Dead code elimination based pointer analysis for multithreaded programs

This paper presents a new approach for optimizing multitheaded programs with pointer constructs. The approach has applications in the area of certified code (proof-carrying code) where a justification or a proof for the correctness of each optimization is required. The optimization meant here is that of dead code elimination.Towards optimizing multithreaded programs the paper presents a new operational semantics for parallel constructs like join-fork constructs, parallel loops, and conditionally spawned threads. The paper also presents a novel type system for flow-sensitive pointer analysis of multithreaded programs. This type system is extended to obtain a new type system for live-variables analysis of multithreaded programs. The live-variables type system is extended to build the third novel type system, proposed in this paper, which carries the optimization of dead code elimination. The justification mentioned above takes the form of type derivation in our approach.     

The Optimization of Slender Reinforced Concrete Columns

Slenderness is an important issue in design of reinforced concrete (RC) columns. Especially for long columns, second order effects may be not so small to neglect, but the calculation of second order effects may take too much time. For that reason, ACI 318 design code includes a simple approach in order to increase the flexural moment of columns according to their slenderness. Thus, second order effects are considered. In optimization, the effect of slenderness can be considered by using the factored design flexural moments. In this paper, harmony search (HS) algorithm is employed to find the optimum design variables of slender RC columns. These design variables are web width, height, diameter and number of reinforcements. The optimization objective is total cost of materials including concrete and steel. The developed method is effective to find the optimal design for axial force, flexural moment and shear force values. As numerical examples, optimum design of columns with different lengths, but with the same loadings and material properties were investigated. Thus, the effect of slenderness was seen on the optimum costs. By the increase of column length, increase of total material cost is more than a linear increase. This situation shows us the effect of slenderness on optimum RC columns (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

General shape optimization capability

Structural optimization is almost as old as the finite element method (FEM). Whereas FEM found its way to real-life applications very quickly, structural optimization remained a topic of interest in the research community for many years. However, there have been a number of attemps recently to develop general purpose program systems for property optimization. For shape optimization, there is no general purpose code currently available that can solve realistic problems. This paper will describe a method of calculating shape sensitivities within , in a simple manner, without resort to external programs. Once the shape sensitivities are obtained, the shape optimization process can proceed in a manner similar to property optimization. The key concept is the use of natural design variables to define the shape changes in a given structure. The design variables are the magnitudes of enforced displacements applied to the structure. The displacements produced by these variables are added to the initial shape to obtain a new shape. This approach can be computationally intensive and since one shape variable is dependent of another, multiple CPU's can be used to significantly reduce the solution time.

Two examples are solved to demonstrate the capability of these techniques. The first is a cantilever beam with holes loaded by a point load at the free end. The shape of the holes as well as the thickness of the beam are selected as the design variables. The second example is the shape optimization of a connecting rod subjected to several different loading and boundary conditions.     

Isogeometric structural shape optimization using a fictitious energy regularization

In isogeometric analysis, NURBS basis functions are used as shape functions in an isoparametric finite-element-type discretization. Among other advantageous features, this approach is able to provide exact and smooth representations of a broad class of computational domains with curved boundaries. Therefore, this discretization method seems to be especially convenient for computational shape optimization, where a smooth and CAD-like parametrization of the optimal geometry is desired. Choosing boundary control point coordinates of an isogeometric discretization as design variables, an additional design model can be avoided. However, for a higher number of design variables, typical drawbacks like oscillating boundaries as known from early node-based shape optimization methods appear. To overcome this problem, we propose to use a fictitious energy regularization: the strain energy of a fictitious deformation, which maps the initial to the optimized domain, is employed as a regularizing term in the optimization problem. Moreover, this deformation is used for efficiently moving the dependent nodes within the domain in each step of the optimization process. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Optimización mixta de estructuras de transporte de energía: aplicación del algoritmo de recocido simulado

A general methodology to optimize the weight of power transmission structures is presented in this article. This methodology is based on the simulated annealing algorithm defined by Kirkpatrick in the early ‘80s. This algorithm consists of a stochastic approach that allows to explore and analyze solutions that do not improve the objective function in order to develop a better exploration of the design region and to obtain the global optimum. The proposed algorithm allows to consider the discrete behavior of the sectional variables for each element and the continuous behavior of the general geometry variables. Thus, an optimization methodology that can deal with a mixed optimization problem and includes both continuum and discrete design variables is developed. In addition, it does not require to study all the possible design combinations defined by discrete design variables. The algorithm proposed usually requires to develop a large number of simulations (structural analysis in this case) in practical applications. Thus, the authors have developed first order Taylor expansions and the first order sensitivity analysis involved in order to reduce the CPU time required. Exterior penalty functions have been also included to deal with the design constraints. Thus, the general methodology proposed allows to optimize real power transmission structures in acceptable CPU time.     

Toward design and fabrication of wind-driven vehicles: Procedure to optimize the threshold of driving forces

Wind energy has been continuously considered as a green, available, and economical alternative source of energy. For centuries, the transformed wind energy to drag-force has been used for transportation in watercrafts. With improvement of aerodynamics, the airfoil was invented to create and use a higher magnitude aerodynamic force, lift-force, in order to elevate airplanes. Later, the lift-force was horizontally applied as the thrust force in land/water wind-crafts. Whereas in airplanes horizontal airfoils (wing) create a vertical lift-force, installed vertical airfoils (wing-sail) produce a horizontal lift-force in wind-crafts. Therefore, this force can be used as thrust (driving) force in lift-based ice, water, and land vehicles. If the prevailing wind is constantly available, the vehicle speed can even exceed the wind velocity. Due to the complex kinematics of such vehicles, however, it should be noted that there would be always an optimum for the thrust force in order to control and navigate the vehicle to the destination point, and to avoid the severe undesired side-forces. This optimum is calculated in wind-craft trajectory software (WTS) which requires many inputs, including variable and constant parameters. Variable parameters consist of wind direction and magnitude in addition to vehicle’s position, velocities, and accelerations. On the other hand, design characteristics of the wind-driven vehicle are known as constant parameters. The land-yacht body’s drag is an unknown constant parameter which alters according to the relative wind. This implies that several wind tunnel experiment in different wind directions and speeds are required in order to obtain the drag coefficients.Therefore in order to bypass the wind tunnel measurements, this study aims to propose a fast and economical procedure to find the aforementioned drag coefficient by integration of a measurement and by a simulation approach. The obtained data can be later used in the optimization and control module of the WTS. The performance of this procedure has been investigated using a case study. For this purpose, a 1:4 prototype three-wheel land-yacht is first designed and fabricated. The land-yacht comprises of three major parts; horizontal airfoil (axle), vertical airfoil, and body. The dimensions of these elements are obtained after development of a code based on kinematics of the land-yacht. The axle is designed to increase the stability of the land-yacht, whereas the shape of the body is intended to produce a low drag coefficient in various directions. Furthermore, a set of experiments has been conducted to measure the body drag of the land-yacht in a direction parallel to the relative wind. This experiment is later used to develop and validate a computational fluid dynamics (CFD) model in order to estimate the drag of the land-yacht body in its various directions against the relative wind. The results show the adequate efficacy of this procedure to provide the required data for the optimization and control module of the WTS.     

Parallel structural optimization applied to bone remodeling on distributed memory machines

In this paper, we investigate parallel structural optimization methods on distributed memory MIMD machines. We have restricted ourselves to the case of minimizing a multivariate non-linear function subject to bounds on the independent variables, when the objective function is expensive to evaluate as compared to the linear algebra portion of the optimization. This is the case in structural applications, when a large three-dimensional finite element mesh is used to model the structure.This paper demonstrates how parallelism can be exploited during the function and gradient computation as well as the optimization iterations. For the finite element analysis, a torus wrap skyline solver is used. The reflective Newton method, which attempts to reduce the number of iterations at the expense of more linear algebra per iteration, is compared with the more conventional active set method. All code is developed for an Intel iPSC/860, but can be ported to other distributed memory machines.The methods developed are applied to problems in bone remodeling. In the area of biomechanics, optimization models can be used to predict changes in the distribution of material properties in bone due to the presence of an artificial implant. The model we have used minimizes a linear combination of the mass and strain energy in the entire domain subject to bounds on the densities in each finite element.Early results show that the reflective Newton method can outperform active set methods when few variables are active at the minimum.     

Multicriterion Aerodynamic Shape Design Optimization and Inverse Problems Using Control Theory and Nash Games

Multicriterion design is gaining importance in aeronautics in order to cope with new needs of society. In the literature, contributions to single discipline and/or single-point design optimization abound. The goal of this paper is to introduce a new approach combining the adjoint method with a formulation derived from game theory for multipoint aerodynamic design problems. Transonic flows around lifting airfoils are analyzed via Euler computations. Airfoil shapes are optimized according to various aerodynamic criteria. The notion of player is introduced. In a competitive Nash game, each player attempts to optimize its own criterion through a symmetric exchange of information with others. A Nash equilibrium is reached when each player, constrained by the strategy of the others, cannot improve further its own criterion. Specific real and virtual symmetric Nash games are implemented to set up an optimization strategy for design under conflict. This work has benefited partially from the support of the National Science Foundation of China Grant NSFC-10372040 and Scientific Research Foundation (SRF) for Returned Overseas Chinese Scholars (ROCS) Grant 2003-091. The first author acknowledges the support of INRIA (Institut National de Recherche en Information et en Automatique), France.     

Generation of efficient frontiers in multi-objective optimization problems by generalized data envelopment analysis

In many practical problems such as engineering design problems, criteria functions cannot be given explicitly in terms of design variables. Under this circumstance, values of criteria functions for given values of design variables are usually obtained by some analyses such as structural analysis, thermodynamical analysis or fluid mechanical analysis. These analyses require considerably much computation time. Therefore, it is not unrealistic to apply existing interactive optimization methods to those problems. On the other hand, there have been many trials using genetic algorithms (GA) for generating efficient frontiers in multi-objective optimization problems. This approach is effective in problems with two or three objective functions. However, these methods cannot usually provide a good approximation to the exact efficient frontiers within a small number of generations in spite of our time limitation. The present paper proposes a method combining generalized data envelopment analysis (GDEA) and GA for generating efficient frontiers in multi-objective optimization problems. GDEA removes dominated design alternatives faster than methods based on only GA. The proposed method can yield desirable efficient frontiers even in non-convex problems as well as convex problems. The effectiveness of the proposed method will be shown through several numerical examples.     

A General Hybrid Optimization Strategy for Curve Fitting in the Non-uniform Rational Basis Spline Framework

In this paper, a general methodology to approximate sets of data points through Non-uniform Rational Basis Spline (NURBS) curves is provided. The proposed approach aims at integrating and optimizing the full set of design variables (both integer and continuous) defining the shape of the NURBS curve. To this purpose, a new formulation of the curve fitting problem is required: it is stated in the form of a constrained nonlinear programming problem by introducing a suitable constraint on the curvature of the curve. In addition, the resulting optimization problem is defined over a domain having variable dimension, wherein both the number and the value of the design variables are optimized. To deal with this class of constrained nonlinear programming problems, a global optimization hybrid tool has been employed. The optimization procedure is split in two steps: firstly, an improved genetic algorithm optimizes both the value and the number of design variables by means of a two-level Darwinian strategy allowing the simultaneous evolution of individuals and species; secondly, the optimum solution provided by the genetic algorithm constitutes the initial guess for the subsequent gradient-based optimization, which aims at improving the accuracy of the fitting curve. The effectiveness of the proposed methodology is proven through some mathematical benchmarks as well as a real-world engineering problem.     

Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation

The paper is concerned with a hybrid optimization of fuzzy inference systems based on hierarchical fair competition-based parallel genetic algorithms (HFCGA) and information granulation. The process of information granulation is realized with the aid of the C-Means clustering. HFCGA being a multi-population based parallel genetic algorithms (PGA) is exploited here to realize structure optimization and carry out parameter estimation of the fuzzy models. The HFCGA becomes helpful in the context of fuzzy models as it restricts a premature convergence encountered quite often in optimization problems. It concerns a set of parameters of the model including among others the number of input variables to be used, a specific subset of input variables, and the number of membership functions. In the hybrid optimization process, two general optimization mechanisms are explored. The structural development of the fuzzy model is realized via the HFCGA optimization and C-Means, whereas to deal with the parametric optimization we proceed with a standard least square method and the use of the HFCGA technique. A suite of comparative studies demonstrates that the proposed algorithm leads to the models whose performance is superior in comparison with some other constructs commonly used in fuzzy modeling.     

Massively parallel differential evolution—pattern search optimization with graphics hardware acceleration: an investigation on bound constrained optimization problems

This paper presents a novel parallel Differential Evolution (DE) algorithm with local search for solving function optimization problems, utilizing graphics hardware acceleration. As a population-based meta-heuristic, DE was originally designed for continuous function optimization. Graphics Processing Units (GPU) computing is an emerging desktop parallel computing technology that is becoming popular with its wide availability in many personal computers. In this paper, the classical DE was adapted in the data-parallel CPU-GPU heterogeneous computing platform featuring Single Instruction-Multiple Thread (SIMT) execution. The global optimal search of the DE was enhanced by the classical local Pattern Search (PS) method. The hybrid DE–PS method was implemented in the GPU environment and compared to a similar implementation in the common computing environment with a Central Processing Unit (CPU). Computational results indicate that the GPU-accelerated SIMT-DE-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid DE–PS with GPU acceleration. The research results demonstrate a promising direction for high speed optimization with desktop parallel computing on a personal computer.     

Subset simulation for unconstrained global optimization

Global optimization problem is known to be challenging, for which it is difficult to have an algorithm that performs uniformly efficient for all problems. Stochastic optimization algorithms are suitable for these problems, which are inspired by natural phenomena, such as metal annealing, social behavior of animals, etc. In this paper, subset simulation, which is originally a reliability analysis method, is modified to solve unconstrained global optimization problems by introducing artificial probabilistic assumptions on design variables. The basic idea is to deal with the global optimization problems in the context of reliability analysis. By randomizing the design variables, the objective function maps the multi-dimensional design variable space into a one-dimensional random variable. Although the objective function itself may have many local optima, its cumulative distribution function has only one maximum at its tail, as it is a monotonic, non-decreasing, right-continuous function. It turns out that the searching process of optimal solution(s) of a global optimization problem is equivalent to exploring the process of the tail distribution in a reliability problem. The proposed algorithm is illustrated by two groups of benchmark test problems. The first group is carried out for parametric study and the second group focuses on the statistical performance.     

A Structured Reduced Sequential Quadratic Programming and Its Application to a Shape Design Problem

The objective of this work is to solve a model one dimensional duct design problem using a particular optimization method. The design problem is formulated as an equality constrained optimization, called all at once method, so that the analysis problem is not solved until the optimal design is reached. Furthermore, the sparsity structure in the Jacobian of the linearized constraints is exploited by decomposing the variables into the design and flow parts. To achieve this, sequential quadratic programming with BFGS update for the reduced Hessian of the Lagrangian function is used with the variable reduction method which preserves the structure of the Jacobian in representing the null space basis matrix. By updating the reduced Hessians of which the dimension is the number of design variables, the storage requirement for the Hessians is reduced by a large amount. In addition, the flow part of the Jacobian can be computed analytically.The algorithm with a line search globalization is described. A global and local analysis is provided with a modification of the paper by Byrd and Nocedal [Mathematical Programming 49(1991) pp 285-323] in which they analyzed a similar algorithm with the orthogonal factorization method which assumes the orthogonality of the null space basis matrix. Numerical results are obtained and compared favorably with results from the black box method, unconstrained optimization formulation.     

Efficient computer experiment-based optimization through variable selection

A computer experiment-based optimization approach employs design of experiments and statistical modeling to represent a complex objective function that can only be evaluated pointwise by running a computer model. In large-scale applications, the number of variables is huge, and direct use of computer experiments would require an exceedingly large experimental design and, consequently, significant computational effort. If a large portion of the variables have little impact on the objective, then there is a need to eliminate these before performing the complete set of computer experiments. This is a variable selection task. The ideal variable selection method for this task should handle unknown nonlinear structure, should be computationally fast, and would be conducted after a small number of computer experiment runs, likely fewer runs (N) than the number of variables (P). Conventional variable selection techniques are based on assumed linear model forms and cannot be applied in this “large P and small N” problem. In this paper, we present a framework that adds a variable selection step prior to computer experiment-based optimization, and we consider data mining methods, using principal components analysis and multiple testing based on false discovery rate, that are appropriate for our variable selection task. An airline fleet assignment case study is used to illustrate our approach.     

Design and veri?cation of airfoil families for large-size wind turbine blades?

For the design of wind turbine blades, the use of a family of specially tailored airfoils is particularly important. The dedicated airfoils can dramatically improve the capability of capturing wind power, reduce the structural weight to save the cost of manufacturing and transportation, and lower the inertial loads as well as the loads due to gust. An overview of the world-wide wind turbine airfoil families developed since 1990’s is presented, such as the S series, the DU series, the Ris? series, and the FFA series. The design and wind-tunnel tests of the Northwestern Polytechnical University (NPU) airfoil family for megawatt-size wind turbines, called the NPU-WA series, are summarized. All tests for the NPU-WA series are carried out in the NF-3 low-speed wind-tunnel with a two-dimensional (2D) test section of 1.6 m?0.8 m and at the Reynolds number ranging from 1.6?106 to 5?106 . The research activities for further improving the NPU-WA airfoils towards lower roughness sensitivity are also reviewed. The development of the new NPU-WA series dedicated for multi-megawatt wind turbines is discussed.     

Elastic-plastic sandwich beams with optimized properties for the impact case

An approach for the design of lightweight sandwich beams with optimal performance under low velocity impact loading is presented. Exemplary for beams with stainless steel faces and aluminium foam cores a procedure is proposed that bases on simulation results using an explicit finite element code. Experimental tests assure the accuracy of numerical analyses and provide data to fit the applied plastic compressible material model. By means of a multicriteria optimization method failure mechanisms are identified which dissipate a maximum of energy and lead to minimal deflections as well as to minimal beam thickness and weight at the same time. Face and core thicknesses are used as design variables. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Optimal design of a 2 DOF parallel robot

This paper is aimed at presenting a study on the optimization of the Biglide mini parallel robot, which comprises two-degree-of-freedom (DOF) mini parallel robots with constant struts. The robot workspace is characterized and the inverse kinematics equation is obtained. In the paper, design optimization is implemented with Genetic Algorithms (GA) for optimization considering transmission quality index and workspace. Here, intended to show the advantages of using the GA, we applied it to a multicriteria optimization problem of 2 DOF mini parallel robot. Genetic algorithms (GA) are so far generally the best and most robust kind of evolutionary algorithms. A GA has a number of advantages. It can quickly scan a vast solution set. Bad proposals do not affect the end solution negatively as they are simply discarded. The obtained results have shown that the use of GA in such kind of optimization problem enhances the quality of the optimization outcome, providing a better and more realistic support for the decision maker. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Performance analysis of distributed solution approaches in simulation-based optimization

Applying computationally expensive simulations in design or process optimization results in long-running solution processes even when using a state-of-the-art distributed algorithm and hardware. Within these simulation-based optimization problems the optimizer has to treat the simulation systems as black-boxes. The distributed solution of this kind of optimization problem demands efficient utilization of resources (i.e. processors) and evaluation of the solution quality. Analyzing the parallel performance is therefore an important task in the development of adequate distributed approaches taking into account the numerical algorithm, its implementation, and the used hardware architecture. In this paper, simulation-based optimization problems are characterized and a distributed solution algorithm is presented. Different performance analysis techniques (e.g. scalability analysis, computational complexity) are discussed and a new approach integrating parallel performance and solution quality is developed. This approach combines a priori and a posteriori techniques and can be applied in early stages of the solution process. The feasibility of the approach is demonstrated by applying it to three different classes of simulation-based optimization problems from groundwater management.