finite element modeling at Civil Engineering at Aalborg University, Computational Mechanics at Aalborg University Esbjerg, Structural Engineering at the University...... on the governing equations and methods of implementing....
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
1994-01-01
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....
Neural Networks for Non-linear Control
DEFF Research Database (Denmark)
Sørensen, O.
1994-01-01
This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....
A cuckoo search algorithm for multimodal optimization.
Cuevas, Erik; Reyna-Orta, Adolfo
2014-01-01
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.
Optimal Algorithm for Algebraic Factoring
Institute of Scientific and Technical Information of China (English)
支丽红
1997-01-01
This paper presents on optimized method for factoring multivariate polynomials over algebraic extension fields defined by an irreducible ascending set. The basic idea is to convert multivariate polynomials to univariate polynomials and algebraic extension fields to algebraic number fields by suitable integer substituteions.Then factorize the univariate polynomials over the algebraic number fields.Finally,construct mulativariate factors of the original polynomial by Hensel lemma and TRUEFACTOR test.Some examples with timing are included.
An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.
Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed
2015-10-01
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; thereby making 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 well-converged and well-diversified approximation of the Pareto front.
Constrained Multi-Level Algorithm for Trajectory Optimization
Adimurthy, V.; Tandon, S. R.; Jessy, Antony; Kumar, C. Ravi
The emphasis on low cost access to space inspired many recent developments in the methodology of trajectory optimization. Ref.1 uses a spectral patching method for optimization, where global orthogonal polynomials are used to describe the dynamical constraints. A two-tier approach of optimization is used in Ref.2 for a missile mid-course trajectory optimization. A hybrid analytical/numerical approach is described in Ref.3, where an initial analytical vacuum solution is taken and gradually atmospheric effects are introduced. Ref.4 emphasizes the fact that the nonlinear constraints which occur in the initial and middle portions of the trajectory behave very nonlinearly with respect the variables making the optimization very difficult to solve in the direct and indirect shooting methods. The problem is further made complex when different phases of the trajectory have different objectives of optimization and also have different path constraints. Such problems can be effectively addressed by multi-level optimization. In the multi-level methods reported so far, optimization is first done in identified sub-level problems, where some coordination variables are kept fixed for global iteration. After all the sub optimizations are completed, higher-level optimization iteration with all the coordination and main variables is done. This is followed by further sub system optimizations with new coordination variables. This process is continued until convergence. In this paper we use a multi-level constrained optimization algorithm which avoids the repeated local sub system optimizations and which also removes the problem of non-linear sensitivity inherent in the single step approaches. Fall-zone constraints, structural load constraints and thermal constraints are considered. In this algorithm, there is only a single multi-level sequence of state and multiplier updates in a framework of an augmented Lagrangian. Han Tapia multiplier updates are used in view of their special role in
Energy Technology Data Exchange (ETDEWEB)
Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S. [Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)
2011-02-15
Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems. (author)
Optimization of constrained multiple-objective reliability problems using evolutionary algorithms
Energy Technology Data Exchange (ETDEWEB)
Salazar, Daniel [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain) and Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: danielsalazaraponte@gmail.com; Rocco, Claudio M. [Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve; Galvan, Blas J. [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain)]. E-mail: bgalvan@step.es
2006-09-15
This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature.
A new optimization algorithm based on chaos
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave's search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate.In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables optimization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.
A Hybrid Evolutionary Algorithm for Discrete Optimization
Directory of Open Access Journals (Sweden)
J. Bhuvana
2015-03-01
Full Text Available Most of the real world multi-objective problems demand us to choose one Pareto optimal solution out of a finite set of choices. Flexible job shop scheduling problem is one such problem whose solutions are required to be selected from a discrete solution space. In this study we have designed a hybrid genetic algorithm to solve this scheduling problem. Hybrid genetic algorithms combine both the aspects of the search, exploration and exploitation of the search space. Proposed algorithm, Hybrid GA with Discrete Local Search, performs global search through the GA and exploits the locality through discrete local search. Proposed hybrid algorithm not only has the ability to generate Pareto optimal solutions and also identifies them with less computation. Five different benchmark test instances are used to evaluate the performance of the proposed algorithm. Results observed shown that the proposed algorithm has produced the known Pareto optimal solutions through exploration and exploitation of the search space with less number of functional evaluations.
Unger, Johannes; Hametner, Christoph; Jakubek, Stefan; Quasthoff, Marcus
2014-12-01
An accurate state of charge (SoC) estimation of a traction battery in hybrid electric non-road vehicles, which possess higher dynamics and power densities than on-road vehicles, requires a precise battery cell terminal voltage model. This paper presents a novel methodology for non-linear system identification of battery cells to obtain precise battery models. The methodology comprises the architecture of local model networks (LMN) and optimal model based design of experiments (DoE). Three main novelties are proposed: 1) Optimal model based DoE, which aims to high dynamically excite the battery cells at load ranges frequently used in operation. 2) The integration of corresponding inputs in the LMN to regard the non-linearities SoC, relaxation, hysteresis as well as temperature effects. 3) Enhancements to the local linear model tree (LOLIMOT) construction algorithm, to achieve a physical appropriate interpretation of the LMN. The framework is applicable for different battery cell chemistries and different temperatures, and is real time capable, which is shown on an industrial PC. The accuracy of the obtained non-linear battery model is demonstrated on cells with different chemistries and temperatures. The results show significant improvement due to optimal experiment design and integration of the battery non-linearities within the LMN structure.
Institute of Scientific and Technical Information of China (English)
陶莉莉; 孔祥东; 钟伟民; 钱锋
2012-01-01
In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modelling by dynamic mixture models
Immune Algorithm For Document Query Optimization
Institute of Scientific and Technical Information of China (English)
WangZiqiang; FengBoqin
2005-01-01
To efficiently retrieve relevant document from the rapid proliferation of large information collections, a novel immune algorithm for document query optimization is proposed. The essential ideal of the immune algorithm is that the crossover and mutation of operator are constructed according to its own characteristics of information retrieval. Immune operator is adopted to avoid degeneracy. Relevant documents retrieved am merged to a single document list according to rank formula. Experimental results show that the novel immune algorithm can lead to substantial improvements of relevant document retrieval effectiveness.
Improvements To Glowworm Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Piotr Oramus
2010-01-01
Full Text Available Glowworm Swarm Optimization algorithm is applied for the simultaneous capture of multipleoptima of multimodal functions. The algorithm uses an ensemble of agents, which scan thesearch space and exchange information concerning a fitness of their current position. Thefitness is represented by a level of a luminescent quantity called luciferin. An agent movesin direction of randomly chosen neighbour, which broadcasts higher value of the luciferin.Unfortunately, in the absence of neighbours, the agent does not move at all. This is anunwelcome feature, because it diminishes the performance of the algorithm. Additionally,in the case of parallel processing, this feature can lead to unbalanced loads. This paperpresents simple modifications of the original algorithm, which improve performance of thealgorithm by limiting situations, in which the agent cannot move. The paper provides resultsof comparison of an original and modified algorithms calculated for several multimodal testfunctions.
An Optimization Synchronization Algorithm for TDDM Signal
Directory of Open Access Journals (Sweden)
Fang Liu
2016-01-01
Full Text Available The time division data modulation (TDDM mechanism is recommended to improve the communications quality and enhance the antijamming capability of the spread spectrum communication system, which will be used in the next generation global navigation satellite (GNSS systems. According to the principle and the characteristics of TDDM signal, an optimization synchronization algorithm is proposed. In the new algorithm, the synchronization accuracy and environmental adaptability have been improved with the special local sequence structure, the multicorrelation processing, and the proportion threshold mechanism. Thus, the inversion estimation formula was established. The simulation results demonstrate that the new algorithm can eliminate the illegibility threat in the synchronization process and can adapt to a lower SNR. In addition, this algorithm is better than the traditional algorithms in terms of synchronization accuracy and adaptability.
Function Optimization Based on Quantum Genetic Algorithm
Directory of Open Access Journals (Sweden)
Ying Sun
2014-01-01
Full Text Available Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA and Genetic Quantum Algorithm (GQA. The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.
Angelic Hierarchical Planning: Optimal and Online Algorithms
2008-12-06
restrict our attention to plans in I∗(Act, s0). Definition 2. ( Parr and Russell , 1998) A plan ah∗ is hierarchically optimal iff ah∗ = argmina∈I∗(Act,s0):T...Murdock, Dan Wu, and Fusun Yaman. SHOP2: An HTN planning system. JAIR, 20:379–404, 2003. Ronald Parr and Stuart Russell . Reinforcement Learning with...Angelic Hierarchical Planning: Optimal and Online Algorithms Bhaskara Marthi Stuart J. Russell Jason Wolfe Electrical Engineering and Computer
Worst-case Optimal Join Algorithms
Ngo, Hung Q; Ré, Christopher; Rudra, Atri
2012-01-01
Efficient join processing is one of the most fundamental and well-studied tasks in database research. In this work, we examine algorithms for natural join queries over many relations and describe a novel algorithm to process these queries optimally in terms of worst-case data complexity. Our result builds on recent work by Atserias, Grohe, and Marx, who gave bounds on the size of a full conjunctive query in terms of the sizes of the individual relations in the body of the query. These bounds, however, are not constructive: they rely on Shearer's entropy inequality which is information-theoretic. Thus, the previous results leave open the question of whether there exist algorithms whose running time achieve these optimal bounds. An answer to this question may be interesting to database practice, as it is known that any algorithm based on the traditional select-project-join style plans typically employed in an RDBMS are asymptotically slower than the optimal for some queries. We construct an algorithm whose runn...
Combinatorial Multiobjective Optimization Using Genetic Algorithms
Crossley, William A.; Martin. Eric T.
2002-01-01
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.
TWO ALGORITHMS FOR LC1 UNCONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Wen-yu Sun; R.J.B.de Sampaio; Jin-Yun Yuan
2000-01-01
In this paper we present two algorithms for LC1 unconstrained optimization problems which use the second order Dini upper directional derivative. These methods are simple and easy to perform. We discuss the related properties of the iteration function, and establish the global and superlinear convergence of our methods.
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2014-01-01
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2017-01-01
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
Algorithm of capacity expansion on networks optimization
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
The paper points out the relationship between the bottleneck and the minimum cutset of the network, and presents a capacity expansion algorithm of network optimization to solve the network bottleneck problem. The complexity of the algorithm is also analyzed. As required by the algorithm, some virtual sources are imported through the whole positive direction subsection in the network, in which a certain capacity value is given. Simultaneously, a corresponding capacity-expanded network is constructed to search all minimum cutsets. For a given maximum flow value of the network, the authors found an adjustment value of each minimum cutset arc's group with gradually reverse calculation and marked out the feasible flow on the capacity-extended networks again with the adjustment value increasing. All this has been done repeatedly until the original topology structure is resumed. So the algorithm can increase the capacity of networks effectively and solve the bottleneck problem of networks.
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
Likelihood inference for discretely observed non-linear diffusions
1998-01-01
This paper is concerned with the Bayesian estimation of non-linear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis-Hastings algorithm, in conjunction with the Euler-Maruyama discretization scheme, are used to sample the posterior distribution of the lat...
Non-linearly weighted fuzzy correlation for color-image retrieval
Institute of Scientific and Technical Information of China (English)
Guoguang Mu(母国光); Hongchen Zhai(翟宏琛); Siyuan Zhang(张思远)
2003-01-01
An algorithm with non-linear weight factors in the summation process for fuzzy correlation of color his-tograms is presented, in which non-linear weights are assigned to some characteristic colors of interest.Experimental results show that this can improve the retrieval of color images with partial aberrations orwith local color characters.
Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging.
Di Pasquale, Nicodemo; Davie, Stuart J; Popelier, Paul L A
2016-04-12
The machine learning method kriging is an attractive tool to construct next-generation force fields. Kriging can accurately predict atomistic properties, which involves optimization of the so-called concentrated log-likelihood function (i.e., fitness function). The difficulty of this optimization problem quickly escalates in response to an increase in either the number of dimensions of the system considered or the size of the training set. In this article, we demonstrate and compare the use of two search algorithms, namely, particle swarm optimization (PSO) and differential evolution (DE), to rapidly obtain the maximum of this fitness function. The ability of these two algorithms to find a stationary point is assessed by using the first derivative of the fitness function. Finally, the converged position obtained by PSO and DE is refined through the limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm, which belongs to the class of quasi-Newton algorithms. We show that both PSO and DE are able to come close to the stationary point, even in high-dimensional problems. They do so in a reasonable amount of time, compared to that with the Newton and quasi-Newton algorithms, regardless of the starting position in the search space of kriging hyperparameters. The refinement through L-BFGS-B is able to give the position of the maximum with whichever precision is desired.
2013-01-01
This book consists of twenty seven chapters, which can be divided into three large categories: articles with the focus on the mathematical treatment of non-linear problems, including the methodologies, algorithms and properties of analytical and numerical solutions to particular non-linear problems; theoretical and computational studies dedicated to the physics and chemistry of non-linear micro-and nano-scale systems, including molecular clusters, nano-particles and nano-composites; and, papers focused on non-linear processes in medico-biological systems, including mathematical models of ferments, amino acids, blood fluids and polynucleic chains.
Generalized Weiszfeld Algorithms for Lq Optimization.
Aftab, Khurrum; Hartley, Richard; Trumpf, Jochen
2015-04-01
In many computer vision applications, a desired model of some type is computed by minimizing a cost function based on several measurements. Typically, one may compute the model that minimizes the L2 cost, that is the sum of squares of measurement errors with respect to the model. However, the Lq solution which minimizes the sum of the qth power of errors usually gives more robust results in the presence of outliers for some values of q, for example, q = 1. The Weiszfeld algorithm is a classic algorithm for finding the geometric L1 mean of a set of points in Euclidean space. It is provably optimal and requires neither differentiation, nor line search. The Weiszfeld algorithm has also been generalized to find the L1 mean of a set of points on a Riemannian manifold of non-negative curvature. This paper shows that the Weiszfeld approach may be extended to a wide variety of problems to find an Lq mean for 1 ≤ q algorithm provably finds the global Lq optimum) and multiple rotation averaging (for which no such proof exists). Experimental results of Lq optimization for rotations show the improved reliability and robustness compared to L2 optimization.
Sha, Daohang
2010-01-01
Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and optimization methods analytically, which can avoid the trouble to select a proper learning rate for the gradient method. The proof of weak convergence of the proposed algorithm also is given. Although this approach is proposed for three-layer, feed-forward neural networks, it could be extended to multiple layer feed-forward neural networks. The effectiveness of the proposed algorithms applied to the identification of behavior of a two-input and two-output non-linear dynamic system is demonstrated by simulation experiments.
Optimal Search Mechanism Analysis of Light Ray Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Jihong SHEN; Jialian LI; Bin WEI
2012-01-01
Based on Fermat's principle and the automatic optimization mechanism in the propagation process of light,an optimal searching algorithm named light ray optimization is presented,where the laws of refraction and reflection of light rays are integrated into searching process of optimization.In this algorithm,coordinate space is assumed to be the space that is full of media with different refractivities,then the space is divided by grids,and finally the searching path is assumed to be the propagation path of light rays.With the law of refraction,the search direction is deflected to the direction that makes the value of objective function decrease.With the law of reflection,the search direction is changed,which makes the search continue when it cannot keep going with refraction.Only the function values of objective problems are used and there is no artificial rule in light ray optimization,so it is simple and easy to realize.Theoretical analysis and the results of numerical experiments show that the algorithm is feasible and effective.
Modified constriction particle swarm optimization algorithm
Institute of Scientific and Technical Information of China (English)
Zhe Zhang; Limin Jia; Yong Qin
2015-01-01
To deal with the demerits of constriction particle swarm optimization (CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random ve-locity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likeli-hood of being trapped into local optima. Final y the convergence of the algorithm is verified by calculation examples.
Configurable intelligent optimization algorithm design and practice in manufacturing
Tao, Fei; Laili, Yuanjun
2014-01-01
Presenting the concept and design and implementation of configurable intelligent optimization algorithms in manufacturing systems, this book provides a new configuration method to optimize manufacturing processes. It provides a comprehensive elaboration of basic intelligent optimization algorithms, and demonstrates how their improvement, hybridization and parallelization can be applied to manufacturing. Furthermore, various applications of these intelligent optimization algorithms are exemplified in detail, chapter by chapter. The intelligent optimization algorithm is not just a single algorit
全局数值寻优的一种混合遗传算法%Hybrid Simplex-improved Genetic Algorithm for Global Numerical Optimization
Institute of Scientific and Technical Information of China (English)
任子武; 伞冶; 陈俊风
2007-01-01
In this paper, a hybrid simplex-improved genetic algorithm (HSIGA) which combines simplex method (SM) and genetic algorithm (GA) is proposed to solve global numerical optimization problems. In this hybrid algorithm some improved genetic mechanisms, for example, non-linear ranking selection,competition and selection among several crossover offspring,adaptive change of mutation scaling and stage evolution, are adopted; and new population is produced through three approaches, i.e. elitist strategy, modified simplex strategy and improved genetic algorithm (IGA) strategy. Numerical experiments are included to demonstrate effectiveness of the proposed algorithm.
Optimized dynamical decoupling via genetic algorithms
Quiroz, Gregory; Lidar, Daniel A.
2013-11-01
We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
Optimized Dynamical Decoupling via Genetic Algorithms
Quiroz, Gregory
2013-01-01
We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
Particle algorithms for optimization on binary spaces
Schäfer, Christian
2011-01-01
We propose a general sequential Monte Carlo approach for optimization of pseudo-Boolean objective functions. There are three aspects we particularly address in this work. First, we give a unified approach to stochastic optimization based on sequential Monte Carlo techniques, including the cross-entropy method and simulated annealing as special cases. Secondly, we point out the need for auxiliary sampling distributions, that is parametric families on binary spaces, which are able to reproduce complex dependency structures. We discuss some known and novel binary parametric families and illustrate their usefulness in our numerical experiments. Finally, we provide numerical evidence that particle-driven optimization algorithms yield superior results on strongly multimodal optimization problems while local search heuristics outperform them on easier problems.
An Optimal Algorithm for Linear Bandits
Cesa-Bianchi, Nicolò
2011-01-01
We provide the first algorithm for online bandit linear optimization whose regret after T rounds is of order sqrt{Td ln N} on any finite class X of N actions in d dimensions, and of order d*sqrt{T} (up to log factors) when X is infinite. These bounds are not improvable in general. The basic idea utilizes tools from convex geometry to construct what is essentially an optimal exploration basis. We also present an application to a model of linear bandits with expert advice. Interestingly, these results show that bandit linear optimization with expert advice in d dimensions is no more difficult (in terms of the achievable regret) than the online d-armed bandit problem with expert advice (where EXP4 is optimal).
Algorithms for Optimally Arranging Multicore Memory Structures
Directory of Open Access Journals (Sweden)
Wei-Che Tseng
2010-01-01
Full Text Available As more processing cores are added to embedded systems processors, the relationships between cores and memories have more influence on the energy consumption of the processor. In this paper, we conduct fundamental research to explore the effects of memory sharing on energy in a multicore processor. We study the Memory Arrangement (MA Problem. We prove that the general case of MA is NP-complete. We present an optimal algorithm for solving linear MA and optimal and heuristic algorithms for solving rectangular MA. On average, we can produce arrangements that consume 49% less energy than an all shared memory arrangement and 14% less energy than an all private memory arrangement for randomly generated instances. For DSP benchmarks, we can produce arrangements that, on average, consume 20% less energy than an all shared memory arrangement and 27% less energy than an all private memory arrangement.
FOGSAA: Fast Optimal Global Sequence Alignment Algorithm
Chakraborty, Angana; Bandyopadhyay, Sanghamitra
2013-04-01
In this article we propose a Fast Optimal Global Sequence Alignment Algorithm, FOGSAA, which aligns a pair of nucleotide/protein sequences faster than any optimal global alignment method including the widely used Needleman-Wunsch (NW) algorithm. FOGSAA is applicable for all types of sequences, with any scoring scheme, and with or without affine gap penalty. Compared to NW, FOGSAA achieves a time gain of (70-90)% for highly similar nucleotide sequences (> 80% similarity), and (54-70)% for sequences having (30-80)% similarity. For other sequences, it terminates with an approximate score. For protein sequences, the average time gain is between (25-40)%. Compared to three heuristic global alignment methods, the quality of alignment is improved by about 23%-53%. FOGSAA is, in general, suitable for aligning any two sequences defined over a finite alphabet set, where the quality of the global alignment is of supreme importance.
Trajectory metaheuristic algorithms to optimize problems combinatorics
Directory of Open Access Journals (Sweden)
Natalia Alancay
2016-12-01
Full Text Available The application of metaheuristic algorithms to optimization problems has been very important during the last decades. The main advantage of these techniques is their flexibility and robustness, which allows them to be applied to a wide range of problems. In this work we concentrate on metaheuristics based on Simulated Annealing, Tabu Search and Variable Neighborhood Search trajectory whose main characteristic is that they start from a point and through the exploration of the neighborhood vary the current solution, forming a trajectory. By means of the instances of the selected combinatorial problems, a computational experimentation is carried out that illustrates the behavior of the algorithmic methods to solve them. The main objective of this work is to perform the study and comparison of the results obtained for the selected trajectories metaheuristics in its application for the resolution of a set of academic problems of combinatorial optimization.
Intelligent perturbation algorithms for space scheduling optimization
Kurtzman, Clifford R.
1990-01-01
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.
Genetic algorithm optimization for finned channel performance
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase in the pressure drop. This leads to an increased pumping power requirement so that one may seek an optimum design for such systems. The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm. Each fin arrangement is considered a solution to the problem(an individual for genetic algorithm). An initial population is generated randomly at the first step. Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.
Non-Linear Relativity in Position Space
Kimberly, D; Medeiros-Neto, J F; Kimberly, Dagny; Magueijo, João; Medeiros, João
2003-01-01
We propose two methods for obtaining the dual of non-linear relativity as previously formulated in momentum space. In the first we allow for the (dual) position space to acquire a non-linear representation of the Lorentz group independently of the chosen representation in momentum space. This requires a non-linear definition for the invariant contraction between momentum and position spaces. The second approach, instead, respects the linearity of the invariant contraction. This fully fixes the dual of momentum space and dictates a set of energy-dependent space-time Lorentz transformations. We discuss a variety of physical implications that would distinguish these two strategies. We also show how they point to two rather distinct formulations of theories of gravity with an invariant energy and/or length scale.
Correlations and Non-Linear Probability Models
DEFF Research Database (Denmark)
Breen, Richard; Holm, Anders; Karlson, Kristian Bernt
2014-01-01
the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models....
Non-linear (loop) quantum cosmology
Bojowald, Martin; Dantas, Christine C; Jaffe, Matthew; Simpson, David
2012-01-01
Inhomogeneous quantum cosmology is modeled as a dynamical system of discrete patches, whose interacting many-body equations can be mapped to a non-linear minisuperspace equation by methods analogous to Bose-Einstein condensation. Complicated gravitational dynamics can therefore be described by more-manageable equations for finitely many degrees of freedom, for which powerful solution procedures are available, including effective equations. The specific form of non-linear and non-local equations suggests new questions for mathematical and computational investigations, and general properties of non-linear wave equations lead to several new options for physical effects and tests of the consistency of loop quantum gravity. In particular, our quantum cosmological methods show how sizeable quantum corrections in a low-curvature universe can arise from tiny local contributions adding up coherently in large regions.
Optimization of neutron monitor data correction algorithms
Energy Technology Data Exchange (ETDEWEB)
Paschalis, P. [Nuclear and Particle Physics Section, Physics Department, National and Kapodistrian University of Athens, Zografos 15783, Athens (Greece); Mavromichalaki, H., E-mail: emavromi@phys.uoa.gr [Nuclear and Particle Physics Section, Physics Department, National and Kapodistrian University of Athens, Zografos 15783, Athens (Greece)
2013-06-21
Nowadays, several neutron monitor stations worldwide, broadcast their cosmic ray data in real time, in order for the scientific community to be able to use these measurements immediately. In parallel, the development of the Neutron Monitor Database (NMDB; (http://www.nmdb.eu)) which collects all the high resolution real time measurements, allows the implementation of various applications and services by using these data instantly. Therefore, it is obvious that the need for high quality real time data is imperative. The quality of the data is handled by different correction algorithms that filter the real time measurements for undesired instrumental variations. In this work, an optimization of the Median Editor that is currently mainly applied to the neutron monitor data and the recently proposed ANN algorithm based on neural networks is presented. This optimization leads to the implementation of the Median Editor Plus and the ANN Plus algorithms. A direct comparison of these algorithms with the newly appeared Edge Editor is performed and the results are presented.
Adaptive spectral identification techniques in presence of undetected non linearities
Cella, G; Guidi, G M
2002-01-01
The standard procedure for detection of gravitational wave coalescing binaries signals is based on Wiener filtering with an appropriate bank of template filters. This is the optimal procedure in the hypothesis of addictive Gaussian and stationary noise. We study the possibility of improving the detection efficiency with a class of adaptive spectral identification techniques, analyzing their effect in presence of non stationarities and undetected non linearities in the noise
A General Nonlinear Optimization Algorithm for Lower Bound Limit Analysis
DEFF Research Database (Denmark)
Krabbenhøft, Kristian; Damkilde, Lars
2003-01-01
The non-linear programming problem associated with the discrete lower bound limit analysis problem is treated by means of an algorithm where the need to linearize the yield criteria is avoided. The algorithm is an interior point method and is completely general in the sense that no particular...... finite element discretization or yield criterion is required. As with interior point methods for linear programming the number of iterations is affected only little by the problem size. Some practical implementation issues are discussed with reference to the special structure of the common lower bound...
Jianwen Guo; Zhenzhong Sun; Hong Tang; Xuejun Jia; Song Wang; Xiaohui Yan; Guoliang Ye; Guohong Wu
2016-01-01
All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test fun...
Focused Crawler Optimization Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
Hartanto Kusuma Wardana
2011-12-01
Full Text Available As the size of the Web continues to grow, searching it for useful information has become more difficult. Focused crawler intends to explore the Web conform to a specific topic. This paper discusses the problems caused by local searching algorithms. Crawler can be trapped within a limited Web community and overlook suitable Web pages outside its track. A genetic algorithm as a global searching algorithm is modified to address the problems. The genetic algorithm is used to optimize Web crawling and to select more suitable Web pages to be fetched by the crawler. Several evaluation experiments are conducted to examine the effectiveness of the approach. The crawler delivers collections consist of 3396 Web pages from 5390 links which had been visited, or filtering rate of Roulette-Wheel selection at 63% and precision level at 93% in 5 different categories. The result showed that the utilization of genetic algorithm had empowered focused crawler to traverse the Web comprehensively, despite it relatively small collections. Furthermore, it brought up a great potential for building an exemplary collections compared to traditional focused crawling methods.
A GREEDY GENETIC ALGORITHM FOR UNCONSTRAINED GLOBAL OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
ZHAO Xinchao
2005-01-01
The greedy algorithm is a strong local searching algorithm. The genetica lgorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.
Bioinspired computation in combinatorial optimization: algorithms and their computational complexity
DEFF Research Database (Denmark)
Neumann, Frank; Witt, Carsten
2012-01-01
Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials...
An Approach In Optimization Of Ad-Hoc Routing Algorithms
Directory of Open Access Journals (Sweden)
Sarvesh Kumar Sharma
2012-06-01
Full Text Available In this paper different optimization of Ad-hoc routing algorithm is surveyed and a new method using training based optimization algorithm for reducing the complexity of routing algorithms is suggested. A binary matrix is assigned to each node in the network and gets updated after each data transfer using the protocols. The use of optimization algorithm in routing algorithm can reduce the complexity of routing to the least amount possible.
Gas pipeline optimization using adaptive algorithms
Energy Technology Data Exchange (ETDEWEB)
Smati, A.; Zemmour, N. [INH, Boumerdes (Algeria)
1996-12-31
Transmission gas pipeline network consume significant amounts of energy. Then, minimizing the energy requirements is a challenging task. Due to the nonlinearity and poor knowledge of the system states, several results, based on the optimal control theory, are obtained only for simple configurations. In this paper an optimization scheme in the face of varying demand is carried out. It is based on the use of a dynamic simulation program as a plant model and the Pareto set technique to sell out useful experiments. Experiments are used for the identification of regression models based on an original class of functions. The nonlinear programming algorithm results. Its connection with regression models permits the definition off-line, and for a long time horizon, of the optimal discharge pressure trajectory for all the compressor stations. The use of adaptive algorithms, with high frequency, permits one to cancel the effect of unknown disturbances and errors in demand forecasts. In this way, an on-line optimization scheme using data of SCADA system is presented.
Modeling and Non-Linear Self-Tuning Robust Trajectory Control of an Autonomous Underwater Vehicle
Directory of Open Access Journals (Sweden)
Thor Inge Fossen
1988-10-01
Full Text Available A non-linear self-tuning algorithm is demonstrated for an autonomous underwater vehicle. Tighter control is achieved by a non-linear parameter identification algorithm which reduces the parameter uncertainty bounds. Expensive hydrodynamic tests for parameter determination can thus be avoided. Excellent tracking performance and robustness to parameter uncertainty are guaranteed through a robust control strategy based on the estimated parameters. The nonlinear control law is highly robust for imprecise models and the neglected dynamics. The non-linear self-tuning control strategy is simulated for the horizontal positioning of an underwater vehicle.
Non linear system become linear system
Directory of Open Access Journals (Sweden)
Petre Bucur
2007-01-01
Full Text Available The present paper refers to the theory and the practice of the systems regarding non-linear systems and their applications. We aimed the integration of these systems to elaborate their response as well as to highlight some outstanding features.
Oscillatons formed by non linear gravity
Obregón, O; Schunck, F E; Obregon, Octavio; Schunck, Franz E.
2004-01-01
Oscillatons are solutions of the coupled Einstein-Klein-Gordon (EKG) equations that are globally regular and asymptotically flat. By means of a Legendre transformation we are able to visualize the behaviour of the corresponding objects in non-linear gravity where the scalar field has been absorbed by means of the conformal mapping.
Correlations and Non-Linear Probability Models
DEFF Research Database (Denmark)
Breen, Richard; Holm, Anders; Karlson, Kristian Bernt
2014-01-01
Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations betwee...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models.......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under...
A Genetic Algorithm for Optimization in Conceptual Design Phase of Robots
Directory of Open Access Journals (Sweden)
Amir Jafari
2007-10-01
Full Text Available One of the most important criteria of the industrial robots is the dynamic performance of manipulators. There are some critical parameters which determine the dynamic performance of robot manipulators. While the correlations between these parameters are complex and highly non-linear, deciding on these parameters to optimize the dynamic performance of manipulators is a difficult and time-consuming task, especially in the early conceptual design phase. The gearbox size and the lengths of the arms are parameters that have a large impact on the performance and the cost of robots. In order to perform optimization, a mathematical programming model is developed. An objective function is defined to determine optimal gearboxes and arm lengths from an acceleration capability perspective. The arm lengths are treated as continuous variables whereas the gearboxes are selected from a list of available units. This paper presents a Genetic algorithm procedure which shows how optimization can be used in the early phases of a development process in order to evaluate the potential of a concept. This study considers a three degree of freedom robot. The mathematical model is coded in the C language and optimized using the Genetic algorithm. Comparison of the obtained results with optimum values based on Complex algorithm clearly shows the advantages of the proposed method.
Distributed Algorithms for Optimal Power Flow Problem
Lam, Albert Y S; Tse, David
2011-01-01
Optimal power flow (OPF) is an important problem for power generation and it is in general non-convex. With the employment of renewable energy, it will be desirable if OPF can be solved very efficiently so its solution can be used in real time. With some special network structure, e.g. trees, the problem has been shown to have a zero duality gap and the convex dual problem yields the optimal solution. In this paper, we propose a primal and a dual algorithm to coordinate the smaller subproblems decomposed from the convexified OPF. We can arrange the subproblems to be solved sequentially and cumulatively in a central node or solved in parallel in distributed nodes. We test the algorithms on IEEE radial distribution test feeders, some random tree-structured networks, and the IEEE transmission system benchmarks. Simulation results show that the computation time can be improved dramatically with our algorithms over the centralized approach of solving the problem without decomposition, especially in tree-structured...
Non-linear dendrites can tune neurons
Directory of Open Access Journals (Sweden)
Romain Daniel Cazé
2014-03-01
Full Text Available A signature of visual, auditory, and motor cortices is the presence of neurons tuned to distinct features of the environment. While neuronal tuning can be observed in most brain areas, its origin remains enigmatic, and new calcium imaging data complicate this problem. Dendritic calcium signals, in a L2/3 neuron from the mouse visual cortex, display a wide range of tunings that could be different from the neuronal tuning (Jia et al 2010. To elucidate this observation we use multi-compartmental models of increasing complexity, from a binary to a realistic biophysical model of L2/3 neuron. These models possess non-linear dendritic subunits inside which the result of multiple excitatory inputs is smaller than their arithmetic sum. While dendritic non-linear subunits are ad-hoc in the binary model, non-linearities in the realistic model come from the passive saturation of synaptic currents. Because of these non-linearities our neuron models are scatter sensitive: the somatic membrane voltage is higher when presynaptic inputs target different dendrites than when they target a single dendrite. This spatial bias in synaptic integration is, in our models, the origin of neuronal tuning. Indeed, assemblies of presynaptic inputs encode the stimulus property through an increase in correlation or activity, and only the assembly that encodes the preferred stimulus targets different dendrites. Assemblies coding for the non-preferred stimuli target single dendrites, explaining the wide range of observed tunings and the possible difference between dendritic and somatic tuning. We thus propose, in accordance with the latest experimental observations, that non-linear integration in dendrites can generate neuronal tuning independently of the coding regime.
A Survey on Meta-Heuristic Global Optimization Algorithms
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Mohammad Khajehzadeh
2011-06-01
Full Text Available Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Recently, metaheuristic global optimization algorithms have become a popular choice for solving complex and intricate problems, which are otherwise difficult to solve by traditional methods. In the present study, an attempt is made to review the most popular and well known metaheuristic global optimization algorithms introduced during the past decades.
OPTIMAL CONTROL ALGORITHMS FOR SECOND ORDER SYSTEMS
Directory of Open Access Journals (Sweden)
Danilo Pelusi
2013-01-01
Full Text Available Proportional Integral Derivative (PID controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.
Pebble bed reactor fuel cycle optimization using particle swarm algorithm
Energy Technology Data Exchange (ETDEWEB)
Tavron, Barak, E-mail: btavron@bgu.ac.il [Planning, Development and Technology Division, Israel Electric Corporation Ltd., P.O. Box 10, Haifa 31000 (Israel); Shwageraus, Eugene, E-mail: es607@cam.ac.uk [Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ (United Kingdom)
2016-10-15
Highlights: • Particle swarm method has been developed for fuel cycle optimization of PBR reactor. • Results show uranium utilization low sensitivity to fuel and core design parameters. • Multi-zone fuel loading pattern leads to a small improvement in uranium utilization. • Thorium mixes with highly enriched uranium yields the best uranium utilization. - Abstract: Pebble bed reactors (PBR) features, such as robust thermo-mechanical fuel design and on-line continuous fueling, facilitate wide range of fuel cycle alternatives. A range off fuel pebble types, containing different amounts of fertile or fissile fuel material, may be loaded into the reactor core. Several fuel loading zones may be used since radial mixing of the pebbles was shown to be limited. This radial separation suggests the possibility to implement the “seed-blanket” concept for the utilization of fertile fuels such as thorium, and for enhancing reactor fuel utilization. In this study, the particle-swarm meta-heuristic evolutionary optimization method (PSO) has been used to find optimal fuel cycle design which yields the highest natural uranium utilization. The PSO method is known for solving efficiently complex problems with non-linear objective function, continuous or discrete parameters and complex constrains. The VSOP system of codes has been used for PBR fuel utilization calculations and MATLAB script has been used to implement the PSO algorithm. Optimization of PBR natural uranium utilization (NUU) has been carried out for 3000 MWth High Temperature Reactor design (HTR) operating on the Once Trough Then Out (OTTO) fuel management scheme, and for 400 MWth Pebble Bed Modular Reactor (PBMR) operating on the multi-pass (MEDUL) fuel management scheme. Results showed only a modest improvement in the NUU (<5%) over reference designs. Investigation of thorium fuel cases showed that the use of HEU in combination with thorium results in the most favorable reactor performance in terms of
Institute of Scientific and Technical Information of China (English)
Hai-tao Bo; Xiao-feng Jia; Xiao-rui Wang
2009-01-01
As in the building of deep buried long tunnels, there are complicated conditions such as great deformation, high stress, multi-variables, high non-linearity and so on, the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country, It has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying, expressing and disposing such kind of multiple variables and complicated non- linear relations. In this paper, a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm, thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory, the global search capability of the immune genetic algorithm is raised, thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel, the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency, the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationlzation design of the wall rock of the tunnel.
Intervals in evolutionary algorithms for global optimization
Energy Technology Data Exchange (ETDEWEB)
Patil, R.B.
1995-05-01
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.
Directory of Open Access Journals (Sweden)
Jianwen Guo
2016-01-01
Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.
Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G
2012-01-01
Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.
Watermark Extraction Optimization Using PSO Algorithm
Directory of Open Access Journals (Sweden)
Mohammad Dehghani Soltani
2013-04-01
Full Text Available In this study we propose an improved method for watermarking based on ML detector that in comparison with similar methods this scheme has more robustness against attacks, with the same embedded length of logo. Embedding the watermark will perform in the low frequency coefficients of wavelet transform of high entropy blocks (blocks which have more information. Then in the watermark extraction step by using PSO algorithm in a way that maximum quality in comparison with previous methods obtain, by optimizing the Lagrange factor in the Neyman-Peyrson test, we extract the watermark. Finally, performance of proposed scheme has been investigated and accuracy of results are shown by simulation.
Optimization Algorithms for Nuclear Reactor Power Control
Energy Technology Data Exchange (ETDEWEB)
Kim, Yeong Min; Oh, Won Jong; Oh, Seung Jin; Chun, Won Gee; Lee, Yoon Joon [Jeju National University, Jeju (Korea, Republic of)
2010-10-15
One of the control techniques that could replace the present conventional PID controllers in nuclear plants is the linear quadratic regulator (LQR) method. The most attractive feature of the LQR method is that it can provide the systematic environments for the control design. However, the LQR approach heavily depends on the selection of cost function and the determination of the suitable weighting matrices of cost function is not an easy task, particularly when the system order is high. The purpose of this paper is to develop an efficient and reliable algorithm that could optimize the weighting matrices of the LQR system
Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
Ünal, Muhammet; Topuz, Vedat; Erdal, Hasan
2013-01-01
Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.
Optimization of machining processes using pattern search algorithm
Directory of Open Access Journals (Sweden)
Miloš Madić
2014-04-01
Full Text Available Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.
Sinou, Jean-Jacques; Thouverez, Fabrice; Jezequel, Louis
2006-01-01
International audience; Herein, a novel non-linear procedure for producing non-linear behaviour and stable limit cycle amplitudes of non-linear systems subjected to super-critical Hopf bifurcation point is presented. This approach, called Complex Non-Linear Modal Analysis (CNLMA), makes use of the non-linear unstable mode which governs the non-linear dynamic of structural systems in unstable areas. In this study, the computational methodology of CNLMA is presented for the systematic estimatio...
Institute of Scientific and Technical Information of China (English)
张毕西; 程硕
2014-01-01
This paper research the optimization of worker-assignment problem with different skills in non lin-ear pipeline for the made to order production of multi-product and small-batch , through using the combina-tion of improved genetic algorithm and linear programming phase, to optimise the workers' assignment and scheduling, achieving the cost optimal target in optimise system ,and demonstrate the results' effectiveness by the method.%文章就如何对多产品、小批量订单式生产模式下有着工人技能差异性的非线性流水线工人指派问题进行研究，通过利用改进的遗传算法和线性规划相结合的方法，对拥有不同技能、不同工序完成时间的工人进行指派和调度，实现非线性流水线生产系统下的成本较优的目标，并通过算例证明该方法的有效性。
Applications of metaheuristic optimization algorithms in civil engineering
Kaveh, A
2017-01-01
The book presents recently developed efficient metaheuristic optimization algorithms and their applications for solving various optimization problems in civil engineering. The concepts can also be used for optimizing problems in mechanical and electrical engineering.
A Feedback Optimal Control Algorithm with Optimal Measurement Time Points
Directory of Open Access Journals (Sweden)
Felix Jost
2017-02-01
Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.
Lunar Habitat Optimization Using Genetic Algorithms
SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.
OPC recipe optimization using genetic algorithm
Asthana, Abhishek; Wilkinson, Bill; Power, Dave
2016-03-01
Optimization of OPC recipes is not trivial due to multiple parameters that need tuning and their correlation. Usually, no standard methodologies exist for choosing the initial recipe settings, and in the keyword development phase, parameters are chosen either based on previous learning, vendor recommendations, or to resolve specific problems on particular special constructs. Such approaches fail to holistically quantify the effects of parameters on other or possible new designs, and to an extent are based on the keyword developer's intuition. In addition, when a quick fix is needed for a new design, numerous customization statements are added to the recipe, which make it more complex. The present work demonstrates the application of Genetic Algorithm (GA) technique for optimizing OPC recipes. GA is a search technique that mimics Darwinian natural selection and has applications in various science and engineering disciplines. In this case, GA search heuristic is applied to two problems: (a) an overall OPC recipe optimization with respect to selected parameters and, (b) application of GA to improve printing and via coverage at line end geometries. As will be demonstrated, the optimized recipe significantly reduced the number of ORC violations for case (a). For case (b) line end for various features showed significant printing and filling improvement.
Efficient algorithms for the laboratory discovery of optimal quantum controls.
Turinici, Gabriel; Le Bris, Claude; Rabitz, Herschel
2004-01-01
The laboratory closed-loop optimal control of quantum phenomena, expressed as minimizing a suitable cost functional, is currently implemented through an optimization algorithm coupled to the experimental apparatus. In practice, the most commonly used search algorithms are variants of genetic algorithms. As an alternative choice, a direct search deterministic algorithm is proposed in this paper. For the simple simulations studied here, it outperforms the existing approaches. An additional algorithm is introduced in order to reveal some properties of the cost functional landscape.
Non-Linear Dynamics and Fundamental Interactions
Khanna, Faqir
2006-01-01
The book is directed to researchers and graduate students pursuing an advanced degree. It provides details of techniques directed towards solving problems in non-linear dynamics and chos that are, in general, not amenable to a perturbative treatment. The consideration of fundamental interactions is a prime example where non-perturbative techniques are needed. Extension of these techniques to finite temperature problems is considered. At present these ideas are primarily used in a perturbative context. However, non-perturbative techniques have been considered in some specific cases. Experts in the field on non-linear dynamics and chaos and fundamental interactions elaborate the techniques and provide a critical look at the present status and explore future directions that may be fruitful. The text of the main talks will be very useful to young graduate students who are starting their studies in these areas.
Fliess, Michel; Join, Cédric; Sira-Ramirez, Hebertt
2008-01-01
International audience; Non-linear state estimation and some related topics, like parametric estimation, fault diagnosis, and perturbation attenuation, are tackled here via a new methodology in numerical differentiation. The corresponding basic system theoretic definitions and properties are presented within the framework of differential algebra, which permits to handle system variables and their derivatives of any order. Several academic examples and their computer simulations, with on-line ...
Non-linear Loudspeaker Unit Modelling
DEFF Research Database (Denmark)
Pedersen, Bo Rohde; Agerkvist, Finn T.
2008-01-01
Simulations of a 6½-inch loudspeaker unit are performed and compared with a displacement measurement. The non-linear loudspeaker model is based on the major nonlinear functions and expanded with time-varying suspension behaviour and flux modulation. The results are presented with FFT plots of three...... frequencies and different displacement levels. The model errors are discussed and analysed including a test with loudspeaker unit where the diaphragm is removed....
Fliess, Michel; Sira-Ramirez, Hebertt
2007-01-01
Non-linear state estimation and some related topics, like parametric estimation, fault diagnosis, and perturbation attenuation, are tackled here via a new methodology in numerical differentiation. The corresponding basic system theoretic definitions and properties are presented within the framework of differential algebra, which permits to handle system variables and their derivatives of any order. Several academic examples and their computer simulations, with on-line estimations, are illustrating our viewpoint.
Institute of Scientific and Technical Information of China (English)
李岳林; 王立标; 曾志伟; 汤彬; 杜宝杰
2009-01-01
针对发动机非稳态工况,建立了非线性模型.设计了滑模控制器和PID控制器分别对节气门开度和点火时刻进行自适应调节.对发动机怠速工况进行仿真的结果表明,发动机转速波动幅值低于6 r/min,表明所建的非线性模型适合于发动机非稳态工况控制.%A non-linear model is set up for engine transient operating conditions. Both sliding mode control-ler and PID controller are designed to realize adaptive regulation of throttle opening and ignition timing respectively.The results of simulation on engine idling show that the amplitude of engine speed fluctuation is less than 6 r/min,indicating that the non-linear model built is appropriate for the control over the transient operating conditions of en-gine.
Celik, Yuksel; Ulker, Erkan
2013-01-01
Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
Optimal Genetic View Selection Algorithm for Data Warehouse
Institute of Scientific and Technical Information of China (English)
Wang Ziqiang; Feng Boqin
2005-01-01
To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.
Deterministic oscillatory search: a new meta-heuristic optimization algorithm
Indian Academy of Sciences (India)
N ARCHANA; R VIDHYAPRIYA; ANTONY BENEDICT; KARTHIK CHANDRAN
2017-06-01
The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms. Theresults show that the proposed algorithm is superior in terms of robustness and problem solving capabilities to other algorithms. The paradigm is also applied to an engineering problem to prove its practicality. It is applied to find the optimal location of multi-type FACTS devices in a power system and tested in the IEEE 39 bus system and UPSEB 75 bus system. Results show better performance over other standard algorithms in terms of voltage stability, real power loss and sizing and cost of FACTS devices.
Efficient Genetic Algorithm sets for optimizing constrained building design problem
National Research Council Canada - National Science Library
Wright, Jonathan; Alajmi, Ali
2016-01-01
.... This requires trying large possible solutions which need heuristic optimization algorithms. A comparison between several heuristic optimization algorithms showed that Genetic Algorithm (GA) is robust on getting the optimum(s) simulation ( Wetter and Wright, 2004; Brownlee et al., 2011; Bichiou and Krarti, 2011; Sahu et al., 2012 ) while the building simulat...
A Hybrid Algorithm for Optimizing Multi- Modal Functions
Institute of Scientific and Technical Information of China (English)
Li Qinghua; Yang Shida; Ruan Youlin
2006-01-01
A new genetic algorithm is presented based on the musical performance. The novelty of this algorithm is that a new genetic algorithm, mimicking the musical process of searching for a perfect state of harmony, which increases the robustness of it greatly and gives a new meaning of it in the meantime, has been developed. Combining the advantages of the new genetic algorithm, simplex algorithm and tabu search, a hybrid algorithm is proposed. In order to verify the effectiveness of the hybrid algorithm, it is applied to solving some typical numerical function optimization problems which are poorly solved by traditional genetic algorithms. The experimental results show that the hybrid algorithm is fast and reliable.
Enhanced Bee Colony Algorithm for Complex Optimization Problems
Directory of Open Access Journals (Sweden)
S.Suriya
2012-01-01
Full Text Available Optimization problems are considered to be one kind of NP hard problems. Usually heuristic approaches are found to provide solutions for NP hard problems. There are a plenty of heuristic algorithmsavailable to solve optimization problems namely: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, etc. The basic Bee Colony algorithm, a population based search algorithm, is analyzed to be a novel tool for complex optimization problems. The algorithm mimics the food foraging behavior of swarmsof honey bees. This paper deals with a modified fitness function of Bee Colony algorithm. The effect of problem dimensionality on the performance of the algorithms will be investigated. This enhanced Bee Colony Optimization will be evaluated based on the well-known benchmark problems. The testing functions like Rastrigin, Rosenbrock, Ackley, Griewank and Sphere are used to evaluavate the performance of the enhanced Bee Colony algorithm. The simulation will be developed on MATLAB.
Non-linear aeroelastic prediction for aircraft applications
de C. Henshaw, M. J.; Badcock, K. J.; Vio, G. A.; Allen, C. B.; Chamberlain, J.; Kaynes, I.; Dimitriadis, G.; Cooper, J. E.; Woodgate, M. A.; Rampurawala, A. M.; Jones, D.; Fenwick, C.; Gaitonde, A. L.; Taylor, N. V.; Amor, D. S.; Eccles, T. A.; Denley, C. J.
2007-05-01
Current industrial practice for the prediction and analysis of flutter relies heavily on linear methods and this has led to overly conservative design and envelope restrictions for aircraft. Although the methods have served the industry well, it is clear that for a number of reasons the inclusion of non-linearity in the mathematical and computational aeroelastic prediction tools is highly desirable. The increase in available and affordable computational resources, together with major advances in algorithms, mean that non-linear aeroelastic tools are now viable within the aircraft design and qualification environment. The Partnership for Unsteady Methods in Aerodynamics (PUMA) Defence and Aerospace Research Partnership (DARP) was sponsored in 2002 to conduct research into non-linear aeroelastic prediction methods and an academic, industry, and government consortium collaborated to address the following objectives: To develop useable methodologies to model and predict non-linear aeroelastic behaviour of complete aircraft. To evaluate the methodologies on real aircraft problems. To investigate the effect of non-linearities on aeroelastic behaviour and to determine which have the greatest effect on the flutter qualification process. These aims have been very effectively met during the course of the programme and the research outputs include: New methods available to industry for use in the flutter prediction process, together with the appropriate coaching of industry engineers. Interesting results in both linear and non-linear aeroelastics, with comprehensive comparison of methods and approaches for challenging problems. Additional embryonic techniques that, with further research, will further improve aeroelastics capability. This paper describes the methods that have been developed and how they are deployable within the industrial environment. We present a thorough review of the PUMA aeroelastics programme together with a comprehensive review of the relevant research
Solving non-linear Horn clauses using a linear Horn clause solver
DEFF Research Database (Denmark)
Kafle, Bishoksan; Gallagher, John Patrick; Ganty, Pierre
2016-01-01
In this paper we show that checking satisfiability of a set of non-linear Horn clauses (also called a non-linear Horn clause program) can be achieved using a solver for linear Horn clauses. We achieve this by interleaving a program transformation with a satisfiability checker for linear Horn...... clauses (also called a solver for linear Horn clauses). The program transformation is based on the notion of tree dimension, which we apply to a set of non-linear clauses, yielding a set whose derivation trees have bounded dimension. Such a set of clauses can be linearised. The main algorithm...
Niching genetic algorithms for optimization in electromagnetics - I. Fundamentals
Sareni, Bruno; Krähenbühl, Laurent; Nicolas, Alain
1998-01-01
Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization.
A Hybrid Aggressive Space Mapping Algorithm for EM Optimization
DEFF Research Database (Denmark)
Bakr, M.; Bandler, J. W.; Georgieva, N.;
1999-01-01
We present a novel, Hybrid Aggressive Space Mapping (HASM) optimization algorithm. HASM is a hybrid approach exploiting both the Trust Region Aggressive Space Mapping (TRASM) algorithm and direct optimization. It does not assume that the final space-mapped design is the true optimal design and is...
PDE Nozzle Optimization Using a Genetic Algorithm
Billings, Dana; Turner, James E. (Technical Monitor)
2000-01-01
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.
Evolutionary Algorithm Geometry Optimization of Optical Antennas
Directory of Open Access Journals (Sweden)
Ramón Díaz de León-Zapata
2016-01-01
Full Text Available Printed circuit antennas have been used for the detection of electromagnetic radiation at a wide range of frequencies that go from radio frequencies (RF up to optical frequencies. The design of printed antennas at optical frequencies has been done by using design rules derived from the radio frequency domain which do not take into account the dispersion of material parameters at optical frequencies. This can make traditional RF antenna design not suitable for optical antenna design. This work presents the results of using a genetic algorithm (GA for obtaining an optimized geometry (unconventional geometries that may be used as optical regime antennas to capture electromagnetic waves. The radiation patterns and optical properties of the GA generated geometries were compared with the conventional dipole geometry. The characterizations were conducted via finite element method (FEM computational simulations.
DEFF Research Database (Denmark)
Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar
2017-01-01
, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site...... and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm...... optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality....
Artificial bee colony algorithm variants on constrained optimization
National Research Council Canada - National Science Library
Bahriye Akay; Dervis Karaboga
2017-01-01
.... In this study, the performance analysis of artificial bee colony algorithm (ABC), one of the intelligent optimization techniques, is examined on constrained problems and the effect of some modifications on the performance of the algorithm is examined...
Efficent Estimation of the Non-linear Volatility and Growth Model
2009-01-01
Ramey and Ramey (1995) introduced a non-linear model relating volatility to growth. The solution of this model by generalised computer algorithms for non-linear maximum likelihood estimation encounters the usual difficulties and is, at best, tedious. We propose an algebraic solution for the model that provides fully efficient estimators and is elementary to implement as a standard ordinary least squares procedure. This eliminates issues such as the ‘guesstimation’ of initial values and mul...
Non Linear Behaviour in Learning Processes
Manfredi, Paolo; Manfredi, Vicenzo Rosario
2003-01-01
This article is mainly based on R. E. Kahn's contribution to the book Non Linear Dynamics in Human Behavior. As stressed by Bronowski, both in art and in science, a person becomes creative by finding "a new unity" that is a link between things which were not thought alike before. Indeed the creative mind is a mind that looks for unexpected likeness finding a more profound unity, a pattern behind chaotic phenomena. In the context of scientific discovery, it can also be argued that creativi...
BRST structure of non-linear superalgebras
Asorey, M; Radchenko, O V; Sugamoto, A
2008-01-01
In this paper we analyse the structure of the BRST structure of nonlinear superalgebras. We consider quadratic non-linear superalgebras where a commutator (in terms of (super) Poisson brackets) of the generators is a quadratic polynomial of the generators. We find the explicit form of the BRST charge up to cubic order in Faddeev-Popov ghost fields for arbitrary quadratic nonlinear superalgebras. We point out the existence of constraints on structure constants of the superalgebra when the nilpotent BRST charge is quadratic in Faddeev-Popov ghost fields. The general results are illustrated by simple examples of superalgebras.
Limits on Non-Linear Electrodynamics
Fouché, M; Rizzo, C
2016-01-01
In this paper we set a framework in which experiments whose goal is to test QED predictions can be used in a more general way to test non-linear electrodynamics (NLED) which contains low-energy QED as a special case. We review some of these experiments and we establish limits on the different free parameters by generalizing QED predictions in the framework of NLED. We finally discuss the implications of these limits on bound systems and isolated charged particles for which QED has been widely and successfully tested.
Institute of Scientific and Technical Information of China (English)
罗袆青; 袁希钢
2008-01-01
The problem of optimal synthesis of an integrated water system is addressed in this study, where water using processes and water treatment operations are combined into a single network such that the total cost of fresh water and wastewater treatment is globally minimized. A superstructure that incorporates all feasible design alterna-fives for wastewater treatment, reuse and recycle, is synthesized with a non-linear programming model. An evolu-tionary approach--an improved particle swarm optimization is proposed for optimizing such systems. Two simple examples are presented to illustrate the global optimization of integrated water networks using the proposed algorithm.
An Adaptive Non-Linear Map and Its Application
Institute of Scientific and Technical Information of China (English)
YAN Xuefeng
2006-01-01
A novel adaptive non-linear mapping (ANLM),integrating an adaptive mapping error (AME) with a chaosgenetic algorithm (CGA) including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping (NLM). The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodically in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimensional olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.
Genetic algorithm and particle swarm optimization combined with Powell method
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
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.
Practice Utilization of Algorithms for Analog Filter Group Delay Optimization
Directory of Open Access Journals (Sweden)
K. Hajek
2007-04-01
Full Text Available This contribution deals with an application of three different algorithms which optimize a group delay of analog filters. One of them is a part of the professional circuit simulator Micro Cap 7 and the others two original algorithms are developed in the MATLAB environment. An all-pass network is used to optimize the group delay of an arbitrary analog filter. Introduced algorithms look for an optimal order and optimal coefficients of an all-pass network transfer function. Theoretical foundations are introduced and all algorithms are tested using the optimization of testing analog filter. The optimization is always run three times because the second, third and fourth-order all-pass network is used. An equalization of the original group delay is a main objective of these optimizations. All outputs of all algorithms are critically evaluated and also described.
Optimal Pid Controller Design Using Adaptive Vurpso Algorithm
Zirkohi, Majid Moradi
2015-04-01
The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.
Adaptive Central Force Optimization Algorithm Based on the Stability Analysis
Directory of Open Access Journals (Sweden)
Weiyi Qian
2015-01-01
Full Text Available In order to enhance the convergence capability of the central force optimization (CFO algorithm, an adaptive central force optimization (ACFO algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.
Automatic design of synthetic gene circuits through mixed integer non-linear programming.
Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias
2012-01-01
Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits.
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
2014-07-01
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.
Accelerating ATM Optimization Algorithms Using High Performance Computing Hardware Project
National Aeronautics and Space Administration — NASA is developing algorithms and methodologies for efficient air-traffic management (ATM). Several researchers have adopted an optimization framework for solving...
An Effective Hybrid Optimization Algorithm for Capacitated Vehicle Routing Problem
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high computational complexity. A hybrid algorithm was developed to solve the problem, in which an artificial immune clonal algorithm (AICA) makes use of the global search ability to search the optimal results and simulated annealing (SA) algorithm employs certain probability to avoid becoming trapped in a local optimum. The results obtained from the computational study show that the proposed algorithm is a feasible and effective method for capacitated vehicle routing problem.
Accelerating ATM Optimization Algorithms Using High Performance Computing Hardware Project
National Aeronautics and Space Administration — NASA is developing algorithms and methodologies for efficient air-traffic management. Several researchers have adopted an optimization framework for solving problems...
Improved symbiotic organisms search algorithm for solving unconstrained function optimization
Directory of Open Access Journals (Sweden)
Sukanta Nama
2016-09-01
Full Text Available Recently, Symbiotic Organisms Search (SOS algorithm is being used for solving complex problems of optimization. This paper proposes an Improved Symbiotic Organisms Search (I-SOS algorithm for solving different complex unconstrained global optimization problems. In the improved algorithm, a random weighted reflective parameter and predation phase are suggested to enhance the performance of the algorithm. The performances of this algorithm are compared with the other state-of-the-art algorithms. The parametric study of the common control parameter has also been performed.
Composite multiobjective optimization beamforming based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
Shi Jing; Meng Weixiao; Zhang Naitong; Wang Zheng
2006-01-01
All thc parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms (GAs).Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.
Variational iteration method for solving non-linear partial differential equations
Energy Technology Data Exchange (ETDEWEB)
Hemeda, A.A. [Department of Mathematics, Faculty of Science, University of Tanta, Tanta (Egypt)], E-mail: aahemeda@yahoo.com
2009-02-15
In this paper, we shall use the variational iteration method to solve some problems of non-linear partial differential equations (PDEs) such as the combined KdV-MKdV equation and Camassa-Holm equation. The variational iteration method is superior than the other non-linear methods, such as the perturbation methods where this method does not depend on small parameters, such that it can fined wide application in non-linear problems without linearization or small perturbation. In this method, the problems are initially approximated with possible unknowns, then a correction functional is constructed by a general Lagrange multiplier, which can be identified optimally via the variational theory.
Function Optimization Based on Quantum Genetic Algorithm
Ying Sun; Yuesheng Gu; Hegen Xiong
2013-01-01
Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on.It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed ,which is called variable-boundary-coded quantum genetic algorithm (vbQGA) in which qubit chromosomes are collapsed into variableboundary- coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained.The m...
Manning, Robert M.
1991-01-01
The dynamic and composite nature of propagation impairments that are incurred on Earth-space communications links at frequencies in and above 30/20 GHz Ka band, i.e., rain attenuation, cloud and/or clear air scintillation, etc., combined with the need to counter such degradations after the small link margins have been exceeded, necessitate the use of dynamic statistical identification and prediction processing of the fading signal in order to optimally estimate and predict the levels of each of the deleterious attenuation components. Such requirements are being met in NASA's Advanced Communications Technology Satellite (ACTS) Project by the implementation of optimal processing schemes derived through the use of the Rain Attenuation Prediction Model and nonlinear Markov filtering theory.
Chebrakov, Yu. V.
2014-01-01
In this paper we discuss techniques suitable for translating the verbal descriptions of computative algorithms into a set of mathematical formulae and demonstrate that logical functions can be used eﬀectively in order to create non-linear analytical formulae, describing a set of combinatorial and number-theoretic computative algorithms.
Non-Linear Dynamics of Saturn's Rings
Esposito, L. W.
2015-12-01
Non-linear processes can explain why Saturn's rings are so active and dynamic. Some of this non-linearity is captured in a simple Predator-Prey Model: Periodic forcing from the moon causes streamline crowding; This damps the relative velocity, and allows aggregates to grow. About a quarter phase later, the aggregates stir the system to higher relative velocity and the limit cycle repeats each orbit, with relative velocity ranging from nearly zero to a multiple of the orbit average: 2-10x is possible. Summary of Halo Results: A predator-prey model for ring dynamics produces transient structures like 'straw' that can explain the halo structure and spectroscopy: Cyclic velocity changes cause perturbed regions to reach higher collision speeds at some orbital phases, which preferentially removes small regolith particles; Surrounding particles diffuse back too slowly to erase the effect: this gives the halo morphology; This requires energetic collisions (v ≈ 10m/sec, with throw distances about 200km, implying objects of scale R ≈ 20km); We propose 'straw', as observed ny Cassini cameras. Transform to Duffing Eqn : With the coordinate transformation, z = M2/3, the Predator-Prey equations can be combined to form a single second-order differential equation with harmonic resonance forcing. Ring dynamics and history implications: Moon-triggered clumping at perturbed regions in Saturn's rings creates both high velocity dispersion and large aggregates at these distances, explaining both small and large particles observed there. This confirms the triple architecture of ring particles: a broad size distribution of particles; these aggregate into temporary rubble piles; coated by a regolith of dust. We calculate the stationary size distribution using a cell-to-cell mapping procedure that converts the phase-plane trajectories to a Markov chain. Approximating the Markov chain as an asymmetric random walk with reflecting boundaries allows us to determine the power law index from
Algorithm for correcting optimization convergence errors in Eclipse.
Zacarias, Albert S; Mills, Michael D
2009-10-14
IMRT plans generated in Eclipse use a fast algorithm to evaluate dose for optimization and a more accurate algorithm for a final dose calculation, the Analytical Anisotropic Algorithm. The use of a fast optimization algorithm introduces optimization convergence errors into an IMRT plan. Eclipse has a feature where optimization may be performed on top of an existing base plan. This feature allows for the possibility of arriving at a recursive solution to optimization that relies on the accuracy of the final dose calculation algorithm and not the optimizer algorithm. When an IMRT plan is used as a base plan for a second optimization, the second optimization can compensate for heterogeneity and modulator errors in the original base plan. Plans with the same field arrangement as the initial base plan may be added together by adding the initial plan optimal fluence to the dose correcting plan optimal fluence.A simple procedure to correct for optimization errors is presented that may be implemented in the Eclipse treatment planning system, along with an Excel spreadsheet to add optimized fluence maps together.
Algorithmic Aspects of Several Data Transfer Service Optimization Problems
Andreica, Mugurel Ionut; Ionescu, Florin; Andreica, Cristina Teodora
2009-01-01
Optimized data transfer services are highly demanded nowadays, due to the large amounts of data which are frequently being produced and accessed. In this paper we consider several data transfer service optimization problems (optimal server location in path networks, optimal packet sequencing and minimum makespan packet scheduling), for which we provide novel algorithmic solutions.
A supervised machine learning estimator for the non-linear matter power spectrum - SEMPS
Mohammed, Irshad
2015-01-01
In this article, we argue that models based on machine learning (ML) can be very effective in estimating the non-linear matter power spectrum ($P(k)$). We employ the prediction ability of the supervised ML algorithms to build an estimator for the $P(k)$. The estimator is trained on a set of cosmological models, and redshifts for which the $P(k)$ is known, and it learns to predict $P(k)$ for any other set. We review three ML algorithms -- Random Forest, Gradient Boosting Machines, and K-Nearest Neighbours -- and investigate their prime parameters to optimize the prediction accuracy of the estimator. We also compute an optimal size of the training set, which is realistic enough, and still yields high accuracy. We find that, employing the optimal values of the internal parameters, a set of $50-100$ cosmological models is enough to train the estimator that can predict the $P(k)$ for a wide range of cosmological models, and redshifts. Using this configuration, we build a blackbox -- Supervised Estimator for Matter...
Optimization of machining processes using pattern search algorithm
Miloš Madić; Miroslav Radovanović
2014-01-01
Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers a...
Automatic Circuit Design and Optimization Using Modified PSO Algorithm
Directory of Open Access Journals (Sweden)
Subhash Patel
2016-04-01
Full Text Available In this work, we have proposed modified PSO algorithm based optimizer for automatic circuit design. The performance of the modified PSO algorithm is compared with two other evolutionary algorithms namely ABC algorithm and standard PSO algorithm by designing two stage CMOS operational amplifier and bulk driven OTA in 130nm technology. The results show the robustness of the proposed algorithm. With modified PSO algorithm, the average design error for two stage op-amp is only 0.054% in contrast to 3.04% for standard PSO algorithm and 5.45% for ABC algorithm. For bulk driven OTA, average design error is 1.32% with MPSO compared to 4.70% with ABC algorithm and 5.63% with standard PSO algorithm.
Chaotic Discrimination and Non-Linear Dynamics
Directory of Open Access Journals (Sweden)
Partha Gangopadhyay
2005-01-01
Full Text Available This study examines a particular form of price discrimination, known as chaotic discrimination, which has the following features: sellers quote a common price but, in reality, they engage in secret and apparently unsystematic price discounts. It is widely held that such forms of price discrimination are seriously inconsistent with profit maximization by sellers.. However, there is no theoretical salience to support this kind of price discrimination. By straining the logic of non-linear dynamics this study explains why such secret discounts are chaotic in the sense that sellers fail to adopt profit-maximising price discounts. A model is developed to argue that such forms of discrimination may derive from the regions of instability of a dynamic model of price discounts.
Symmetries in Non-Linear Mechanics
Aldaya, Victor; López-Ruiz, Francisco F; Cossío, Francisco
2014-01-01
In this paper we exploit the use of symmetries of a physical system so as to characterize the corresponding solution manifold by means of Noether invariants. This constitutes a necessary preliminary step towards the correct quantisation in non-linear cases, where the success of Canonical Quantisation is not guaranteed in general. To achieve this task "point symmetries" of the Lagrangian are generally not enough, and the notion of contact transformations is in order. The use of the Poincar\\'e-Cartan form permits finding both the symplectic structure on the solution manifold, through the Hamilton-Jacobi transformation, and the required symmetries, realized as Hamiltonian vector fields, associated with functions on the solution manifold (thus constituting an inverse of the Noether Theorem), lifted back to the evolution space through the inverse of this Hamilton-Jacobi mapping. In this framework, solutions and symmetries are somehow identified and this correspondence is also kept at a perturbative level. We prese...
Risks of non-linear climate change
Energy Technology Data Exchange (ETDEWEB)
Van Ham, J.; Van Beers, R.J.; Builtjes, P.J.H.; Koennen, G.P.; Oerlemans, J.; Roemer, M.G.M. [TNO-SCMO, Delft (Netherlands)
1995-12-31
Climate forcing as a result of increased concentrations of greenhouse gases has been primarily addressed as a problem of a possibly warmer climate. So far, such change has been obscured in observations, possibly as a result of natural climate variability and masking by aerosols. Consequently, projections of the effect of climate forcing have to be based on modelling, more specifically by applying Global Circulation Models GCMs. These GCMs do not cover all possible feedbacks; neither do they address all specific possible effects of climate forcing. The investigation reviews possible non-linear climate change which does not fall within the coverage of present GCMs. The review includes the potential relevance of changes in biogeochemical cycles, aerosol and cloud feedback, albedo instability, ice-flow instability, changes in the thermohaline circulation and changes resulting from stratospheric cooling. It is noted that these changes may have different time horizons. Three from the investigated issues provide indications for a possible non-linear change. On the decadal scale stratospheric cooling, which is the result of the enhanced greenhouse effect, in combination with a depleted ozone layer, could provide a positive feedback to further ozone depletion, in particular in the Arctic. Decreasing albedo on the Greenland ice sheet may enhance the runoff from this ice sheet significantly in case of warming on a timescale of a few centuries. Changes in ocean circulation in the North Atlantic could seasonally more than compensate a global warming of 3C in North-West Europe on a timescale of centuries to a millennium. 263 refs.
Institute of Scientific and Technical Information of China (English)
周泽民; 曾新吾; 龚昌超; 田章福; 孙海洋
2013-01-01
针对调制气流声源存在较强的谐波畸变，将声源系统等效为 Hammerstein 非线性模型，利用该模型下的预失真技术对声源进行非线性补偿研究。根据辨识的 Hammerstein 模型中静态非线性部分带有直流分量的特点，给出了考虑直流分量补偿的预失真算法，并用数值仿真验证了算法的准确性和直流分量补偿的必要性。在非线性补偿实验中，根据单频信号辨识得到 Hammerstein 模型参数，采用 NFxPEM算法求得对应的预失真 Wiener 模型参数和预失真波形。实验结果表明，与直接发射相比，补偿发射后声波的功率谱中谐波能量有所下降，而基频能量有小幅度的上升，说明了研究思路的正确性。%Aimed at the harmonic distortion problem in the air-modulated speaker(AMS),the AMS behavioral model was represented by a Hammerstein structure,and the research on predistortion of AMS based on this model was made.As the DC offset exists in the nonlinearity of the Hammerstein model,a predistortion algorithm considering the DC offset compensation was developed.The validity of the algorithm and the necessity of the DC offset compensation were verified by computer simulation.In the experiment,a single sinusoidal excitation signal was first used to identify the Hammerstein model.Then,using the identified system parameters,the NFxPEMalgorithm was performed to obtain the parameters of Wiener predistorter and to predistort the excitation signal.From the experiment results,it is found that our approach is effective in reducing the harmonic power with a relatively small upgrade in the fundamental frequency power.
Application of Bees Algorithm in Multi-Join Query Optimization
Directory of Open Access Journals (Sweden)
Mohammad Alamery
2012-09-01
Full Text Available Multi-join query optimization is an important technique for designing and implementing database management system. It is a crucial factor that affects the capability of database. This paper proposes a Bees algorithm that simulates the foraging behavior of honey bee swarm to solve Multi-join query optimization problem. The performance of the Bees algorithm and Ant Colony Optimization algorithm are compared with respect to computational time and the simulation result indicates that Bees algorithm is more effective and efficient.
General Object-oriented Framework for Iterative Optimization Algorithms
Mornar, Vedran; Vanjak, Zvonimir
2001-01-01
It is usually impossible to exactly solve the hard optimization problems. One is thus directed to iterative algorithms. In implementation of these iterative algorithms, some common characteristics can be observed, which can be generalized in an object-oriented framework. This can significantly reduce the time needed for implementation of an iterative algorithm.
Biology-Derived Algorithms in Engineering Optimization
Yang, Xin-She
2010-01-01
Biology-derived algorithms are an important part of computational sciences, which are essential to many scientific disciplines and engineering applications. Many computational methods are derived from or based on the analogy to natural evolution and biological activities, and these biologically inspired computations include genetic algorithms, neural networks, cellular automata, and other algorithms.
Hybrid Algorithm for Optimal Load Sharing in Grid Computing
Directory of Open Access Journals (Sweden)
A. Krishnan
2012-01-01
Full Text Available Problem statement: Grid Computing is the fast growing industry, which shares the resources in the organization in an effective manner. Resource sharing requires more optimized algorithmic structure, otherwise the waiting time and response time are increased and the resource utilization is reduced. Approach: In order to avoid such reduction in the performances of the grid system, an optimal resource sharing algorithm is required. In recent days, many load sharing technique are proposed, which provides feasibility but there are many critical issues are still present in these algorithms. Results: In this study a hybrid algorithm for optimization of load sharing is proposed. The hybrid algorithm contains two components which are Hash Table (HT and Distributed Hash Table (DHT. Conclusion: The results of the proposed study show that the hybrid algorithm will optimize the task than existing systems.
Teaching learning based optimization algorithm and its engineering applications
Rao, R Venkata
2016-01-01
Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.
A FUZZY CLOPE ALGORITHM AND ITS OPTIMAL PARAMETER CHOICE
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm directly affects the validity of the clustering results, which is still an open issue. For this purpose, this paper proposes a fuzzy CLOPE algorithm, and presents a method for the optimal parameter choice by defining a modified partition fuzzy degree as a clustering validity function. The experimental results with real data set illustrate the effectiveness of the proposed fuzzy CLOPE algorithm and optimal parameter choice method based on the modified partition fuzzy degree.
A NEW FAMILY OF TRUST REGION ALGORITHMS FOR UNCONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Yuhong Dai; Dachuan Xu
2003-01-01
Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously. differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.
Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem
Rahmalia, Dinita
2017-08-01
Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.
An optimal scheduling algorithm based on task duplication
Institute of Scientific and Technical Information of China (English)
Ruan Youlin; Liu Gan; Zhu Guangxi; Lu Xiaofeng
2005-01-01
When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O ( v2 ), where v represents the number of tasks.
Earth Observation Satellites Scheduling Based on Decomposition Optimization Algorithm
Directory of Open Access Journals (Sweden)
Feng Yao
2010-11-01
Full Text Available A decomposition-based optimization algorithm was proposed for solving Earth Observation Satellites scheduling problem. The problem was decomposed into task assignment main problem and single satellite scheduling sub-problem. In task assignment phase, the tasks were allocated to the satellites, and each satellite would schedule the task respectively in single satellite scheduling phase. We adopted an adaptive ant colony optimization algorithm to search the optimal task assignment scheme. Adaptive parameter adjusting strategy and pheromone trail smoothing strategy were introduced to balance the exploration and the exploitation of search process. A heuristic algorithm and a very fast simulated annealing algorithm were proposed to solve the single satellite scheduling problem. The task assignment scheme was valued by integrating the observation scheduling result of multiple satellites. The result was responded to the ant colony optimization algorithm, which can guide the search process of ant colony optimization. Computation results showed that the approach was effective to the satellites observation scheduling problem.
Transonic Wing Shape Optimization Using a Genetic Algorithm
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
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.
A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization
Directory of Open Access Journals (Sweden)
Soroor Sarafrazi
2015-07-01
Full Text Available It is now well recognized that pure algorithms can be promisingly improved by hybridization with other techniques. One of the relatively new metaheuristic algorithms is Gravitational Search Algorithm (GSA which is based on the Newton laws. In this paper, to enhance the performance of GSA, a novel algorithm called “Kepler”, inspired by the astrophysics, is introduced. The Kepler algorithm is based on the principle of the first Kepler law. The hybridization of GSA and Kepler algorithm is an efficient approach to provide much stronger specialization in intensification and/or diversification. The performance of GSA–Kepler is evaluated by applying it to 14 benchmark functions with 20–1000 dimensions and the optimal approximation of linear system as a practical optimization problem. The results obtained reveal that the proposed hybrid algorithm is robust enough to optimize the benchmark functions and practical optimization problems.
Directory of Open Access Journals (Sweden)
M. Balasubbareddy
2015-12-01
Full Text Available A novel optimization algorithm is proposed to solve single and multi-objective optimization problems with generation fuel cost, emission, and total power losses as objectives. The proposed method is a hybridization of the conventional cuckoo search algorithm and arithmetic crossover operations. Thus, the non-linear, non-convex objective function can be solved under practical constraints. The effectiveness of the proposed algorithm is analyzed for various cases to illustrate the effect of practical constraints on the objectives' optimization. Two and three objective multi-objective optimization problems are formulated and solved using the proposed non-dominated sorting-based hybrid cuckoo search algorithm. The effectiveness of the proposed method in confining the Pareto front solutions in the solution region is analyzed. The results for single and multi-objective optimization problems are physically interpreted on standard test functions as well as the IEEE-30 bus test system with supporting numerical and graphical results and also validated against existing methods.
Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen
2006-01-01
This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm,an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.
Non-linear optical titanyl arsenates: Crystal growth and properties
Nordborg, Jenni Eva Louise
Crystals are appreciated not only for their appearance, but also for their unique physical properties which are utilized by the photonic industry in appliances that we come across every day. An important part of enabling the technical use of optical devices is the manufacture of crystals. This dissertation deals with a specific group of materials called the potassium titanyl phosphate (KIP) family, known for their non-linear optical and ferroelectric properties. The isomorphs vary in their linear optical and dielectric properties, which can be tuned to optimize device performance by forming solid solutions of the different materials. Titanyl arsenates have a wide range of near-infrared transmission which makes them useful for tunable infrared lasers. The isomorphs examined in the present work were primarily RbTiOASO4 (RTA) and CsTiOAsO4 (CTA) together with the mixtures RbxCs 1-xTiOAsO4 (RCTA). Large-scale crystals were grown by top seeding solution growth utilizing a three-zone furnace with excellent temperature control. Sufficiently slow cooling and constant upward lifting produced crystals with large volumes useable for technical applications. Optical quality RTA crystals up to 10 x 12 x 20 mm were grown. The greater difficulty in obtaining good crystals of CTA led to the use of mixed RCTA materials. The mixing of rubidium and cesium in RCTA is more favorable to crystal growth than the single components in pure RTA and CTA. Mixed crystals are rubidium-enriched and contain only 20-30% of the cesium concentration in the flux. The cesium atoms show a preference for the larger cation site. The network structure is very little affected by the cation substitution; consequently, the non-linear optical properties of the Rb-rich isomorphic mixtures of RTA and CTA can be expected to remain intact. Crystallographic methods utilizing conventional X-ray tubes, synchrotron radiation and neutron diffraction have been employed to investigate the properties of the atomic
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
Directory of Open Access Journals (Sweden)
Luiz Augusto da Cruz Meleiro
2005-06-01
Full Text Available In this work a MIMO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by two MISO Functional Link Networks (FLNs, identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presents as advantages fast training and guaranteed convergence, since the estimation of the weights is a linear optimization problem. Besides, the elimination of non-significant weights generates parsimonious models, which allows for fast execution in an MPC-based algorithm. The proposed algorithm showed good potential in identification and control of non-linear processes.Neste trabalho um controlador preditivo não linear multivariável foi desenvolvido para um processo de fermentação alcoólica extrativa. O modelo interno do controlador foi representado por duas redes do tipo Functional Link (FLN, identificadas usando dados de simulação gerados a partir de um modelo validado experimentalmente. A estrutura FLN apresenta como vantagem o treinamento rápido e convergência garantida, já que a estimação dos seus pesos é um problema de otimização linear. Além disso, a eliminação de pesos não significativos gera modelos parsimoniosos, o que permite a rápida execução em algoritmos de controle preditivo baseado em modelo. Os resultados mostram que o algoritmo proposto tem grande potencial para identificação e controle de processos não lineares.
Resource Allocation in Public Cluster with Extended Optimization Algorithm
Akbar, Z.; Handoko, L. T.
2007-01-01
We introduce an optimization algorithm for resource allocation in the LIPI Public Cluster to optimize its usage according to incoming requests from users. The tool is an extended and modified genetic algorithm developed to match specific natures of public cluster. We present a detail analysis of optimization, and compare the results with the exact calculation. We show that it would be very useful and could realize an automatic decision making system for public clusters.
Genetic-Algorithm Tool For Search And Optimization
Wang, Lui; Bayer, Steven
1995-01-01
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.
TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization
2016-11-28
magnitude in computational experiments on portfolio optimization problems. The research on this topic has been published as [CG15a], where details can...AFRL-AFOSR-UK-TR-2017-0001 TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization Horst Hamacher Technische Universität...To) 15 May 2013 to 12 May 2016 4. TITLE AND SUBTITLE TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization 5a. CONTRACT
SNMP Based Network Optimization Technique Using Genetic Algorithms
Directory of Open Access Journals (Sweden)
M. Mohamed Surputheen
2012-03-01
Full Text Available Genetic Algorithms (GAs has innumerable applications through the optimization techniques and network optimization is one of them. SNMP (Simple Network Management Protocol is used as the basic network protocol for monitoring the network activities health of the systems. This paper deals with adding Intelligence to the various aspects of SNMP by adding optimization techniques derived out of genetic algorithms, which enhances the performance of SNMP processes like routing.
Non-linear Kalman filters for calibration in radio interferometry
Tasse, Cyril
2014-01-01
We present a new calibration scheme based on a non-linear version of Kalman filter that aims at estimating the physical terms appearing in the Radio Interferometry Measurement Equation (RIME). We enrich the filter's structure with a tunable data representation model, together with an augmented measurement model for regularization. We show using simulations that it can properly estimate the physical effects appearing in the RIME. We found that this approach is particularly useful in the most extreme cases such as when ionospheric and clock effects are simultaneously present. Combined with the ability to provide prior knowledge on the expected structure of the physical instrumental effects (expected physical state and dynamics), we obtain a fairly cheap algorithm that we believe to be robust, especially in low signal-to-noise regime. Potentially the use of filters and other similar methods can represent an improvement for calibration in radio interferometry, under the condition that the effects corrupting visib...
Computational models of signalling networks for non-linear control.
Fuente, Luis A; Lones, Michael A; Turner, Alexander P; Stepney, Susan; Caves, Leo S; Tyrrell, Andy M
2013-05-01
Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.
Parameters Optimization of Plasma Hardening Process Using Genetic Algorithm and Neural Network
Institute of Scientific and Technical Information of China (English)
LIU Gu; WANG Liu-ying; CHEN Gui-ming; HUA Shao-chun
2011-01-01
Plasma surface hardening process was performed to improve the performance of the AISI 1045 carbon steel.Experiments were carried out to characterize the hardening qualities.A predicting and optimizing model using genetic algorithm-back propagation neural network（GA-BP） was developed based on the experimental results.The non-linear relationship between properties of hardening layers and process parameters was established.The results show that the GA-BP predicting model is reliable since prediction results are in rather good agreement with measured results.The optimal properties of the hardened layer were deduced from GA.And through multi optimizations,the optimum comprehensive performances of the hardened layer were as follows：plasma arc current is 90 A,hardening speed is 2.2 m/min,plasma gas flow rate is 6.0 L/min and hardening distance is 4.3 mm.It concludes that GA-BP mode developed in this study provides a promising method for plasma hardening parameters prediction and optimization.
Non-Linear Sigma Model on Conifolds
Parthasarathy, R
2002-01-01
Explicit solutions to the conifold equations with complex dimension $n=3,4$ in terms of {\\it{complex coordinates (fields)}} are employed to construct the Ricci-flat K\\"{a}hler metrics on these manifolds. The K\\"{a}hler 2-forms are found to be closed. The complex realization of these conifold metrics are used in the construction of 2-dimensional non-linear sigma model with the conifolds as target spaces. The action for the sigma model is shown to be bounded from below. By a suitable choice of the 'integration constants', arising in the solution of Ricci flatness requirement, the metric and the equations of motion are found to be {\\it{non-singular}}. As the target space is Ricci flat, the perturbative 1-loop counter terms being absent, the model becomes topological. The inherent U(1) fibre over the base of the conifolds is shown to correspond to a gauge connection in the sigma model. The same procedure is employed to construct the metric for the resolved conifold, in terms of complex coordinates and the action ...
Non-Linear Electrohydrodynamics in Microfluidic Devices
Directory of Open Access Journals (Sweden)
Jun Zeng
2011-03-01
Full Text Available Since the inception of microfluidics, the electric force has been exploited as one of the leading mechanisms for driving and controlling the movement of the operating fluid and the charged suspensions. Electric force has an intrinsic advantage in miniaturized devices. Because the electrodes are placed over a small distance, from sub-millimeter to a few microns, a very high electric field is easy to obtain. The electric force can be highly localized as its strength rapidly decays away from the peak. This makes the electric force an ideal candidate for precise spatial control. The geometry and placement of the electrodes can be used to design electric fields of varying distributions, which can be readily realized by Micro-Electro-Mechanical Systems (MEMS fabrication methods. In this paper, we examine several electrically driven liquid handling operations. The emphasis is given to non-linear electrohydrodynamic effects. We discuss the theoretical treatment and related numerical methods. Modeling and simulations are used to unveil the associated electrohydrodynamic phenomena. The modeling based investigation is interwoven with examples of microfluidic devices to illustrate the applications.
A danger-theory-based immune network optimization algorithm.
Zhang, Ruirui; Li, Tao; Xiao, Xin; Shi, Yuanquan
2013-01-01
Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.
A New Algorithm for Generalized Optimal Discriminant Vectors
Institute of Scientific and Technical Information of China (English)
吴小俊; 杨静宇; 王士同; 郭跃飞; 曹奇英
2002-01-01
A study has been conducted on the algorithm of solving generalized optimal set of discriminant vectors in this paper. This paper proposes an analytical algorithm of solving generalized optimal set of discriminant vectors theoretically for the first time. A lot of computation time can be saved because all the generalized optimal sets of discriminant vectors can be obtained simultaneously with the proposed algorithm, while it needs no iterative operations. The proposed algorithm can yield a much higher recognition rate. Furthermore,the proposed algorithm overcomes the shortcomings of conventional human face recognition algorithms which were effective for small sample size problems only. These statements are supported by the numerical simulation experiments on facial database of ORL.
Decoherence in optimized quantum random-walk search algorithm
Zhang, Yu-Chao; Bao, Wan-Su; Wang, Xiang; Fu, Xiang-Qun
2015-08-01
This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative. Project supported by the National Basic Research Program of China (Grant No. 2013CB338002).
A Multiobjective Optimization Algorithm Based on Discrete Bacterial Colony Chemotaxis
Directory of Open Access Journals (Sweden)
Zhigang Lu
2014-01-01
Full Text Available Bacterial colony chemotaxis algorithm was originally developed for optimal problem with continuous space. In this paper the discrete bacterial colony chemotaxis (DBCC algorithm is developed to solve multiobjective optimization problems. The basic DBCC algorithm has the disadvantage of being trapped into the local minimum. Therefore, some improvements are adopted in the new algorithm, such as adding chaos transfer mechanism when the bacterium choose their next locations and the crowding distance operation to maintain the population diversity in the Pareto Front. The definition of chaos transfer mechanism is used to retain the elite solution produced during the operation, and the definition of crowding distance is used to guide the bacteria for determinate variation, thus enabling the algorithm obtain well-distributed solution in the Pareto optimal set. The convergence properties of the DBCC strategy are tested on some test functions. At last, some numerical results are given to demonstrate the effectiveness of the results obtained by the new algorithm.
Analog Circuit Design Optimization Based on Evolutionary Algorithms
Directory of Open Access Journals (Sweden)
Mansour Barari
2014-01-01
Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.
MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
Ahmed M.E. Khalil
2015-06-01
Full Text Available The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA and the Krill Herd Algorithm (KHA. The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.
When do evolutionary algorithms optimize separable functions in parallel?
DEFF Research Database (Denmark)
Doerr, Benjamin; Sudholt, Dirk; Witt, Carsten
2013-01-01
is that evolutionary algorithms make progress on all subfunctions in parallel, so that optimizing a separable function does not take not much longer than optimizing the hardest subfunction-subfunctions are optimized "in parallel." We show that this is only partially true, already for the simple (1+1) evolutionary...... algorithm ((1+1) EA). For separable functions composed of k Boolean functions indeed the optimization time is the maximum optimization time of these functions times a small O(log k) overhead. More generally, for sums of weighted subfunctions that each attain non-negative integer values less than r = o(log1...
Optimal design of steel portal frames based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
Yue CHEN; Kai HU
2008-01-01
As for the optimal design of steel portal frames, due to both the complexity of cross selections of beams and columns and the discreteness of design variables, it is difficult to obtain satisfactory results by traditional optimization. Based on a set of constraints of the Technical Specification for Light-weighted Steel Portal Frames of China, a genetic algorithm (GA) optimization program for portal frames, written in MATLAB code, was proposed in this paper. The graph user interface (GUI) is also developed for this optimal program, so that it can be used much more conveniently. Finally, some examples illustrate the effectiveness and efficiency of the genetic-algorithm-based optimal program.
Accuracy Analysis of Attitude Computation Based on Optimal Coning Algorithm
Directory of Open Access Journals (Sweden)
Jianfeng Chen
2012-09-01
Full Text Available To accurately evaluate the applicability of optimal coning algorithms, the direct influence of their periodic components on attitude accuracy is investigated. The true value of the change of the rotation vector is derived from the classical coning motion for analytic comparison. The analytic results show that the influence of periodic components is mostly dominant in two types of optimal coning algorithms. Considering that the errors of periodic components cannot be simply neglected, these algorithms are categorized with simplified forms. A variety of simulations are done under the classical coning motion. The numerical results are in good agreement with the analytic deductions. Considering their attitude accuracy, optimal coning algorithms of the 4-subinterval and 5-subinterval algorithms optimized with angular increments are not recommended for use for real application.
Accuracy Analysis of Attitude Computation Based on Optimal Coning Algorithm
Directory of Open Access Journals (Sweden)
Xiyuan Chen
2012-11-01
Full Text Available To accurately evaluate the applicability of optimal coning algorithms, the direct influence of their periodic components on attitude accuracy is investigated. The true value of the change of the rotation vector is derived from the classical coning motion for analytic comparison. The analytic results show that the influence of periodic components is mostly dominant in two types of optimal coning algorithms. Considering that the errors of periodic components cannot be simply neglected, these algorithms are categorized with simplified forms. A variety of simulations are done under the classical coning motion. The numerical results are in good agreement with the analytic deductions. Considering their attitude accuracy, optimal coning algorithms of the 4-subinterval and 5-subinterval algorithms optimized with angular increments are not recommended for use for real application.Defence Science Journal, 2012, 62(6, pp.361-368, DOI:http://dx.doi.org/10.14429/dsj.62.1430
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
Holst, Terry L.
2005-01-01
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.
Optimization of Shallow Foundation Using Gravitational Search Algorithm
Directory of Open Access Journals (Sweden)
Mohammad Khajehzadeh
2012-01-01
Full Text Available In this study an effective method for nonlinear constrained optimization of shallow foundation is presented. A newly developed heuristic global optimization algorithm called Gravitational Search Algorithm (GSA is introduced and applied for the optimization of foundation. The algorithm is classified as random search algorithm and does not require initial values and uses a random search instead of a gradient search, so derivative information is unnecessary. The optimization procedure controls all geotechnical and structural design constraints while reducing the overall cost of the foundation. To verify the efficiency of the proposed method, two design examples of spread footing are illustrated. To further validate the effectiveness and robustness of the GSA, these examples are solved using genetic algorithm. The results indicate that the proposed method could provide solutions of high quality, accuracy and efficiency for optimum design of foundation.
Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad
2016-01-01
In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.
Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad
2016-11-01
In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.
Optimization Algorithms in School Scheduling Programs: Study, Analysis and Results
Directory of Open Access Journals (Sweden)
Lina PUPEIKIENE
2009-04-01
Full Text Available To create good and optimal school schedule is very important and practical task. Currently in Lithuania schools are using two programs for making the school schedule at the moment. But none of these programs is very effective. Optimization Department of Lithuanian Institute of Mathematics and Informatics (IMI has created ``School schedule optimization program''. It has three optimization algorithms for making best school schedule. A user can choose not only few optimization options and get few optimal schedules, but some subjective and objectives parameters. The making of initial data file is advanced in this program. XML format is used for creating initial data file and getting all optimal results files. The purpose of this study is to analyze used optimization algorithms used in ``School schedule optimization program'' and to compare results with two most popular commercial school scheduling programs in Lithuania.
A Novel and Robust Evolution Algorithm for Optimizing Complicated Functions
Gao, Yifeng; Zhao, Ge
2011-01-01
In this paper, a novel mutation operator of differential evolution algorithm is proposed. A new algorithm called divergence differential evolution algorithm (DDEA) is developed by combining the new mutation operator with divergence operator and assimilation operator (divergence operator divides population, and, assimilation operator combines population), which can detect multiple solutions and robustness in noisy environment. The new algorithm is applied to optimize Michalewicz Function and to track changing of rain-induced-attenuation process. The results based on DDEA are compared with those based on Differential Evolution Algorithm (DEA). It shows that DDEA algorithm gets better results than DEA does in the same premise. The new algorithm is significant for optimizing and tracking the characteristics of MIMO (Multiple Input Multiple Output) channel at millimeter waves.
Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.
Directory of Open Access Journals (Sweden)
Gonglin Yuan
Full Text Available Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1 βk ≥ 0 2 the search direction has the trust region property without the use of any line search method 3 the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.
Optimal Parallel Algorithm for the Knapsack Problem Without Memory Conflicts
Institute of Scientific and Technical Information of China (English)
Ken-Li Li; Ren-Fa Li; Qing-Hua Li
2004-01-01
The knapsack problem is well known to be NP-complete. Due to its importance in cryptosystem and in number theory, in the past two decades, much effort has been made in order to find techniques that could lead to practical algorithms with reasonable running time. This paper proposes a new parallel algorithm for the knapsack problem where the optimal merging algorithm is adopted. The proposed algorithm is based on an EREW-SIMD machine with shared memory. It is proved that the proposed algorithm is both optimal and the first without memory conflicts algorithm for the knapsack problem. The comparisons of algorithm performance show that it is an improvement over the past researches.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Energy Technology Data Exchange (ETDEWEB)
Qiang, Ji; Mitchell, Chad
2014-11-03
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.
On algorithm for building of optimal α-decision trees
Alkhalid, Abdulaziz
2010-01-01
The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.
A New Class of Hybrid Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Da-Qing Guo; Yong-Jin Zhao; Hui Xiong; Xiao Li
2007-01-01
A new class of hybrid particle swarm optimization (PSO) algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique (LDIW) and the mutative scale chaos optimization algorithm (MSCOA) are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.
Flower pollination algorithm: A novel approach for multiobjective optimization
Yang, Xin-She; Karamanoglu, Mehmet; He, Xingshi
2014-09-01
Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.
Support Vector Machine Optimized by Improved Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiang Chang Sheng
2013-07-01
Full Text Available Parameters of support vector machines (SVM which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm.
Genetic algorithms for multicriteria shape optimization of induction furnace
Kůs, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo
2012-09-01
In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.
A Cooperative Harmony Search Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Gang Li
2014-01-01
Full Text Available Harmony search algorithm (HS is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs and particle swarm optimizers (PSOs. It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.
Hitting probabilities for non-linear systems of stochastic waves
Dalang, Robert C
2012-01-01
We consider a $d$-dimensional random field $u = \\{u(t,x)\\}$ that solves a non-linear system of stochastic wave equations in spatial dimensions $k \\in \\{1,2,3\\}$, driven by a spatially homogeneous Gaussian noise that is white in time. We mainly consider the case where the spatial covariance is given by a Riesz kernel with exponent $\\beta$. Using Malliavin calculus, we establish upper and lower bounds on the probabilities that the random field visits a deterministic subset of $\\IR^d$, in terms, respectively, of Hausdorff measure and Newtonian capacity of this set. The dimension that appears in the Hausdorff measure is close to optimal, and shows that when $d(2-\\beta) > 2(k+1)$, points are polar for $u$. Conversely, in low dimensions $d$, points are not polar. There is however an interval in which the question of polarity of points remains open.
Kidney-inspired algorithm for optimization problems
Jaddi, Najmeh Sadat; Alvankarian, Jafar; Abdullah, Salwani
2017-01-01
In this paper, a population-based algorithm inspired by the kidney process in the human body is proposed. In this algorithm the solutions are filtered in a rate that is calculated based on the mean of objective functions of all solutions in the current population of each iteration. The filtered solutions as the better solutions are moved to filtered blood and the rest are transferred to waste representing the worse solutions. This is a simulation of the glomerular filtration process in the kidney. The waste solutions are reconsidered in the iterations if after applying a defined movement operator they satisfy the filtration rate, otherwise it is expelled from the waste solutions, simulating the reabsorption and excretion functions of the kidney. In addition, a solution assigned as better solution is secreted if it is not better than the worst solutions simulating the secreting process of blood in the kidney. After placement of all the solutions in the population, the best of them is ranked, the waste and filtered blood are merged to become a new population and the filtration rate is updated. Filtration provides the required exploitation while generating a new solution and reabsorption gives the necessary exploration for the algorithm. The algorithm is assessed by applying it on eight well-known benchmark test functions and compares the results with other algorithms in the literature. The performance of the proposed algorithm is better on seven out of eight test functions when it is compared with the most recent researches in literature. The proposed kidney-inspired algorithm is able to find the global optimum with less function evaluations on six out of eight test functions. A statistical analysis further confirms the ability of this algorithm to produce good-quality results.
Davidon's optimally conditioned algorithms for unconstrained optimization
Energy Technology Data Exchange (ETDEWEB)
Nazareth, L.
1976-01-01
Recently, Davidon (Math. Prog., 9, 1-30) has published some new and very promising algorithms for minimizing unconstrained functionals. A particular perspective on these algorithms is presented, and extensions of some of the theory underlying them are developed in this paper.
An Improved Particle Swarm Optimization Algorithm Based on Ensemble Technique
Institute of Scientific and Technical Information of China (English)
SHI Yan; HUANG Cong-ming
2006-01-01
An improved particle swarm optimization (PSO) algorithm based on ensemble technique is presented. The algorithm combines some previous best positions (pbest) of the particles to get an ensemble position (Epbest), which is used to replace the global best position (gbest). It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.
A Unified Differential Evolution Algorithm for Global Optimization
Energy Technology Data Exchange (ETDEWEB)
Qiang, Ji; Mitchell, Chad
2014-06-24
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.
Wolf Search Algorithm for Solving Optimal Reactive Power Dispatch Problem
Directory of Open Access Journals (Sweden)
Kanagasabai Lenin
2015-03-01
Full Text Available This paper presents a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA for solving the multi-objective reactive power dispatch problem. Wolf Search algorithm is a new bio – inspired heuristic algorithm which based on wolf preying behaviour. The way wolves search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power dispatches. And the speciality of wolf is possessing both individual local searching ability and autonomous flocking movement and this special property has been utilized to formulate the search algorithm .The proposed (WSA algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the good performance of the proposed algorithm .
Model-based multiobjective evolutionary algorithm optimization for HCCI engines
Ma, He; Xu, Hongming; Wang, Jihong; Schnier, Thorsten; Neaves, Ben; Tan, Cheng; Wang, Zhi
2014-01-01
Modern engines feature a considerable number of adjustable control parameters. With this increasing number of Degrees of Freedom (DoF) for engines, and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated engine optimization approach is desired. In this paper, a self-learning evolutionary algorithm based multi-objective globally optimization approach for a H...
A Hamiltonian Algorithm for Singular Optimal LQ Control Systems
Delgado-Tellez, M
2012-01-01
A Hamiltonian algorithm, both theoretical and numerical, to obtain the reduced equations implementing Pontryagine's Maximum Principle for singular linear-quadratic optimal control problems is presented. This algorithm is inspired on the well-known Rabier-Rheinhboldt constraints algorithm used to solve differential-algebraic equations. Its geometrical content is exploited fully by implementing a Hamiltonian extension of it which is closer to Gotay-Nester presymplectic constraint algorithm used to solve singular Hamiltonian systems. Thus, given an optimal control problem whose optimal feedback is given in implicit form, a consistent set of equations is obtained describing the first order differential conditions of Pontryaguine's Maximum Principle. Such equations are shown to be Hamiltonian and the set of first class constraints corresponding to controls that are not determined, are obtained explicitly. The strength of the algorithm is shown by exhibiting a numerical implementation with partial feedback on the c...
Multiphase Return Trajectory Optimization Based on Hybrid Algorithm
Directory of Open Access Journals (Sweden)
Yi Yang
2016-01-01
Full Text Available A hybrid trajectory optimization method consisting of Gauss pseudospectral method (GPM and natural computation algorithm has been developed and utilized to solve multiphase return trajectory optimization problem, where a phase is defined as a subinterval in which the right-hand side of the differential equation is continuous. GPM converts the optimal control problem to a nonlinear programming problem (NLP, which helps to improve calculation accuracy and speed of natural computation algorithm. Through numerical simulations, it is found that the multiphase optimal control problem could be solved perfectly.
Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
Chen, Wei
2015-07-01
In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.
Directory of Open Access Journals (Sweden)
Sukanta Nama
2016-04-01
Full Text Available Differential evolution (DE is an effective and powerful approach and it has been widely used in different environments. However, the performance of DE is sensitive to the choice of control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Backtracking Search Optimization Algorithm (BSA is a new evolutionary algorithm (EA for solving real-valued numerical optimization problems. An ensemble algorithm called E-BSADE is proposed which incorporates concepts from DE and BSA. The performance of E-BSADE is evaluated on several benchmark functions and is compared with basic DE, BSA and conventional DE mutation strategy.
Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.
2014-01-01
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
Directory of Open Access Journals (Sweden)
A. Hajnoori
2013-07-01
Full Text Available Portfolio optimization problem follows the calculation of investment income per share, based on return and risk criteria. Since stock risk is achieved by calculating its return, which is itself computed based on stock price, it is essential to forecast the stock price, efficiently. In this paper, in order to predict the stock price, grey fuzzy technique with high efficiency is employed. The proposed study of this paper calculates the return and risk of each asset and portfolio optimization model is developed based on cardinality constraint and investment income per share. To solve the resulted model, Invasive Weed Optimization (IWO algorithm is applied. In an example this algorithm is compared with other metaheuristic algorithms such as Imperialist Competitive Algorithm (ICA, Genetic Algorithm (GA and Particle Swarm Optimization (PSO. The results show that the applied algorithm performs significantly better than other algorithms.
Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-López, S; Portilla-Figueras, J A
2014-01-01
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.
Hybrid Algorithm for the Optimization of Training Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Hayder M. Albeahdili
2015-10-01
Full Text Available The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN. Although stochastic gradient descend (SGD is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA and particle swarm optimization (PSO is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches.
PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm
Directory of Open Access Journals (Sweden)
Wei Chen Esmonde Lim
2014-01-01
Full Text Available 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.
PCB drill path optimization by combinatorial cuckoo search algorithm.
Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G
2014-01-01
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.
A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT
Holdsworth, Clay; Liao, Jay; Phillips, Mark H
2012-01-01
Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then...
Predictability of extremes in non-linear hierarchically organized systems
Kossobokov, V. G.; Soloviev, A.
2011-12-01
phenomena of highly complex origin, by their nature, implies using problem oriented methods, which design breaks the limits of classical statistical or econometric applications. The unambiguously designed forecast/prediction algorithms of the "yes or no" variety, analyze the observable quantitative integrals and indicators available to a given date, then provides unambiguous answer to the question whether a critical transition should be expected or not in the next time interval. Since the predictability of an originating non-linear dynamical system is limited in principle, the probabilistic component of forecast/prediction algorithms is represented by the empirical probabilities of alarms, on one side, and failures-to-predict, on the other, estimated on control sets achieved in the retro- and prospective experiments. Predicting in advance is the only decisive test of forecast/predictions and the relevant on-going experiments are conducted in the case seismic extremes, recessions, and increases of unemployment rate. The results achieved in real-time testing keep being encouraging and confirm predictability of the extremes.
Research on particle swarm optimization algorithm based on optimal movement probability
Ma, Jianhong; Zhang, Han; He, Baofeng
2017-01-01
The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.
Generating Feature Spaces for Linear Algorithms with Regularized Sparse Kernel Slow Feature Analysis
Böhmer, W.; Grünewälder, S.; Nickisch, H.; Obermayer, K.
2013-01-01
Without non-linear basis functions many problems can not be solved by linear algorithms. This article proposes a method to automatically construct such basis functions with slow feature analysis (SFA). Non-linear optimization of this unsupervised learning method generates an orthogonal basis on the
Directory of Open Access Journals (Sweden)
Yuksel Celik
2013-01-01
Full Text Available Marriage in honey bees optimization (MBO is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
Optimal Design of Materials for DJMP Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FENG Zhong-ren; WANG Xiong-jiang
2004-01-01
The genetic algorithm was used in optimal design of deep jet method pile. The cost of deep jetmethod pile in one unit area of foundation was taken as the objective function. All the restrains were listed followingthe corresponding specification. Suggestions were proposed and the modified. The real-coded Genetic Algorithm wasgiven to deal with the problems of excessive computational cost and premature convergence. Software system of opti-mal design of deep jet method pile was developed.
Differential evolution algorithm for global optimizations in nuclear physics
Qi, Chong
2017-04-01
We explore the applicability of the differential evolution algorithm in finding the global minima of three typical nuclear structure physics problems: the global deformation minimum in the nuclear potential energy surface, the optimization of mass model parameters and the lowest eigenvalue of a nuclear Hamiltonian. The algorithm works very effectively and efficiently in identifying the minima in all problems we have tested. We also show that the algorithm can be parallelized in a straightforward way.
A TRUST-REGION ALGORITHM FOR NONLINEAR INEQUALITY CONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Xiaojiao Tong; Shuzi Zhou
2003-01-01
This paper presents a new trust-region algorithm for n-dimension nonlinear optimization subject to m nonlinear inequality constraints. Equivalent KKT conditions are derived,which is the basis for constructing the new algorithm. Global convergence of the algorithm to a first-order KKT point is established under mild conditions on the trial steps, local quadratic convergence theorem is proved for nondegenerate minimizer point. Numerical experiment is presented to show the effectiveness of our approach.
QOS-BASED MULTICAST ROUTING OPTIMIZATION ALGORITHMS FOR INTERNET
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Most of the multimedia applications require strict Quality-of-Service (QoS) guarantee during the communication between a single source and multiple destinations. The paper mainly presents a QoS Multicast Routing algorithms based on Genetic Algorithm (QMRGA). Simulation results demonstrate that the algorithm is capable of discovering a set of QoS-based near optimized, non-dominated multicast routes within a few iterations, even for the networks environment with uncertain parameters.
A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization
Directory of Open Access Journals (Sweden)
Zhijun Luo
2014-01-01
Full Text Available A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.
Gradient Gene Algorithm: a Fast Optimization Method to MST Problem
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The extension of Minimum Spanning Tree(MST) problem is an NP hardproblem which does not exit a polynomial time algorithm. In this paper, a fast optimizat ion method on MST problem--the Gradient Gene Algorithm is introduced. Compar ed with other evolutionary algorithms on MST problem, it is more advanced: firstly, very simple and easy to realize; then, efficient and accurate; finally general on other combination optimization problems.
Advanced optimization of permanent magnet wigglers using a genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Hajima, Ryoichi [Univ. of Tokyo (Japan)
1995-12-31
In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.
Parallel optimization algorithms and their implementation in VLSI design
Lee, G.; Feeley, J. J.
1991-01-01
Two new parallel optimization algorithms based on the simplex method are described. They may be executed by a SIMD parallel processor architecture and be implemented in VLSI design. Several VLSI design implementations are introduced. An application example is reported to demonstrate that the algorithms are effective.
Bio Inspired Algorithms in Single and Multiobjective Reliability Optimization
DEFF Research Database (Denmark)
Madsen, Henrik; Albeanu, Grigore; Burtschy, Bernard
2014-01-01
Non-traditional search and optimization methods based on natural phenomena have been proposed recently in order to avoid local or unstable behavior when run towards an optimum state. This paper describes the principles of bio inspired algorithms and reports on Migration Algorithms and Bees...
Application of Bacterial Foraging Optimization in Non-linear Model Identification%细菌生存优化在非线性模型辨识中的应用
Institute of Scientific and Technical Information of China (English)
林卫星; Peter X.Liu; 李文磊; 陈炎海; 欧超
2009-01-01
提出了一种新的基于细菌生存优化(Bacterial Foraging Optimization BFO)的非线性模型辨识方法.它是利用群集智能仿生BFO算法对一类Hammerstein系统进行辨识,从而估计出它的参数模型.通过对这类输入非线性模型进行辨识,并用仿真实验说明BFO算法的参数设置与选择方法.比较基于粒子群优化(Particle Swarm Optimization PSO)的非线性模型辨识算法,特别是对有色噪声的鲁棒性、模型的辨识精度、辨识收敛速度进行对比分析,以得出BFO辨识算法的优缺点及其有效性.
Genetic algorithm for neural networks optimization
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
Niche Genetic Algorithm with Accurate Optimization Performance
Institute of Scientific and Technical Information of China (English)
LIU Jian-hua; YAN De-kun
2005-01-01
Based on crowding mechanism, a novel niche genetic algorithm was proposed which can record evolutionary direction dynamically during evolution. After evolution, the solutions's precision can be greatly improved by means of the local searching along the recorded direction. Simulation shows that this algorithm can not only keep population diversity but also find accurate solutions. Although using this method has to take more time compared with the standard GA, it is really worth applying to some cases that have to meet a demand for high solution precision.
The fuzzy logic algorithm has the ability to describe knowledge in a descriptive human-like manner in the form of simple rules using linguistic variables, and provides a new way of modeling uncertain or naturally fuzzy hydrological processes like non-linear rainfall-runoff relationships. Fuzzy infe...
Non-Linear Unit Root Properties of Crude Oil Production
Svetlana Maslyuk; Russell Smyth
2007-01-01
While there is good reason to expect crude oil production to be non-linear, previous studies that have examined the stochastic properties of crude oil production have assumed that crude oil production follows a linear process. If crude oil production is a non-linear process, conventional unit root tests, which assume linear and systematic adjustment, could interpret departure from linearity as permanent stochastic disturbances. The objective of this paper is to test for non-linearities and un...
Non-Linear Back-propagation: Doing Back-Propagation withoutDerivatives of the Activation Function
DEFF Research Database (Denmark)
Hertz, John; Krogh, Anders Stærmose; Lautrup, Benny
1997-01-01
The conventional linear back-propagation algorithm is replaced by a non-linear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in hardware realizations of neural processors. In this paper we derive the non-linear back-propagatio......-propagation algorithms in the framework of recurrent back-propagation and present some numerical simulations of feed-forward networks on the NetTalk problem. A discussion of implementation in analog VLSI electronics concludes the paper.......The conventional linear back-propagation algorithm is replaced by a non-linear version, which avoids the necessity for calculating the derivative of the activation function. This may be exploited in hardware realizations of neural processors. In this paper we derive the non-linear back...
MPC Toolbox with GPU Accelerated Optimization Algorithms
DEFF Research Database (Denmark)
Gade-Nielsen, Nicolai Fog; Jørgensen, John Bagterp; Dammann, Bernd
2012-01-01
The introduction of Graphical Processing Units (GPUs) in scientific computing has shown great promise in many different fields. While GPUs are capable of very high floating point performance and memory bandwidth, its massively parallel architecture requires algorithms to be reimplemented to suit...
A Hybrid Mutation Chemical Reaction Optimization Algorithm for Global Numerical Optimization
Directory of Open Access Journals (Sweden)
Ransikarn Ngambusabongsopa
2015-01-01
Full Text Available This paper proposes a hybrid metaheuristic approach that improves global numerical optimization by increasing optimal quality and accelerating convergence. This algorithm involves a recently developed process for chemical reaction optimization and two adjustment operators (turning and mutation operators. Three types of mutation operators (uniform, nonuniform, and polynomial were combined with chemical reaction optimization and turning operator to find the most appropriate framework. The best solution among these three options was selected to be a hybrid mutation chemical reaction optimization algorithm for global numerical optimization. The optimal quality, convergence speed, and statistical hypothesis testing of our algorithm are superior to those previous high performance algorithms such as RCCRO, HP-CRO2, and OCRO.
Non-linear finite element analysis in structural mechanics
Rust, Wilhelm
2015-01-01
This monograph describes the numerical analysis of non-linearities in structural mechanics, i.e. large rotations, large strain (geometric non-linearities), non-linear material behaviour, in particular elasto-plasticity as well as time-dependent behaviour, and contact. Based on that, the book treats stability problems and limit-load analyses, as well as non-linear equations of a large number of variables. Moreover, the author presents a wide range of problem sets and their solutions. The target audience primarily comprises advanced undergraduate and graduate students of mechanical and civil engineering, but the book may also be beneficial for practising engineers in industry.
OPTIMIZATION BASED ON LMPROVED REAL—CODED GENETIC ALGORITHM
Institute of Scientific and Technical Information of China (English)
ShiYu; YuShenglin
2002-01-01
An improved real-coded genetic algorithm is pro-posed for global optimization of functionsl.The new algo-rithm is based om the judgement of the searching perfor-mance of basic real-coded genetic algorithm.The opera-tions of basic real-coded genetic algorithm are briefly dis-cussed and selected.A kind of chaos sequence is described in detail and added in the new algorithm ad a disturbance factor.The strategy of field partition is also used to im-prove the strcture of the new algorithm.Numerical ex-periment shows that the mew genetic algorithm can find the global optimum of complex funtions with satistaiting precision.
Adaptive symbiotic organisms search (SOS algorithm for structural design optimization
Directory of Open Access Journals (Sweden)
Ghanshyam G. Tejani
2016-07-01
Full Text Available The symbiotic organisms search (SOS algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.
Implementation and Optimization of A Fast Inter Prediction Algorithm
Directory of Open Access Journals (Sweden)
Xiaoyu Li
2013-07-01
Full Text Available Audio Video coding Standard is the second generation Source Coding-Decoding standards of China, especially for embedded audio/video platform. This paper proposes an efficient and fast inter prediction algorithm, which is one of the key techniques of Audio Video coding Standard.Reducing of the redundancy in source sequence inter prediction, it could improve the picture quality. The optimization schemes include two aspects,which are the algorithm framework, variables and data structure. Optimized results demonstrate that our algorithm has a notable improvement of the clockcycle efficiency. Furthermore, this research also gives a valuable insight of the combination with quantum information.
Optimal Path Planning for Mobile Robot Using Tailored Genetic Algorithm
Directory of Open Access Journals (Sweden)
Dong Xiao Xian
2013-07-01
Full Text Available During routine inspecting, mobile robot may be requested to visit multiple locations to execute special tasks occasionally. This study aims at optimal path planning for multiple goals visiting task based on tailored genetic algorithm. The proposed algorithm will generate an optimal path that has the least idle time, which is proven to be more effective on evaluating a path in our previous work. In proposed algorithm, customized chromosome representing a path and genetic operators including repair and cut are developed and implemented. Afterwards, simulations are carried out to verify the effectiveness and applicability. Finally, analysis of simulation results is conducted and future work is addressed.
NONMONOTONE PRECONDITIONAL CURVILINEAR PATH ALGORITHMS FOR UNCONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
朱德通
2003-01-01
This paper presents nonmonotonic quasi-Newton algorithms via two pre-conditional curvilinear paths, the preconditional modified gradient path and the precon-ditional optimal path, for unconstrained optimization problem. We employ the stableBunch-Parlett factorization method to form two curvilinear paths very easily. Thenonmonotone criterion is used to speed up the convergence progress in the contoursof objective function with large curvature. Theoretical analyses are given which provethat the proposed algorithms are globally convergent and have a local superlinear con-vergence rate under some reasonable conditions. The results of numerical experimentsare reported to show the effectiveness of the proposed algorithms.
Optimization of deep learning algorithms for object classification
Horváth, András.
2017-02-01
Deep learning is currently the state of the art algorithm for image classification. The complexity of these feedforward neural networks have overcome a critical point, resulting algorithmic breakthroughs in various fields. On the other hand their complexity makes them executable in tasks, where High-throughput computing powers are available. The optimization of these networks -considering computational complexity and applicability on embedded systems- has not yet been studied and investigated in details. In this paper I show some examples how this algorithms can be optimized and accelerated on embedded systems.
Imperialist competitive algorithm combined with chaos for global optimization
Talatahari, S.; Farahmand Azar, B.; Sheikholeslami, R.; Gandomi, A. H.
2012-03-01
A novel chaotic improved imperialist competitive algorithm (CICA) is presented for global optimization. The ICA is a new meta-heuristic optimization developed based on a socio-politically motivated strategy and contains two main steps: the movement of the colonies and the imperialistic competition. Here different chaotic maps are utilized to improve the movement step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the other ICA-based methods demonstrates the superiority of the CICA for the benchmark functions.
Energy Technology Data Exchange (ETDEWEB)
Ravi Kanth, A.S.V. [Applied Mathematics Division, School of Science and Humanities, V.I.T. University, Vellore-632 014, Tamil Nadu (India)], E-mail: asvravikanth@yahoo.com; Aruna, K. [Applied Mathematics Division, School of Science and Humanities, V.I.T. University, Vellore-632 014, Tamil Nadu (India)
2008-11-17
In this Letter, we propose a reliable algorithm to develop exact and approximate solutions for the linear and non-linear systems of partial differential equations. The approach rest mainly on two-dimensional differential transform method which is one of the approximate methods. The method can easily be applied to many linear and non-linear problems and is capable of reducing the size of computational work. Exact solutions can also be achieved by the known forms of the series solutions. Several illustrative examples are given to demonstrate the effectiveness of the present method.
Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms
Cao, Ming; Lu, Ming; Zhang, Jian-Ping
2004-01-01
This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation m
Multi-objective gene-pool optimal mixing evolutionary algorithms
Luong, N.H.; La Poutré, J.A.; Bosman, P.A.N.; Igel, C.
2014-01-01
The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), with a lean, but sufficient, linkage model and an efficient variation operator, has been shown to be a robust and efficient methodology for solving single objective (SO) optimization problems with superior performance c
Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms
Cao, Ming; Lu, Ming; Zhang, Jian-Ping
2004-01-01
This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation m
Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm
Directory of Open Access Journals (Sweden)
P. Sabarinath
2015-01-01
Full Text Available The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.
Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms
Cao, Ming; Lu, Ming; Zhang, Jian-Ping
2004-01-01
This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation
Solution of optimal power flow using evolutionary-based algorithms
African Journals Online (AJOL)
This paper applies two reliable and efficient evolutionary-based methods named Shuffled Frog Leaping Algorithm ... Grey Wolf Optimizer (GWO) to solve Optimal Power Flow (OPF) problem. OPF is ..... The wolves search for the prey based on the alpha, beta, and delta positions. ..... Energy Conversion and Management, Vol.
A Hybrid Aggressive Space Mapping Algorithm for EM Optimization
DEFF Research Database (Denmark)
Bakr, Mohamed H.; Bandler, John W.; Georgieva, N.;
1999-01-01
We propose a novel hybrid aggressive space-mapping (HASM) optimization algorithm. HASM exploits both the trust-region aggressive space-mapping (TRASM) strategy and direct optimization. Severe differences between the coarse and fine models and nonuniqueness of the parameter extraction procedure ma...
Directory of Open Access Journals (Sweden)
Rabindra Kumar Sahu
2016-03-01
Full Text Available This paper presents the design and analysis of Proportional-Integral-Double Derivative (PIDD controller for Automatic Generation Control (AGC of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization (TLBO algorithm. At first, a two-area reheat thermal power system with appropriate Generation Rate Constraint (GRC is considered. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PIDD controller. The superiority of the proposed TLBO based PIDD controller has been demonstrated by comparing the results with recently published optimization technique such as hybrid Firefly Algorithm and Pattern Search (hFA-PS, Firefly Algorithm (FA, Bacteria Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and conventional Ziegler Nichols (ZN for the same interconnected power system. Also, the proposed approach has been extended to two-area power system with diverse sources of generation like thermal, hydro, wind and diesel units. The system model includes boiler dynamics, GRC and Governor Dead Band (GDB non-linearity. It is observed from simulation results that the performance of the proposed approach provides better dynamic responses by comparing the results with recently published in the literature. Further, the study is extended to a three unequal-area thermal power system with different controllers in each area and the results are compared with published FA optimized PID controller for the same system under study. Finally, sensitivity analysis is performed by varying the system parameters and operating load conditions in the range of ±25% from their nominal values to test the robustness.
A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution
Directory of Open Access Journals (Sweden)
Lijin Wang
2015-01-01
Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.
Electronic Commerce Logistics Network Optimization Based on Swarm Intelligent Algorithm
Directory of Open Access Journals (Sweden)
Yabing Jiao
2013-09-01
Full Text Available This article establish an efficient electronic commerce logistics operation system to reduce distribution costs and build a logistics network operation model based on around the B2C electronic commerce enterprise logistics network operation system. B2C electronic commerce transactions features in the enterprise network platform. To solve the NP-hard problem this article use hybrid ant colony algorithm, particle swarm algorithm and group swarm intelligence algorithm to get a best solution. According to the intelligent algorithm, design of electronic commerce logistics network optimization system, enter the national 22 electronic commerce logistics network for validation. Through the experiment to verify the optimized logistics cost greatly decreased. This research can help B2C electronic commerce enterprise logistics network to optimize decision-making under the premise of ensuring the interests of consumers and service levels also can be an effective way for enterprises to improve the efficiency of logistics services and reduce operation costs
PCNN document segmentation method based on bacterial foraging optimization algorithm
Liao, Yanping; Zhang, Peng; Guo, Qiang; Wan, Jian
2014-04-01
Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.
Optimizing graph algorithms on pregel-like systems
Salihoglu, Semih
2014-03-01
We study the problem of implementing graph algorithms efficiently on Pregel-like systems, which can be surprisingly challenging. Standard graph algorithms in this setting can incur unnecessary inefficiencies such as slow convergence or high communication or computation cost, typically due to structural properties of the input graphs such as large diameters or skew in component sizes. We describe several optimization techniques to address these inefficiencies. Our most general technique is based on the idea of performing some serial computation on a tiny fraction of the input graph, complementing Pregel\\'s vertex-centric parallelism. We base our study on thorough implementations of several fundamental graph algorithms, some of which have, to the best of our knowledge, not been implemented on Pregel-like systems before. The algorithms and optimizations we describe are fully implemented in our open-source Pregel implementation. We present detailed experiments showing that our optimization techniques improve runtime significantly on a variety of very large graph datasets.
Wismans, Luc; Berkum, van Eric; Bliemer, Michiel; Allkim, T.P.; Arem, van B.
2748-01-01
Multi objective optimization of externalities of traffic is performed solving a network design problem in which Dynamic Traffic Management measures are used. The resulting Pareto optimal set is determined by employing the SPEA2+ evolutionary algorithm.
The analysis of the convergence of ant colony optimization algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Qingbao; WANG Lingling
2007-01-01
The ant colony optimization algorithm has been widely studied and many important results have been obtained.Though this algorithm has been applied to many fields.the analysis about its convergence is much less,which will influence the improvement of this algorithm.Therefore,the convergence of this algorithm applied to the traveling salesman problem(TSP)was analyzed in detail.The conclusion that this algorithm will definitely converge to the optimal solution under the condition of 0＜q0＜1 was proved true.In addition,the influence on its convergence caused by the properties of the closed path,heuristic functions,the pheromone and q0 was analyzed.Based on the above-mentioned,some conclusions about how to improve the speed of its convergence are obtained.
SAR Image Segmentation Based On Hybrid PSOGSA Optimization Algorithm
Directory of Open Access Journals (Sweden)
Amandeep Kaur
2014-09-01
Full Text Available Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR images is still a challenging problem. We proposed a fast SAR image segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA. In this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.
Graph-based local elimination algorithms in discrete optimization
Shcherbina, Oleg
2009-01-01
The aim of this paper is to provide a review of structural decomposition methods in discrete optimization and to give a unified framework in the form of local elimination algorithms (LEA). This paper is organized as follows. Local elimination algorithms for discrete optimization (DO) problems (DOPs) with constraints are considered; a classification of dynamic programming computational procedures is given. We introduce Elimination Game and Elimination tree. Application of bucket elimination algorithm from constraint satisfaction (CS) to solving DOPs is done. We consider different local elimination schemes and related notions. Clustering that merges several variables into single meta-variable defines a promising approach to solve DOPs. This allows to create a quotient (condensed) graph and apply a local block elimination algorithm. In order to describe a block elimination process, we introduce Block Elimination Game. We discuss the connection of aforementioned local elimination algorithmic schemes and a way of ...
Stochastic Recursive Algorithms for Optimization Simultaneous Perturbation Methods
Bhatnagar, S; Prashanth, L A
2013-01-01
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from sim...
Optimization of Submarine Hydrodynamic Coefficients Based on Immune Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
HU Kun; XU Yi-fan
2010-01-01
Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations, an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations. Some hydrody-namic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm. By using adaptive weight method to determine the weight and target function, the multi-objective optimization could be transla-ted into single-objective optimization. For a certain kind of submarine, three typical maneuvers were chosen to be the objects of study: overshoot maneuver in horizontal plane, overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane. From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrody-namic coefficient, the efficiency of proposed method is proved.
AN OPTIMIZATION ALGORITHM BASED ON BACTERIA BEHAVIOR
Directory of Open Access Journals (Sweden)
Ricardo Contreras
2014-09-01
Full Text Available Paradigms based on competition have shown to be useful for solving difficult problems. In this paper we present a new approach for solving hard problems using a collaborative philosophy. A collaborative philosophy can produce paradigms as interesting as the ones found in algorithms based on a competitive philosophy. Furthermore, we show that the performance - in problems associated to explosive combinatorial - is comparable to the performance obtained using a classic evolutive approach.
Online algorithms for optimal energy distribution in microgrids
Wang, Yu; Nelms, R Mark
2015-01-01
Presenting an optimal energy distribution strategy for microgrids in a smart grid environment, and featuring a detailed analysis of the mathematical techniques of convex optimization and online algorithms, this book provides readers with essential content on how to achieve multi-objective optimization that takes into consideration power subscribers, energy providers and grid smoothing in microgrids. Featuring detailed theoretical proofs and simulation results that demonstrate and evaluate the correctness and effectiveness of the algorithm, this text explains step-by-step how the problem can b
Optimizing Combination of Units Commitment Based on Improved Genetic Algorithms
Institute of Scientific and Technical Information of China (English)
LAI Yifei; ZHANG Qianhua; JIA Junping
2007-01-01
GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic recombination and survival of the fittest. By use of coding betterment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for optimizing combination of units in thermal power plants is proposed.And through taking examples, test result are analyzed and compared with results of some different algorithms. Numerical results show available value for the unit commitment problem with examples.
Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm
Tang, Xinggang; Zhang, Weihong; Zhu, Jihong
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.
Support vector machines optimization based theory, algorithms, and extensions
Deng, Naiyang; Zhang, Chunhua
2013-01-01
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi
Two-Phase Algorithm for Optimal Camera Placement
Jun-Woo Ahn; Tai-Woo Chang; Sung-Hee Lee; Yong Won Seo
2016-01-01
As markers for visual sensor networks have become larger, interest in the optimal camera placement problem has continued to increase. The most featured solution for the optimal camera placement problem is based on binary integer programming (BIP). Due to the NP-hard characteristic of the optimal camera placement problem, however, it is difficult to find a solution for a complex, real-world problem using BIP. Many approximation algorithms have been developed to solve this problem. In this pape...
An Efficient Algorithm for Query Transformation in Semantic Query Optimization
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Semantic query optimization (SQO) is comparatively a recent approach for the transformation of given query into equivalent alternative query using matching rules in order to select an optimal query based on the costs of executing alternative queries. The key aspect of the algorithm proposed here is that previous proposed SQO techniques can be considered equally in the uniform cost model, with which optimization opportunities will not be missed. At the same time, the authors used the implication closure to guarantee that any matched rule will not be lost. The authors implemented their algorithm for the optimization of decomposed sub-query in local database in Multi-Database Integrator (MDBI), which is a multidatabase project. The experimental results verify that this algorithm is effective in the process of SQO.
Optimization Algorithms Testing and Convergence by Using a Stacked Histogram
Directory of Open Access Journals (Sweden)
ZAPLATILEK, K.
2011-02-01
Full Text Available The article describes an original method of optimization algorithms testing and convergence. The method is based on so-called stacked histogram. Stacked histogram is a histogram with its features marked by a chosen colour scheme. Thus, the histogram maintains the information on the input digital sequence. This approach enables an easy identification of the hidden defects in the random process statistical distribution. The stacked histogram is used for the testing of the convergent quality of various optimization techniques. Its width, position and colour scheme provides enough information on the chosen algorithm optimization trajectory. Both the classic iteration techniques and the stochastic optimization algorithm with the adaptation were used as examples.
Two-Phase Algorithm for Optimal Camera Placement
Directory of Open Access Journals (Sweden)
Jun-Woo Ahn
2016-01-01
Full Text Available As markers for visual sensor networks have become larger, interest in the optimal camera placement problem has continued to increase. The most featured solution for the optimal camera placement problem is based on binary integer programming (BIP. Due to the NP-hard characteristic of the optimal camera placement problem, however, it is difficult to find a solution for a complex, real-world problem using BIP. Many approximation algorithms have been developed to solve this problem. In this paper, a two-phase algorithm is proposed as an approximation algorithm based on BIP that can solve the optimal camera placement problem for a placement space larger than in current studies. This study solves the problem in three-dimensional space for a real-world structure.
Comparison of evolutionary algorithms for LPDA antenna optimization
Lazaridis, Pavlos I.; Tziris, Emmanouil N.; Zaharis, Zaharias D.; Xenos, Thomas D.; Cosmas, John P.; Gallion, Philippe B.; Holmes, Violeta; Glover, Ian A.
2016-08-01
A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.
Online Algorithms for Adaptive Optimization in Heterogeneous Delay Tolerant Networks
Directory of Open Access Journals (Sweden)
Wissam Chahin
2013-12-01
Full Text Available Delay Tolerant Networks (DTNs are an emerging type of networks which do not need a predefined infrastructure. In fact, data forwarding in DTNs relies on the contacts among nodes which may possess different features, radio range, battery consumption and radio interfaces. On the other hand, efficient message delivery under limited resources, e.g., battery or storage, requires to optimize forwarding policies. We tackle optimal forwarding control for a DTN composed of nodes of different types, forming a so-called heterogeneous network. Using our model, we characterize the optimal policies and provide a suitable framework to design a new class of multi-dimensional stochastic approximation algorithms working for heterogeneous DTNs. Crucially, our proposed algorithms drive online the source node to the optimal operating point without requiring explicit estimation of network parameters. A thorough analysis of the convergence properties and stability of our algorithms is presented.
A superlinear interior points algorithm for engineering design optimization
Herskovits, J.; Asquier, J.
1990-01-01
We present a quasi-Newton interior points algorithm for nonlinear constrained optimization. It is based on a general approach consisting of the iterative solution in the primal and dual spaces of the equalities in Karush-Kuhn-Tucker optimality conditions. This is done in such a way to have primal and dual feasibility at each iteration, which ensures satisfaction of those optimality conditions at the limit points. This approach is very strong and efficient, since at each iteration it only requires the solution of two linear systems with the same matrix, instead of quadratic programming subproblems. It is also particularly appropriate for engineering design optimization inasmuch at each iteration a feasible design is obtained. The present algorithm uses a quasi-Newton approximation of the second derivative of the Lagrangian function in order to have superlinear asymptotic convergence. We discuss theoretical aspects of the algorithm and its computer implementation.
A superlinear interior points algorithm for engineering design optimization
Herskovits, J.; Asquier, J.
1990-01-01
We present a quasi-Newton interior points algorithm for nonlinear constrained optimization. It is based on a general approach consisting of the iterative solution in the primal and dual spaces of the equalities in Karush-Kuhn-Tucker optimality conditions. This is done in such a way to have primal and dual feasibility at each iteration, which ensures satisfaction of those optimality conditions at the limit points. This approach is very strong and efficient, since at each iteration it only requires the solution of two linear systems with the same matrix, instead of quadratic programming subproblems. It is also particularly appropriate for engineering design optimization inasmuch at each iteration a feasible design is obtained. The present algorithm uses a quasi-Newton approximation of the second derivative of the Lagrangian function in order to have superlinear asymptotic convergence. We discuss theoretical aspects of the algorithm and its computer implementation.
Optimization of Algorithms Using Extensions of Dynamic Programming
AbouEisha, Hassan M.
2017-04-09
We study and answer questions related to the complexity of various important problems such as: multi-frontal solvers of hp-adaptive finite element method, sorting and majority. We advocate the use of dynamic programming as a viable tool to study optimal algorithms for these problems. The main approach used to attack these problems is modeling classes of algorithms that may solve this problem using a discrete model of computation then defining cost functions on this discrete structure that reflect different complexity measures of the represented algorithms. As a last step, dynamic programming algorithms are designed and used to optimize those models (algorithms) and to obtain exact results on the complexity of the studied problems. The first part of the thesis presents a novel model of computation (element partition tree) that represents a class of algorithms for multi-frontal solvers along with cost functions reflecting various complexity measures such as: time and space. It then introduces dynamic programming algorithms for multi-stage and bi-criteria optimization of element partition trees. In addition, it presents results based on optimal element partition trees for famous benchmark meshes such as: meshes with point and edge singularities. New improved heuristics for those benchmark meshes were ob- tained based on insights of the optimal results found by our algorithms. The second part of the thesis starts by introducing a general problem where different problems can be reduced to and show how to use a decision table to model such problem. We describe how decision trees and decision tests for this table correspond to adaptive and non-adaptive algorithms for the original problem. We present exact bounds on the average time complexity of adaptive algorithms for the eight elements sorting problem. Then bounds on adaptive and non-adaptive algorithms for a variant of the majority problem are introduced. Adaptive algorithms are modeled as decision trees whose depth
Non-linear Evolution of Rayleigh-Taylor Instability in a Radiation Supported Atmosphere
Jiang, Yan-Fei; Stone, James
2012-01-01
The non-linear regime of Rayleigh-Taylor instability (RTI) in a radiation supported atmosphere, consisting of two uniform fluids with different densities, is studied numerically. We perform simulations using our recently developed numerical algorithm for multi-dimensional radiation hydrodynamics based on a variable Eddington tensor as implemented in Athena, focusing on the regime where scattering opacity greatly exceeds absorption opacity. We find that the radiation field can reduce the growth and mixing rate of RTI, but this reduction is only significant when radiation pressure significantly exceeds gas pressure. Small scale structures are also suppressed in this case. In the non-linear regime, dense fingers sink faster than rarefied bubbles can rise, leading to asymmetric structures about the interface. By comparing the calculations that use a variable Eddington tensor (VET) versus the Eddington approximation, we demonstrate that anisotropy in the radiation field can affect the non-linear development of RTI...
Solving non-linear Horn clauses using a linear Horn clause solver
DEFF Research Database (Denmark)
Kafle, Bishoksan; Gallagher, John Patrick; Ganty, Pierre
2016-01-01
then proceeds by applying the linearisation transformation and solver for linear Horn clauses to a sequence of sets of clauses with successively increasing dimension bound. The approach is then further developed by using a solution of clauses of lower dimension to (partially) linearise clauses of higher......In this paper we show that checking satisfiability of a set of non-linear Horn clauses (also called a non-linear Horn clause program) can be achieved using a solver for linear Horn clauses. We achieve this by interleaving a program transformation with a satisfiability checker for linear Horn...... clauses (also called a solver for linear Horn clauses). The program transformation is based on the notion of tree dimension, which we apply to a set of non-linear clauses, yielding a set whose derivation trees have bounded dimension. Such a set of clauses can be linearised. The main algorithm...
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use
Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel
2016-01-01
Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.
Wang, Gang; Wang, Chaoli; Du, Qinghui; Cai, Xuan
2016-10-01
In this paper, we address the output consensus problem of tracking a desired trajectory for a group of second-order agents on a directed graph with a fixed topology. Each agent is modelled by a second-order non-linear system with unknown non-linear dynamics and unknown non-linear control gains. Only a subset of the agents is given access to the desired trajectory information directly. A distributed adaptive consensus protocol driving all agents to track the desired trajectory is presented using the backstepping technique and approximation technique of Fourier series (FSs). The FS structure is taken not only for tracking the non-linear dynamics but also the unknown portion in the controller design procedure, which can avoid virtual controllers containing the uncertain terms. Stability analysis and parameter convergence of the proposed algorithm are conducted based on the Lyapunov theory and the algebraic graph theory. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Though the proposed work is applicable for second-order non-linear systems containing unknown non-linear control gains, the proposed controller design can be easily extended to higher-order non-linear systems containing unknown non-linear control gains. Simulation results show the effectiveness of the proposed schemes.
Frequency selective non-linear blending to improve image quality in liver CT
Energy Technology Data Exchange (ETDEWEB)
Bongers, M.N.; Bier, G.; Kloth, C.; Schabel, C.; Nikolaou, K.; Horger, M. [University Hospital of Tuebingen (Germany). Dept. of Diagnostic and Interventional Radiology; Fritz, J. [Johns Hopkins University School of Medicine, Baltimore, MD (United States). Russell H. Morgan Dept. of Radiology and Radiological Science
2016-12-15
To evaluate the effects of a new frequency selective non-linear blending (NLB) algorithm on the contrast resolution of liver CT with low intravascular concentration of iodine contrast. Our local ethics committee approved this retrospective study. The informed consent requirement was waived. CT exams of 25 patients (60% female, mean age: 65±16 years of age) with late phase CT scans of the liver were included as a model for poor intrahepatic vascular contrast enhancement. Optimal post-processing settings to enhance the contrast of hepatic vessels were determined. Outcome variables included signal-to-noise (SNR) and contrast-to-noise ratios (CNR) of hepatic vessels and SNR of liver parenchyma of standard and post-processed images. Image quality was quantified by two independent readers using Likert scales. The post-processing settings for the visualization of hepatic vasculature were optimal at a center of 115HU, delta of 25HU, and slope of 5. Image noise was statistically indifferent between standard and post-processed images. The CNR between the hepatic vasculature (HV) and liver parenchyma could be significantly increased for liver veins (CNR{sub Standard} 1.62±1.10, CNR{sub NLB} 3.6±2.94, p=0.0002) and portal veins (CNR{sub Standard} 1.31±0.85, CNR{sub NLB} 2.42±3.03, p=0.046). The SNR of liver parenchyma was significantly higher on post-processed images (SNR{sub NLB} 11.26±3.16, SNR{sub Standard} 8.85± 2.27, p=0.008). The overall image quality and depiction of HV were significantly higher on post-processed images (NLB{sub DHV}: 4 [3-4.75], S{sub tandardDHV}: 2 [1.3-2.5], p=<0.0001; {sub NLBIQ}: 4 [4-4], {sub StandardIQ}: 2 [2-3], p=<0.0001). The use of a frequency selective non-linear blending algorithm increases the contrast resolution of liver CT and can improve the visibility of the hepatic vasculature in the setting of a low contrast ratio between vessels and the parenchyma.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2016-03-01
Full Text Available The performance of rapid prototyping (RP processes is often measured in terms of build time, product quality, dimensional accuracy, cost of production, mechanical and tribological properties of the models and energy consumed in the process. The success of any RP process in terms of these performance measures entails selection of the optimum combination of the influential process parameters. Thus, in this work the single-objective and multi-objective optimization problems of a widely used RP process, namely, fused deposition modeling (FDM, are formulated, and the same are solved using the teaching-learning-based optimization (TLBO algorithm and non-dominated Sorting TLBO (NSTLBO algorithm, respectively. The results of the TLBO algorithm are compared with those obtained using genetic algorithm (GA, and quantum behaved particle swarm optimization (QPSO algorithm. The TLBO algorithm showed better performance as compared to GA and QPSO algorithms. The NSTLBO algorithm proposed to solve the multi-objective optimization problems of the FDM process in this work is a posteriori version of the TLBO algorithm. The NSTLBO algorithm is incorporated with non-dominated sorting concept and crowding distance assignment mechanism to obtain a dense set of Pareto optimal solutions in a single simulation run. The results of the NSTLBO algorithm are compared with those obtained using non-dominated sorting genetic algorithm (NSGA-II and the desirability function approach. The Pareto-optimal set of solutions for each problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for the FDM process.
Graphical and Analytical Analysis of the Non-Linear PLL
de Boer, Bjorn; Radovanovic, S.; Annema, Anne J.; Nauta, Bram
The fixed width control pulses from the Bang-Bang Phase Detector in non-linear PLLs allow for operation at higher data rates than the linear PLL. The high non-linearity of the Bang- Bang Phase Detector gives rise to unwanted effects, such as limit-cycles, not yet fully described. This paper
Non-linear stochastic response of a shallow cable
DEFF Research Database (Denmark)
Larsen, Jesper Winther; Nielsen, Søren R.K.
2004-01-01
The paper considers the stochastic response of geometrical non-linear shallow cables. Large rain-wind induced cable oscillations with non-linear interactions have been observed in many large cable stayed bridges during the last decades. The response of the cable is investigated for a reduced two-degrees-of-freedom...
Non Linear Gauge Fixing for FeynArts
Gajdosik, Thomas
2007-01-01
We review the non-linear gauge-fixing for the Standard Model, proposed by F. Boudjema and E. Chopin, and present our implementation of this non-linear gauge-fixing to the Standard Model and to the minimal supersymmetric Standard Model in FeynArts.
Identification of Non-Linear Structures using Recurrent Neural Networks
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.
1995-01-01
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....
Identification of Non-Linear Structures using Recurrent Neural Networks
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....
Identification of Non-Linear Structures using Recurrent Neural Networks
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.
1995-01-01
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....
Non-linear wave packet dynamics of coherent states
Indian Academy of Sciences (India)
J Banerji
2001-02-01
We have compared the non-linear wave packet dynamics of coherent states of various symmetry groups and found that certain generic features of non-linear evolution are present in each case. Thus the initial coherent structures are quickly destroyed but are followed by Schrödinger cat formation and revival. We also report important differences in their evolution.
A hybrid genetic algorithm to optimize simple distillation column sequences
Institute of Scientific and Technical Information of China (English)
GAN YongSheng; Andreas Linninger
2004-01-01
Based on the principles of Genetic Algorithms (GAs), a hybrid genetic algorithm used to optimize simple distillation column sequences was established. A new data structure, a novel arithmetic crossover operator and a dynamic mutation operator were proposed. Together with the feasibility test of distillation columns, they are capable to obtain the optimum simple column sequence at one time without the limitation of the number of mixture components, ideal or non-ideal mixtures and sloppy or sharp splits. Compared with conventional algorithms, this hybrid genetic algorithm avoids solving complicated nonlinear equations and demands less derivative information and computation time. Result comparison between this genetic algorithm and Underwood method and Doherty method shows that this hybrid genetic algorithm is reliable.
Françoise Benz
2004-01-01
ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...
Françoise Benz
2004-01-01
ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on nat...
Optimal Placement Algorithms for Virtual Machines
Bellur, Umesh; SD, Madhu Kumar
2010-01-01
Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers.
Non-linear dynamics of wind turbine wings
DEFF Research Database (Denmark)
Larsen, Jesper Winther; Nielsen, Søren R.K.
2006-01-01
by the rotation of the aerodynamic load and the curvature, as well as inertial induced non-linearities caused by the support point motion. The non-linear partial differential equations of motion in the moving frame of reference have been discretized, using the fixed base eigenmodes as a functional basis......The paper deals with the formulation of non-linear vibrations of a wind turbine wing described in a wing fixed moving coordinate system. The considered structural model is a Bernoulli-Euler beam with due consideration to axial twist. The theory includes geometrical non-linearities induced....... Important non-linear couplings between the fundamental blade mode and edgewise modes have been identified based on a resonance excitation of the wing, caused by a harmonically varying support point motion with the circular frequency omega. Assuming that the fundamental blade and edgewise eigenfrequencies...
Employment of CB models for non-linear dynamic analysis
Klein, M. R. M.; Deloo, P.; Fournier-Sicre, A.
1990-01-01
The non-linear dynamic analysis of large structures is always very time, effort and CPU consuming. Whenever possible the reduction of the size of the mathematical model involved is of main importance to speed up the computational procedures. Such reduction can be performed for the part of the structure which perform linearly. Most of the time, the classical Guyan reduction process is used. For non-linear dynamic process where the non-linearity is present at interfaces between different structures, Craig-Bampton models can provide a very rich information, and allow easy selection of the relevant modes with respect to the phenomenon driving the non-linearity. The paper presents the employment of Craig-Bampton models combined with Newmark direct integration for solving non-linear friction problems appearing at the interface between the Hubble Space Telescope and its solar arrays during in-orbit maneuvers. Theory, implementation in the FEM code ASKA, and practical results are shown.
Non-linear dielectric monitoring of biological suspensions
Energy Technology Data Exchange (ETDEWEB)
Treo, E F; Felice, C J [Departamento de BioingenierIa, Universidad Nacional de Tucuman and Consejo Nacional de Investigaciones Cientificas y Tecnicas. CC327, CP4000, San Miguel de Tucuman (Argentina)
2007-11-15
Non-linear dielectric spectroscopy as a tool for in situ monitoring of enzyme assumes a non-linear behavior of the sample when a sinusoidal voltage is applied to it. Even many attempts have been made to improve the original experiments, all of them had limited success. In this paper we present upgrades made to a non-linear dielectric spectrometer developed and the results obtained when using different cells. We emphasized on the electrode surface, characterizing the grinding and polishing procedure. We found that the biological medium does not behave as expected, and the non-linear response is generated in the electrode-electrolyte interface. The electrochemistry of this interface can bias unpredictably the measured non-linear response.
A solution quality assessment method for swarm intelligence optimization algorithms.
Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua
2014-01-01
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.
Non-linear conductivity in Coulomb glasses
Energy Technology Data Exchange (ETDEWEB)
Voje, A.; Bergli, J. [Department of Physics, University of Oslo, P. O. Box 1048 Blindern, 0316 Oslo (Norway); Ortuno, M.; Somoza, A.M. [Departamento de Fisica - CIOyN, Universidad de Murcia, Murcia 30.071 (Spain); Caravaca, M.
2009-12-15
We have studied the nonlinear conductivity of two-dimensional Coulomb glasses. We have used a Monte Carlo algorithm to simulate the dynamic of the system under an applied electric field E. We have compared results for two different models: a regular square lattice with only diagonal disorder and a random array of sites with diagonal and off-diagonal disorder. We have found that for moderate fields the logarithm of the conductivity is proportional to {radical}(E)/T{sup 2}, reproducing experimental results. We have also found that in the nonlinear regime the site occupancy in the Coulomb gap follows a Fermi-Dirac distribution with an effective temperature T{sub eff} higher than the phonon bath temperature T. (Abstract Copyright [2009], Wiley Periodicals, Inc.)
Solutions of Multi Objective Fuzzy Transportation Problems with Non-Linear Membership Functions
Directory of Open Access Journals (Sweden)
Dr. M. S. Annie Christi
2016-11-01
Full Text Available Multi-objective transportation problem with fuzzy interval numbers are considered. The solution of linear MOTP is obtained by using non-linear membership functions. The optimal compromise solution obtained is compared with the solution got by using a linear membership function. Some numerical examples are presented to illustrate this.
Optimal Design of a Centrifugal Compressor Impeller Using Evolutionary Algorithms
Directory of Open Access Journals (Sweden)
Soo-Yong Cho
2012-01-01
Full Text Available An optimization study was conducted on a centrifugal compressor. Eight design variables were chosen from the control points for the Bezier curves which widely influenced the geometric variation; four design variables were selected to optimize the flow passage between the hub and the shroud, and other four design variables were used to improve the performance of the impeller blade. As an optimization algorithm, an artificial neural network (ANN was adopted. Initially, the design of experiments was applied to set up the initial data space of the ANN, which was improved during the optimization process using a genetic algorithm. If a result of the ANN reached a higher level, that result was re-calculated by computational fluid dynamics (CFD and was applied to develop a new ANN. The prediction difference between the ANN and CFD was consequently less than 1% after the 6th generation. Using this optimization technique, the computational time for the optimization was greatly reduced and the accuracy of the optimization algorithm was increased. The efficiency was improved by 1.4% without losing the pressure ratio, and Pareto-optimal solutions of the efficiency versus the pressure ratio were obtained through the 21st generation.
Directory of Open Access Journals (Sweden)
Imam Ahmad Ashari
2016-11-01
Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.
Directory of Open Access Journals (Sweden)
Imam Ahmad Ashari
2016-11-01
Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.
Sequential unconstrained minimization algorithms for constrained optimization
Byrne, Charles
2008-02-01
The problem of minimizing a function f(x):RJ → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G_k(x)=f(x)+g_k(x), to obtain xk. The auxiliary functions gk(x):D ⊆ RJ → R+ are nonnegative on the set D, each xk is assumed to lie within D, and the objective is to minimize the continuous function f:RJ → R over x in the set C=\\overline D , the closure of D. We assume that such minimizers exist, and denote one such by \\hat x . We assume that the functions gk(x) satisfy the inequalities 0\\leq g_k(x)\\leq G_{k-1}(x)-G_{k-1}(x^{k-1}), for k = 2, 3, .... Using this assumption, we show that the sequence {f(xk)} is decreasing and converges to f({\\hat x}) . If the restriction of f(x) to D has bounded level sets, which happens if \\hat x is unique and f(x) is closed, proper and convex, then the sequence {xk} is bounded, and f(x^*)=f({\\hat x}) , for any cluster point x*. Therefore, if \\hat x is unique, x^*={\\hat x} and \\{x^k\\}\\rightarrow {\\hat x} . When \\hat x is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton-Raphson method. The proof techniques used for SUMMA can be extended to obtain related results for the induced proximal
Adaptive backtracking search optimization algorithm with pattern search for numerical optimization
Institute of Scientific and Technical Information of China (English)
Shu Wang; Xinyu Da; Mudong Li; Tong Han
2016-01-01
Thebacktracking search optimization algorithm (BSA) is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capa-bility to find global optimal solutions. However, the algorithm is stil insufficient in balancing the exploration and the exploita-tion. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts: the BSA is used for the exploration phase and the modified pattern search method completes the ex-ploitation phase. In particular, a simple but effective strategy of adapting one of BSA’s important control parameters is intro-duced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Com-putation 2014 (IEEE CEC2014) over six widely-used bench-marks and 22 real-parameter single objective numerical opti-mization benchmarks in IEEE CEC2014. The results of ex-periment and statistical analysis demonstrate the effective-ness and efficiency of the proposed algorithm.
Optimization of reliability allocation strategies through use of genetic algorithms
Energy Technology Data Exchange (ETDEWEB)
Campbell, J.E.; Painton, L.A.
1996-08-01
This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.
Optimal Power Flow by Interior Point and Non Interior Point Modern Optimization Algorithms
Directory of Open Access Journals (Sweden)
Marcin Połomski
2013-03-01
Full Text Available The idea of optimal power flow (OPF is to determine the optimal settings for control variables while respecting various constraints, and in general it is related to power system operational and planning optimization problems. A vast number of optimization methods have been applied to solve the OPF problem, but their performance is highly dependent on the size of a power system being optimized. The development of the OPF recently has tracked significant progress both in numerical optimization techniques and computer techniques application. In recent years, application of interior point methods to solve OPF problem has been paid great attention. This is due to the fact that IP methods are among the fastest algorithms, well suited to solve large-scale nonlinear optimization problems. This paper presents the primal-dual interior point method based optimal power flow algorithm and new variant of the non interior point method algorithm with application to optimal power flow problem. Described algorithms were implemented in custom software. The experiments show the usefulness of computational software and implemented algorithms for solving the optimal power flow problem, including the system model sizes comparable to the size of the National Power System.
Non-linear Langevin model for the early-stage dynamics of electrospinning jets
Lauricella, Marco; Pisignano, Dario; Succi, Sauro
2015-01-01
We present a non-linear Langevin model to investigate the early-stage dynamics of electrified polymer jets in electrospinning experiments. In particular, we study the effects of air drag force on the uniaxial elongation of the charged jet, right after ejection from the nozzle. Numerical simulations show that the elongation of the jet filament close to the injection point is significantly affected by the non-linear drag exerted by the surrounding air. These result provide useful insights for the optimal design of current and future electrospinning experiments.
Performance Trend of Different Algorithms for Structural Design Optimization
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.
1996-01-01
Nonlinear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Center, a project was initiated to assess performance of different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with the sequential unconstrained minimizations technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.
Jin, Junchen
2016-01-01
The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998
Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces
Martin, Rodney A.; Wheeler, Kevin R.; SunSpiral, Vytas; Allan, Mark B.
2006-01-01
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.
A multi-group firefly algorithm for numerical optimization
Tong, Nan; Fu, Qiang; Zhong, Caiming; Wang, Pengjun
2017-08-01
To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). A Firefly colony is divided into several subgroups with different model parameters. Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. And then, each firefly achieves local search by following the brighter firefly in its neighbors. At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. Experimental results verify the effectiveness of the proposed algorithm.
A Cooperative Coevolutionary Cuckoo Search Algorithm for Optimization Problem
Directory of Open Access Journals (Sweden)
Hongqing Zheng
2013-01-01
Full Text Available Taking inspiration from an organizational evolutionary algorithm for numerical optimization, this paper designs a kind of dynamic population and combining evolutionary operators to form a novel algorithm, a cooperative coevolutionary cuckoo search algorithm (CCCS, for solving both unconstrained, constrained optimization and engineering problems. A population of this algorithm consists of organizations, and an organization consists of dynamic individuals. In experiments, fifteen unconstrained functions, eleven constrained functions, and two engineering design problems are used to validate the performance of CCCS, and thorough comparisons are made between the CCCS and the existing approaches. The results show that the CCCS obtains good performance in the solution quality. Moreover, for the constrained problems, the good performance is obtained by only incorporating a simple constraint handling technique into the CCCS. The results show that the CCCS is quite robust and easy to use.
A Cooperative Optimization Algorithm Inspired by Chaos–Order Transition
Directory of Open Access Journals (Sweden)
Fangzhen Ge
2015-01-01
Full Text Available The growing complexity of optimization problems in distributed systems (DSs has motivated computer scientists to strive for efficient approaches. This paper presents a novel cooperative algorithm inspired by chaos–order transition in a chaotic ant swarm (CAS. This work analyzes the basic dynamic characteristics of a DS in light of a networked multiagent system at microlevel and models a mapping from state set to self-organization mechanism set under the guide of system theory at macrolevel. A collaborative optimization algorithm (COA in DS based on the chaos–order transition of CAS is then devised. To verify the validity of the proposed model and algorithm, we solve a locality-based task allocation in a networked multiagent system that uses COA. Simulations show that our algorithm is feasible and effective compared with previous task allocation approaches, thereby illustrating that our design ideas are correct.
Non-linear vorticity upsurge in Burgers flow
Lam, F
2016-01-01
We demonstrate that numerical solutions of Burgers' equation can be obtained by a scale-totality algorithm for fluids of small viscosity (down to one billionth). Two sets of initial data, modelling simple shears and wall boundary layers, are chosen for our computations. Most of the solutions are carried out well into the fully turbulent regime over finely-resolved scales in space and in time. It is found that an abrupt spatio-temporal concentration in shear constitutes an essential part during the flow evolution. The vorticity surge has been instigated by the non-linearity complying with instantaneous enstrophy production while ad hoc disturbances play no role in the process. In particular, the present method predicts the precipitous vorticity re-distribution and accumulation, predominantly over localised regions of minute dimension. The growth rate depends on viscosity and is a strong function of initial data. Nevertheless, the long-time energy decay is history-independent and is inversely proportional to ti...
Improved Quantum-Inspired Evolutionary Algorithm for Engineering Design Optimization
Directory of Open Access Journals (Sweden)
Jinn-Tsong Tsai
2012-01-01
Full Text Available An improved quantum-inspired evolutionary algorithm is proposed for solving mixed discrete-continuous nonlinear problems in engineering design. The proposed Latin square quantum-inspired evolutionary algorithm (LSQEA combines Latin squares and quantum-inspired genetic algorithm (QGA. The novel contribution of the proposed LSQEA is the use of a QGA to explore the optimal feasible region in macrospace and the use of a systematic reasoning mechanism of the Latin square to exploit the better solution in microspace. By combining the advantages of exploration and exploitation, the LSQEA provides higher computational efficiency and robustness compared to QGA and real-coded GA when solving global numerical optimization problems with continuous variables. Additionally, the proposed LSQEA approach effectively solves mixed discrete-continuous nonlinear design optimization problems in which the design variables are integers, discrete values, and continuous values. The computational experiments show that the proposed LSQEA approach obtains better results compared to existing methods reported in the literature.
Using neural networks to speed up optimization algorithms
Bazan, M
2000-01-01
The paper presents the application of radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in computation times of about 30 minutes for each objective function evaluation (on a DEC-Alpha 600/333) and only the most robust (deterministic) optimization algorithms can be applied. Using a RBF function approximator, the achieved speed-up of the search algorithm is in the order of 25% for problems with two parameters and about 18% for problems with three and five design variables. (13 refs).
Application of Different Algorithms to Optimal Design of Canal Sections
Directory of Open Access Journals (Sweden)
A. Kentli
2014-08-01
Full Text Available Today the increased world population and therefore the growth demand has forced the researchers to investigate better water canal networks distributing much more water while at least keeping its quality. Canal design formulas are explicitly obtained for different cross-sections considering minimum area but optimal design of canal sections considering seepage and evaporation losses are still an open area to study. In this study, two different algorithms are applied to this problem and results are compared with the one in literature. Genetic algorithm and sequential quadratic programming technique are used in optimization. Triangular, rectangular and trapezoidal cross-sections are optimized. It is seen that both algorithms are giving more accurate results than in literature.
Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization
Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)
2002-01-01
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.
Localization of Non-Linearly Modeled Autonomous Mobile Robots Using Out-of-Sequence Measurements
Directory of Open Access Journals (Sweden)
Jesus M. de la Cruz
2012-02-01
Full Text Available This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.
Localization of non-linearly modeled autonomous mobile robots using out-of-sequence measurements.
Besada-Portas, Eva; Lopez-Orozco, Jose A; Lanillos, Pablo; de la Cruz, Jesus M
2012-01-01
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.
Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem
Directory of Open Access Journals (Sweden)
Kanagasabai Lenin
2015-04-01
Full Text Available In this paper, a novel approach Modified Monkey optimization (MMO algorithm for solving optimal reactive power dispatch problem has been presented. MMO is a population based stochastic meta-heuristic algorithm and it is inspired by intelligent foraging behaviour of monkeys. This paper improves both local leader and global leader phases. The proposed (MMO algorithm has been tested in standard IEEE 30 bus test system and simulation results show the worthy performance of the proposed algorithm in reducing the real power loss.
Distribution System Optimization Planning Based on Plant Growth Simulation Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Chun; CHENG Hao-zhong; HU Ze-chun; WANG Yi
2008-01-01
An approach for the integrated optimization of the construction/expansion capacity of high-voltage/medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant growth simulation algorithm (PGSA). In the optimization process, fixed costs correspondent to the investment in lines and substations and the variable costs associated to the operation of the system were considered under the constraints of branch capacity, substation capacity and bus voltage. The optimization variables considerably reduce the dimension of variables and speed up the process of optimizing. The effectiveness of the proposed approach was tested by a distribution system planning.
Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm
Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.
2017-02-01
Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.
Optimal Design of RF Energy Harvesting Device Using Genetic Algorithm
Mori, T.; Sato, Y.; Adriano, R.; Igarashi, H.
2015-11-01
This paper presents optimal design of an RF energy harvesting device using genetic algorithm (GA). In the present RF harvester, a planar spiral antenna (PSA) is loaded with matching and rectifying circuits. On the first stage of the optimal design, the shape parameters of PSA are optimized using . Then, the equivalent circuit of the optimized PSA is derived for optimization of the circuits. Finally, the parameters of RF energy harvesting circuit are optimized to maximize the output power using GA. It is shown that the present optimization increases the output power by a factor of five. The manufactured energy harvester starts working when the input electric field is greater than 0.5 V/m.
Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods
Saadia Zahid; Fawad Hussain; Muhammad Rashid; Muhammad Haroon Yousaf; Hafiz Adnan Habib
2015-01-01
Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount o...
Theory, Design, and Algorithms for Optimal Control of wireless Networks
2010-06-09
significantly outperform existing protocols (such as AODV ) in terms of total network cost Furthermore, we have shown that even when components of our...achieved through distributed control algorithms that jointly optimize power control, routing , and congestion factors. A second stochastic model approach...updates the network queue state, node-transmission powers amongst others, allowing for power control, scheduling, and routing algorithms to maximize
A coordinate-wise optimization algorithm for the Fused Lasso
Höfling, Holger; Binder, Harald; Schumacher, Martin
2010-01-01
L1 -penalized regression methods such as the Lasso (Tibshirani 1996) that achieve both variable selection and shrinkage have been very popular. An extension of this method is the Fused Lasso (Tibshirani and Wang 2007), which allows for the incorporation of external information into the model. In this article, we develop new and fast algorithms for solving the Fused Lasso which are based on coordinate-wise optimization. This class of algorithms has recently been applied very successfully to so...
Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence
Energy Technology Data Exchange (ETDEWEB)
Pelikan, M.; Goldberg, D.E.; Cantu-Paz, E.
2000-01-19
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.
Asynchronous Nested Optimization Algorithms and Their Parallel Implementation
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Large scale optimization problems can only be solved in anefficient way, if their special structure is taken as the basis of algorithm design. In this paper we consider a very broad class of large-scale problems with special structure, namely tree structured problems. We show how the exploitation of the structure leads to efficient decomposition algorithms and how it may be impl emented in a parallel environment.