SOMS: SurrOgate MultiStart algorithm for use with nonlinear programming for global optimization

SOMS is a general surrogate-based multistart algorithm, which is used in combination with any local optimizer to ���nd global optima for computationally expensive functions with multiple local minima. SOMS differs from previous multistart methods in that a surrogate approximation is used by the multistart algorithm to help reduce the number of function evaluations necessary to identify the most promising points from which to start each nonlinear programming local search. SOMS���s numerical results are compared with four well-known methods, namely, Multi-Level Single Linkage (MLSL), MATLAB���s MultiStart, MATLAB���s GlobalSearch, and GLOBAL. In addition, we propose a class of wavy test functions that mimic the wavy nature of objective functions arising in many black-box simulations. Extensive comparisons of algorithms on the wavy testfunctions and on earlier standard global-optimization test functions are done for a total of 19 different test problems. The numerical results indicate that SOMS performs favorably in comparison to alternative methods and does especially well on wavy functions when the number of function evaluations allowed is limited.

Self-organizing Routing Algorithm fo Wireless Sensors Networks (WSN) using Ant Colony Optimization (ACO) with Tinyos.

This paper describes the basic tools to work with wireless sensors. TinyOShas a componentbased architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools ? all of which can be used asia or be further refined for a custom application. TinyOS was originally developed as a research project at the University of California Berkeley, but has since grown to have an international community of developers and users. Some algorithms concerning packet routing are shown. Incar entertainment systems can be based on wireless sensors in order to obtain information from Internet, but routing protocols must be implemented in order to avoid bottleneck problems. Ant Colony algorithms are really useful in such cases, therefore they can be embedded into the sensors to perform such routing task.

Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via B��zier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

Distributed bees algorithm for task allocation in swarm of robots

In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of robots. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets' qualities that are represented as scalar values. Decision-making mechanism is distributed and robots autonomously choose their assignments taking into account targets' qualities and distances. We tested the scalability of the proposed DBA algorithm in terms of number of robots and number of targets. For that, the experiments were performed in the simulator for various sets of parameters, including number of robots, number of targets, and targets' utilities. Control parameters inherent to DBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased.

Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels

An aerodynamic optimization of the ICE 2 high-speed train nose in term of front wind action sensitivity is carried out in this paper. The nose is parametrically defined by Be?zier Curves, and a three-dimensional representation of the nose is obtained using thirty one design variables. This implies a more complete parametrization, allowing the representation of a real model. In order to perform this study a genetic algorithm (GA) is used. Using a GA involves a large number of evaluations before finding such optimal. Hence it is proposed the use of metamodels or surrogate models to replace Navier-Stokes solver and speed up the optimization process. Adaptive sampling is considered to optimize surrogate model fitting and minimize computational cost when dealing with a very large number of design parameters. The paper introduces the feasi- bility of using GA in combination with metamodels for real high-speed train geometry optimization.

A 3D multi-objective optimization planning algorithm for wireless sensor networks

The complexity of planning a wireless sensor network is dependent on the aspects of optimization and on the application requirements. Even though Murphy's Law is applied everywhere in reality, a good planning algorithm will assist the designers to be aware of the short plates of their design and to improve them before the problems being exposed at the real deployment. A 3D multi-objective planning algorithm is proposed in this paper to provide solutions on the locations of nodes and their properties. It employs a developed ray-tracing scheme for sensing signal and radio propagation modelling. Therefore it is sensitive to the obstacles and makes the models of sensing coverage and link quality more practical compared with other heuristics that use ideal unit-disk models. The proposed algorithm aims at reaching an overall optimization on hardware cost, coverage, link quality and lifetime. Thus each of those metrics are modelled and normalized to compose a desirability function. Evolutionary algorithm is designed to efficiently tackle this NP-hard multi-objective optimization problem. The proposed algorithm is applicable for both indoor and outdoor 3D scenarios. Different parameters that affect the performance are analyzed through extensive experiments; two state-of-the-art algorithms are rebuilt and tested with the same configuration as that of the proposed algorithm. The results indicate that the proposed algorithm converges efficiently within 600 iterations and performs better than the compared heuristics.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Efficient algorithm for routing optimization via statistical mechanics

Many practical routing algorithms are heuristic, adhoc and centralized, rendering generic and optimal path configurations difficult to obtain. Here we study a scenario whereby selected nodes in a given network communicate with fixed routers and employ statistical physics methods to obtain optimal routing solutions subject to a generic cost. A distributive message-passing algorithm capable of optimizing the path configuration in real instances is devised, based on the analytical derivation, and is greatly simplified by expanding the cost function around the optimized flow. Good algorithmic convergence is observed in most of the parameter regimes. By applying the algorithm, we study and compare the pros and cons of balanced traffic configurations to that of consolidated traffic, which provides important implications to practical communication and transportation networks. Interesting macroscopic phenomena are observed from the optimized states as an interplay between the communication density and the cost functions used. �� 2013 IEEE.

Optimization of pavement maintenance and rehabilitation programming using shuffled complex evolution algorithm

The major barrier to practical optimization of pavement preservation programming has always been that for formulations where the identity of individual projects is preserved, the solution space grows exponentially with the problem size to an extent where it can become unmanageable by the traditional analytical optimization techniques within reasonable limit. This has been attributed to the problem of combinatorial explosion that is, exponential growth of the number of combinations. The relatively large number of constraints often presents in a real-life pavement preservation programming problems and the trade-off considerations required between preventive maintenance, rehabilitation and reconstruction, present yet another factor that contributes to the solution complexity. In this research study, a new integrated multi-year optimization procedure was developed to solve network level pavement preservation programming problems, through cost-effectiveness based evolutionary programming analysis, using the Shuffled Complex Evolution (SCE) algorithm.^ A case study problem was analyzed to illustrate the robustness and consistency of the SCE technique in solving network level pavement preservation problems. The output from this program is a list of maintenance and rehabilitation treatment (M&R) strategies for each identified segment of the network in each programming year, and the impact on the overall performance of the network, in terms of the performance levels of the recommended optimal M&R strategy. ^ The results show that the SCE is very efficient and consistent in the simultaneous consideration of the trade-off between various pavement preservation strategies, while preserving the identity of the individual network segments. The flexibility of the technique is also demonstrated, in the sense that, by suitably coding the problem parameters, it can be used to solve several forms of pavement management programming problems. It is recommended that for large networks, some sort of decomposition technique should be applied to aggregate sections, which exhibit similar performance characteristics into links, such that whatever M&R alternative is recommended for a link can be applied to all the sections connected to it. In this way the problem size, and hence the solution time, can be greatly reduced to a more manageable solution space. ^ The study concludes that the robust search characteristics of SCE are well suited for solving the combinatorial problems in long-term network level pavement M&R programming and provides a rich area for future research. ^

Stable algorithm for event detection in event-driven particle dynamics : logical states

Acknowledgments The authors gratefully acknowledge the support of the German Research Foundation (DFG) through the Cluster of Excellence ���Engineering of Advanced Materials��� at the University of Erlangen-Nuremberg and through Grant Po 472/25.

Scheduling Optimization with LDA and Greedy Algorithm

<p>Scheduling optimization is concerned with the optimal allocation of events to time slots. In this paper, we look at one particular example of scheduling problems - the 2015 Joint Statistical Meetings. We want to assign each session among similar topics to time slots to reduce scheduling conflicts. Chapter 1 briefly talks about the motivation for this example as well as the constraints and the optimality criterion. Chapter 2 proposes use of Latent Dirichlet Allocation (LDA) to identify the topic proportions in each session and talks about the fitting of the model. Chapter 3 translates these ideas into a mathematical formulation and introduces a Greedy Algorithm to minimize conflicts. Chapter 4 demonstrates the improvement of the scheduling with this method.</p>

Development of a heterogeneous clustering algorithm for particle shower reconstruction in high energy physics using the SYCL abstraction layer

Nei prossimi anni �� atteso un aggiornamento sostanziale di LHC, che prevede di aumentare la luminosit�� integrata di un fattore 10 rispetto a quella attuale. Tale parametro �� proporzionale al numero di collisioni per unit�� di tempo. Per questo, le risorse computazionali necessarie a tutti i livelli della ricostruzione cresceranno notevolmente. Dunque, la collaborazione CMS ha cominciato gi�� da alcuni anni ad esplorare le possibilit�� offerte dal calcolo eterogeneo, ovvero la pratica di distribuire la computazione tra CPU e altri acceleratori dedicati, come ad esempio schede grafiche (GPU). Una delle difficolt�� di questo approccio �� la necessit�� di scrivere, validare e mantenere codice diverso per ogni dispositivo su cui dovr�� essere eseguito. Questa tesi presenta la possibilit�� di usare SYCL per tradurre codice per la ricostruzione di eventi in modo che sia eseguibile ed efficiente su diversi dispositivi senza modifiche sostanziali. SYCL �� un livello di astrazione per il calcolo eterogeneo, che rispetta lo standard ISO C++. Questo studio si concentra sul porting di un algoritmo di clustering dei depositi di energia calorimetrici, CLUE, usando oneAPI, l'implementazione SYCL supportata da Intel. Inizialmente, �� stato tradotto l'algoritmo nella sua versione standalone, principalmente per prendere familiarit�� con SYCL e per la comodit�� di confronto delle performance con le versioni gi�� esistenti. In questo caso, le prestazioni sono molto simili a quelle di codice CUDA nativo, a parit�� di hardware. Per validare la fisica, l'algoritmo �� stato integrato all'interno di una versione ridotta del framework usato da CMS per la ricostruzione. I risultati fisici sono identici alle altre implementazioni mentre, dal punto di vista delle prestazioni computazionali, in alcuni casi, SYCL produce codice pi�� veloce di altri livelli di astrazione adottati da CMS, presentandosi dunque come una possibilit�� interessante per il futuro del calcolo eterogeneo nella fisica delle alte energie.