An optimization algorithm for multimodal functions inspired by collective animal behavior

Interest in multimodal function optimization is expanding rapidly as real-world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the collective animal behavior. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact with each other based on the biologic laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison with other methods reported in the literature.     

Topology and thickness optimization of laminated composites including manufacturing constraints

This paper presents a gradient based optimization method for large-scale topology and thickness optimization of fiber reinforced monolithic laminated composite structures including certain manufacturing constraints to attain industrial relevance. This facilitates application of predefined fiber mats and reduces the risk of failure such as delamination and matrix cracking problems. The method concerns simultaneous determination of the optimum thickness and fiber orientation throughout a laminated structure with fixed outer geometry. The laminate thickness may vary as an integer number of plies, and possible fiber orientations are limited to a finite set. The conceptual combinatorial problem is relaxed to a continuous problem and solved on basis of interpolation schemes with penalization through the so-called Discrete Material Optimization method, explicitly including manufacturing constraints as a large number of sparse linear constraints. The methodology is demonstrated on several numerical examples.     

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.     

Optimally Adaptive Integration of Univariate Lipschitz Functions

We consider the problem of approximately integrating a Lipschitz function f (with a known Lipschitz constant) over an interval. The goal is to achieve an additive error of at most ε using as few samples of f as possible. We use the adaptive framework: on all problem instances an adaptive algorithm should perform almost as well as the best possible algorithm tuned for the particular problem instance. We distinguish between and , the performances of the best possible deterministic and randomized algorithms, respectively. We give a deterministic algorithm that uses samples and show that an asymptotically better algorithm is impossible. However, any deterministic algorithm requires samples on some problem instance. By combining a deterministic adaptive algorithm and Monte Carlo sampling with variance reduction, we give an algorithm that uses at most samples. We also show that any algorithm requires samples in expectation on some problem instance (f,ε), which proves that our algorithm is optimal.     

Subquadratic Algorithms for 3SUM

We obtain subquadratic algorithms for 3SUM on integers and rationals in several models. On a standard word RAM with w-bit words, we obtain a running time of . In the circuit RAM with one nonstandard AC ⁰ operation, we obtain . In external memory, we achieve O(n ²/(MB)), even under the standard assumption of data indivisibility. Cache-obliviously, we obtain a running time of . In all cases, our speedup is almost quadratic in the “parallelism” the model can afford, which may be the best possible. Our algorithms are Las Vegas randomized; time bounds hold in expectation, and in most cases, with high probability.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.     

分块云计算

Jimmy Nilsson在过去数年间观察到一种架构风格正慢慢流行,通过对过去数年的总结,对过往的痛苦自省,他将这种新的架构风格做了总结。     

Neck postures in air traffic controllers with and without neck/shoulder disorders

Prolonged computer work with an extended neck is commonly believed to be associated with an increased risk of neck-shoulder disorders. The aim of this study was to compare neck postures during computer work between female cases with neck-shoulder disorders, and healthy referents. Based on physical examinations, 13 cases and 11 referents were selected among 70 female air traffic controllers with the same computer-based work tasks and identical workstations. Postures and movements were measured by inclinometers, placed on the forehead and upper back (C7/Th1) during authentic air traffic control. A recently developed method was applied to assess flexion/extension in the neck, calculated as the difference between head and upper back flexion/extension. Results: cases and referents did not differ significantly in neck posture (median neck flexion/extension: -10 degrees vs. -9 degrees ; p=0.9). Hence, the belief that neck extension posture is associated with neck-shoulder disorders in computer work is not supported by the present data.     

A mathematical model for estimation of sulphide capacities of multi-component slags

The present work presents mathematical model for the estimation of sulphide capacities. The sulphide capacity, C_S is expressed as (1) where ΔG⁰ is the standard Gibbs energy change for the reaction, (2) ξ, being a function of both temperature and composition, is characterized by the system under consideration. By optimizing ξ, the sulphide capacities could be estimated as functions of temperature and composition. The slag composition is described using a modified Temkin approach, where complex polymeric ions are considered as dissociated to simple species. The model has been applied to the binary systems CaO-SiO₂, MnO-SiO₂ as well as the CaO-MnO-SiO₂ ternary system. The results show that reliable predictions of the sulphide capacities can be obtained using the present model.     

Estimating shear properties of walnut wood: a combined experimental and theoretical approach

In this study, several theoretical models to numerically estimate shear properties of orthotropic materials are introduced. These approaches are based on the combination of Hankinson’s empirically derived formula with other empirical and analytical calculations. Next to shear moduli, which are estimated from the elastic moduli and Poisson’s ratios, shear strengths are also estimated from the in-axis strengths. The models are validated by mechanical tests on walnut wood (Juglans regia L.), for which a sufficient data set can be found in literature. The Arcan test is used to estimate the shear moduli, while the shear block test is used to estimate the shear strengths. The results show that the model, which is based on a combined use of Hankinson’s formula and tensor rotation, gives the best estimation of shear moduli as evaluated by the minimum differences to the experimentally obtained results. For the shear strengths, a combination of Hankinson’s formula and Norris’ failure criterion shows the best agreement in comparison to the experimental data. The theoretical calculations may be used for a time efficient estimation of shear modulus and strength in comparison to the very time-consuming experimental estimation.