Computational Optimization Methods Research Articles

Dipole sources of the main geomagnetic field

The parameters of 15 arbitrary dipoles that, in aggregate, represent the main geomagnetic field (MGF) are estimated to obtain information on the distribution of MGF sources within the Earth in the form of dipoles with an arbitrary position and value of the magnetic moment vector. For an adequate estimation of the results, the method of obtaining the data is described, including: the eccentric dipole model and the derivation of the basic formulas for the magnetic field components of an arbitrary dipole; the method of estimating the parameters of these dipoles, including the computational scheme and optimization method; necessary constraints on the dipoles parameters and a justification of the choice of the initial values in the optimization fitting of the parameters. The results are presented as a map of the location of the centers of the dipoles and their northern axial poles for the epochs 1955 and 2005 and plots of changes in all six parameters of 15 dipoles for 50years. Most of the dipole centers are located in the lower mantle. The results suggest the existence of current systems in the lower mantle that produce dipole magnetic fields. These currents are provided by the high conductivity of wustite, an important component of the mantle, which, at a depth of 1000-2200km, transforms to the low-spin state of iron with increased density and electrical conductivity.

Short Note on Computational Intelligent Techniques for Industrial Production Systems

Computational intelligent has been accepted and recognized by global research scientists, decision makers and practicing researchers in recent years as powerful artificial intelligent techniques, for solving unlimited number of complex real-world problems particularly related to research area of unpredictable and uncertain optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete satisfactory solution with higher level of degree of satisfaction for the real world practical problems on optimization. Therefore, new global computational intelligent optimization methods are required to handle these issues seriously. One such method is hybrid evolutionary computation, a generic, flexible, robust, beneficial and versatile framework for solving complex and unpredictable problems of global optimization and search in real world application problems.

Computational methods in drug discovery.

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Honey Bees Inspired Optimization Method: The Bees Algorithm.

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering

Computational optimization methods are most often used to find a single or multiple optimal or near-optimal solutions to the underlying optimization problem describing the problem at hand. In this paper, we elevate the use of optimization to a higher level in arriving at useful problem knowledge associated with the optimal or near-optimal solutions to a problem. In the proposed innovization process, first a set of trade-off optimal or near-optimal solutions are found using an evolutionary algorithm. Thereafter, the trade-off solutions are analyzed to decipher useful relationships among problem entities automatically so as to provide a better understanding of the problem to a designer or a practitioner. We provide an integrated algorithm for the innovization process and demonstrate the usefulness of the procedure to three real-world engineering design problems. New and innovative design principles obtained in each case should clearly motivate engineers and practitioners for its further application to more complex problems and its further development as a more efficient data analysis procedure.

Fast and robust technique for design of axisymmetric TIR optics in case of an extended light source

We propose a new optimization method for computation of axisymmetric total internal reflection (TIR) optics for light-emitting diodes (LEDs). The method is designed for the general case of an extended light source, and is based on the analytical computation of an initial TIR profile for a point light source and its subsequent optimization. The distinctive feature of the proposed method is low computation time, which is a result of using in the optimization process of the adapted ray-tracing technique, based on the representation of optical surfaces by a set of truncated cones. The TIR optical element, generating a uniformly illuminated circular area with an angular size of 60°, is computed for the Lambert light source with a size of 1 × 1 mm2. The results of simulations prove the high performance of the proposed technique.

TH-A-WAB-05: Selection of Candidate Lesions for Image-Guided Bone Biopsy Using [F-18]NaF PET/CT Response to Therapy

Purpose: Site selection for image‐guided biopsies in patients with multiple lesions is typically based on physician preference and clinical feasibility. This study aims to develop a method to additionally incorporate quantitative imaging data for automatic selection of candidate lesions for post‐treatment bone biopsy, using [18F]NaF response metrics in combination with physician feasibility. Methods: 12 metastatic prostate cancer patients undergoing conventional treatment received whole‐body [18F]NaF PET/CT scans pre‐and mid‐treatment. We quantitatively optimized biopsy lesion selection by maximizing a lesion‐specific objective function score(OFS). Here, OFS is defined as the weighted global sum of seven criteria separated into two categories: 1)physician‐feasibility category including physician‐preferred lesion location and absolute volume scores, and 2)imaging‐response category including four imaging response metrics (lesion volume, SUVmax, SUVmean, SUVtotal %change from pre‐to mid‐treatment) and an overall response score. Criteria were weighted evenly within each category. Weighting ratios between physician‐feasibility and imaging‐response (PF:IR) were varied from 2:1 to 1:2 to test OFS sensitivity. Results: 102 of 650+ total lesions met minimum criteria to qualify for biopsy. Within patients, maximum OFS ranged from <1%–27% greater than the next highest scoring lesion, indicating some patients have multiple lesions desirable for biopsy. Assessing sensitivity, within‐patient lesion rankings based on OFS values were stable from 1:1‐to‐2:1 (R=0.94), 1:1‐to‐1:2 (R=0.96), and from 1:2‐to‐2:1 (R=0.81) for all lesions. Compared to 1:1, the lesion identified as having the maximum OFS remained top‐seeded in 5 patients and in the top three highest ranking lesions in all patients for both 1:2 and 2:1. After selecting optimal candidates, biopsy feasibility was confirmed by the physician for 32/36 high‐ranking lesions, of which all top choices were confirmed. Conclusion: This newly developed computational optimization method was successfully used to quantitatively identify candidate lesions for bone biopsies. The method was robust to different weighting ratios and candidacy for biopsy was physician confirmed.

Optimization-based decision support for scenario analysis in electronic sourcing markets with volume discounts

E-Sourcing software has become an integral part of electronic commerce. Beyond the use of single-lot auction formats, there has been an emerging interest in using e-sourcing software for complex negotiations. Procurement markets typically exhibit scale economies leading to various types of volume discounts which are in wide-spread use in practice. The analysis of bids in such negotiations typically leads to computationally hard optimization problems. Scenario analysis describes a process, in which procurement managers compute different award allocations as a result of different allocation constraints and parameters that they put in place. This paper discusses an optimization model and computational methods which allow for effective scenario analysis with allocation problems in the presence of different types of discount policies and allocation constraints. The model reduces the number of parameter settings to explore considerably. The models are such that they can often not be solved exactly for realistic problem sizes in practically acceptable time frames. Therefore, we provide results of numerical experiments using exact algorithms and heuristics to solve the problem. We find that RINS and Variable Neighborhood Search can be effectively used in traditional branch-and-cut algorithms for this problem. Overall, new computational approaches allow procurement managers to evaluate offers even in markets with a complex set of volume discounts and multiple allocation constraints.

Soft Computing Methods in Business Optimization

There are some tasks that nature manages to perform very easily but which algorithms designed by human beings cannot complete. We can find these tasks in complicated and variable environments. Mathematicians have turned their attention to nature, and on the basis of analogy they created the fuzzy logic, neural networks, and evolutionary algorithms theories. There are various areas of use, such as optimization in management, risk management, decision making, the stock market, technology control, simulation, prediction, cluster analyses, and other branches of applications in business.

Soft Computing Methods in Business Optimization

Soft Computing Methods in Business Optimization

Computational Methods for On-Node Performance Optimization and Inter-Node Scalability of HPC Applications

In the age of multi-core and specialized accelerators in high performance computing (HPC) systems, it is critical to understand application characteristics and apply suitable optimizations in order to fully utilize advanced computing system. Often time, the process involves multiple stages of application performance diagnosis and a trial-and-error type of approach for optimization. In this study, a general guideline of performance optimization has been demonstrated with two class-representing applications. The main focuses are on node-level optimization and inter-node scalability improvement. While the number of optimization case studies is somewhat limited in this paper, the result provides insights into the systematic approach in HPC applications performance engineering.

Parameter Estimation Based on Evolutionary Computation for P-Class Chaotic Systems

The problem of parameter estimation in chaotic systems is important to deal with current engineering problems. Obtaining and understanding precise information about chaotic system dynamics can be potentially exploited. The parameter estimation should be focussed as a problem with multimodal non-continuous functions, where some methods get stuck in local minimum. This problem can be dealt with optimization computational methods. In this work, estimation of parameters and initial conditions for P-class chaotic systems is achieved using evolutionary computation.

A computational method for geometric optimization of enhanced heat transfer devices based upon entropy generation minimization

SUMMARYA computational fluid dynamics‐based optimization methodology is developed, appropriate for the geometric optimization of enhanced heat transfer devices based upon the principle of entropy generation minimization, in which the objective function is evaluated from a flow field obtained by computational simulation. A quasi‐Newton optimization procedure is employed, with computation of the objective function gradients based upon a finite difference approach. The optimization procedure is developed to be general with regard to the choice of objective function, the details of the problem under consideration, and the computational methodology employed in solving the fluid flow and heat transfer problems. A novel implementation of a Taylor series‐based procedure for the fast solution of nearby problems is presented, which is found to greatly benefit the efficiency of the present methodology. Finally, a numerical experiment is presented, illustrating the use of the present method in the geometric optimization of a practical enhanced heat transfer device on the basis of the criterion of entropy generation minimization. The optimization of the fin spacing of a simple plate fin heat sink is considered, and a comparison of the computational results with results obtained by analytical optimization based upon empirical friction factor and Nusselt number correlations is given. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Two-Level Wing-Body-Fairing Optimization of a Civil Transport Aircraft

HE overall design objective for a civil transport aircraft is to minimize its direct operating cost and hence to maximize the profit potential for airlines. This can translate into a goal of minimization of the cruise drag in the aerodynamic process based on principles of the Breguet range equation. Aerodynamic design of the wing is crucial for achieving the goal, but as the fidelity of the computational fluid dynamics method increased significantly, the detaileddesignofanumberoffeaturesincludingenginepylons, flaptrackfairing,andwing-bodyfairinghasbeguntoplaymoreandmore important roles in pushing the efficiency boundaries further. The development of aerodynamic optimization has largely been driven by progresses made in the fields of computational fluid dynamics, parametric geometry and efficient optimization methods, as illustrated by increasing number of published papers in the field [1,2]. The engineering application of the methodology is also enabled by the rapid development and increasing affordability of high-performance computing facilities. With increasing demand to improve fuel efficiency for civil aircraft and more awareness on aviation’s environmental impact, the pursuit for higher fuel efficiency has promoted the technology development in the aerospace industry across awide range of topics, including new materials to reduce weight, new engines with larger bypass ratios, and improved aerodynamic design. Aerodynamic shape optimization using computational fluid dynamics (CFD) has become an indispensable tool in aircraft design process. Much of the previous effort has been focused on airfoil and wing design optimization in order to achieve the maximum possible benefits from using the state-of-the-art computational tools. Two approaches aregenerally exploredto improvethe lift-to-drag ratio of airfoil and wing design. The first approach is following the philosophy of inverse design procedure [3], in which a gradual manipulation of the geometry is attempted to match a pressure distribution given based on past experiences. In the second framework, a numerical optimization algorithm is coupled with parametric geometry definition and CFD codes to optimize the aerodynamic merits of interests, normally the maximization of lift-to-drag ratio subject to constraints on lift and other criteria such as structural weight. It is also possible to combine the two approaches in a complex design procedure in order to benefit from both past experiences and increasing capabilities of numerical optimization [4]. The primary role in designing the wing-body fairing is to provide aerodynamic streamlined flow while encapsulating the internal structures and equipment. The flow passing the fairing is controlled by varying the convexity of the surface in both streamwise and crossflow directions. The presence of local deformation of the concave shape and convex shape will lead to expansion and compression waves, respectively. By a careful combination of concave andconvexdeformations,the flowoverthesurfacecanbe controlled to generate the desired pressure distribution. Therefore, it is essential to allow flexible variation of surface curvatures in the geometry design of wing-body fairing. The choice ofshapeparametersofthegeometrydefinitioninanydesignwillalso play an important role in achieving the balance between geometry flexibility and optimization efficiency. It can be recalled that in addition to fairing geometry, two primary types of aerodynamic shape can be seen in aircraft design: lifting surfaces and fuselagetype geometry. The definition of the fuselage is normally stipulated by space and structural requirements such as pressurization of the passenger cabin. The lifting surface is generally defined by lofting through a series of airfoil shapes to generate desired aerodynamic characteristics. This paper is organized as follows. Section II introduces related work. Section III describes the main contributions of the paper: a two-level geometry modeling approach and corresponding optimizationframework.Detailsonhowthegeometryisdefinedarealso presented. Optimization procedures using a combination of response-surface-based approach and manual intervention are given in Sec. IV. This is followed by conclusions presented in Sec. V.

Topology optimization of continuum structures under uncertainty – A Polynomial Chaos approach

A computational method for topology optimization in the presence of uncertainty is proposed. The method combines the spectral stochastic approach for the representation and propagation of uncertainties with an existing deterministic topology optimization technique. The idea in spectral stochastic formulations is to add an extra dimension, a random dimension, to the problem where the stochastic variability of the input parameters (and outputs of interest) can be modeled. The resulting compact representations for the response quantities allow for efficient and accurate calculation of sensitivities of response statistics with respect to the design variables which are then fed into a gradient-based optimizer that searches for the optimum design. From a variety of frameworks for uncertainty-informed optimization, robust topology optimization is chosen to demonstrate the applicability of the method. Examples from continuum topology optimization under uncertainty in material properties are presented. It is also shown that results obtained from the proposed method are in excellent agreement with those obtained from a Monte Carlo-based optimization algorithm.

Particle Swarm Optimization: Technique, System and Challenges

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven’t been made and which one the modified or variant PSO that will be developed.

Pattern Synthesis of Concentric Ring Array Antennas by Differential Evolution Algorithm

In this paper, we propose a computational global optimization method based on differential evolution (DE) algorithm for synthesizing large multiple concentric rings arrays to generate a pencil beam with minimum peak side lobe level (PSLL) and constrained first null beam width (FNBW). The synthesis is performed with a differential evolution technique to optimize the ring spacing, the number of the elements in each ring as well as the amplitude distribution. Simulated results of the designed synthesis illustrate the efficiency and reliability of our proposed method.

How to Install Sensors for Structural Model Updating?

Structural model updating can be treated as the process to extract information from measurement for modifying the structural model such that the model calculated responses fit the measured data. The updated model is very important for structural response prediction, structural damage detection and structural control. The location for sensor installation has significant effect on the amount of information that can be extracted from the measured data. This paper presents a methodology for identifying the “optimal” locations to install a given number of sensors on a structure so as to find useful information for structural model updating. The proposed method relies on the information entropy as a measure of the uncertainties associated with the identified model parameters for a given sensor configuration. The larger the value of information entropy, the higher the uncertainty of the identified model parameters will be. As a result, the problem of optimal sensor placement can be transformed to a discrete optimization problem with the information entropy as the objective function and the sensor configuration as the minimization variable. However, the corresponding numerical minimization problem is computational demanding for real structures with many degrees of freedom (DOFs). One of the contributions of this paper is to propose a computational efficient optimization method based on genetic algorithm for solving this minimization problem. A model of typical transmission tower with 40 nodes, 160 elements and 216 DOFs is used as a numerical example to illustrate the proposed methodology. The computational time of the proposed optimization method can be future reduced by making use of parallel computing technologies.

Bis(μ-oxo) Dicopper(III) Species of the Simplest Peralkylated Diamine: Enhanced Reactivity toward Exogenous Substrates

N,N,N',N'-tetramethylethylenediamine (TMED), the simplest and most extensively used peralkylated diamine ligand, is conspicuously absent from those known to form a bis(μ-oxo)dicopper(III) (O) species, [(TMED)(2)Cu(III)(2)(μ(2)-O)(2)](2+), upon oxygenation of its Cu(I) complex. Presented here is the characterization of this O species and its reactivity toward exogenous substrates. Its formation is complicated both by the instability of the [(TMED)Cu(I)](1+) precursor and by competitive formation of a presumed mixed-valent trinuclear [(TMED)(3)Cu(III)Cu(II)(2)(μ(3)-O)(2)](3+) (T) species. Under most reaction conditions, the T species dominates, yet, the O species can be formed preferentially (>80%) upon oxygenation of acetone solutions, if the copper concentration is low (<2 mM) and [(TMED)Cu(I)](1+) is prepared immediately before use. The experimental data of this simplest O species provide a benchmark by which to evaluate density functional theory (DFT) computational methods for geometry optimization and spectroscopic predictions. The enhanced thermal stability of [(TMED)(2)Cu(III)(2)(μ(2)-O)(2)](2+) and its limited steric demands compared to other O species allows more efficient oxidation of exogenous substrates, including benzyl alcohol to benzaldehyde (80% yield), highlighting the importance of ligand structure to not only enhance the oxidant stability but also maintain accessibility to the nascent metal/O(2) oxidant.

Determination of Optimal Pulse Metal Inert Gas Welding Parameters with a Neuro-GA Technique

Optimization of a manufacturing process is a rigorous task because it has to take into account all the factors that influence the product quality and productivity. Welding is a multi-variable process, which is influenced by a lot of process uncertainties. Therefore, the optimization of welding process parameters is considerably complex. Advancement in computational methods, evolutionary algorithms, and multiobjective optimization methods create ever-more effective solutions to this problem. This work concerns the selection of optimal parameters setting of pulsed metal inert gas welding (PMIGW) process for any desired output parameters setting. Six process parameters, namely pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate and table feed rate were used as input variables, and the strength of the welded plate, weld bead geometry, transverse shrinkage, angular distortion and deposition efficiency were considered as the output variables. Artificial neural network (ANN) models were used for mapping input and output parameters. Neuro genetic algorithm (Neuro-GA) technique was used to determine the optimal PMIGW process parameters. Experimental result shows that the designed parameter setting of PMIGW process, which was obtained from Neuro-GA optimization, indeed produced the desired weld-quality.