In this study, an accurate and efficient quantum genetic algorithm (QGA) combined with an improved self-adaptive (SA) scheme is proposed to solve electromagnetic optimisation problems. QGA is employed as the main opti...
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In this study, an accurate and efficient quantum genetic algorithm (QGA) combined with an improved self-adaptive (SA) scheme is proposed to solve electromagnetic optimisation problems. QGA is employed as the main optimisation frame because of its wider search range and higher efficiency than the conventional genetic algorithm. By introducing an improved SA scheme, the population at each generation is divided into two groups for crossover operation according to the magnitudes of individual fitness values. The crossover probability and mutation rate remain unchanged at the early stage of iterative process while the SA scheme will be carried out for the rest of the iterative process. Moreover, the elitist model is introduced to save the optimal father-individuals and abandon the worst ones. All these strategies make the whole population nearly converge to the optimal solution very fast. In two numerical examples of filter design and linear array synthesis, the effectiveness of the author's proposed optimisation algorithm, combined with the finite-difference time-domain method and finite-element method in HFSS, respectively, is verified.
This paper introduces a new classification scheme for the solar radiation estimation techniques based on three categories: empirical models (based on statistical regression techniques), simulated models (based on trai...
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This paper introduces a new classification scheme for the solar radiation estimation techniques based on three categories: empirical models (based on statistical regression techniques), simulated models (based on training), and optimized models (based on optimization algorithms). For the optimized model category, a novelty bees algorithm estimation based on a linear empirical model is developed. Eight different methods from three classes have been tested on three sample geographic positions of Iran in order to compare the efficiency, complexity, sensed parameters, and required prior training of each category with others by implementing in the Matlab software. Among all tested models, the best properties are obtained for optimized empirical models by optimization algorithms. The main advantages of this model type are that it eliminates the training stage and therefore reduces the complexity rather than simulated models yet offers high accuracy estimation. Copyright (c) 2014 John Wiley & Sons, Ltd.
In this study, we propose a new, secure method of sharing useful chemical information from small-molecule libraries, without revealing the structures of the libraries' molecules. Our method shares the relationship...
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In this study, we propose a new, secure method of sharing useful chemical information from small-molecule libraries, without revealing the structures of the libraries' molecules. Our method shares the relationship between molecules rather than structural descriptors. This is an important advance because, over the past few years, several groups have developed and published new methods of analyzing small-molecule screening data. These methods include advanced hit-picking protocols, promiscuous active filters, economic optimization algorithms, and screening visualizations, which can identify patterns in the data that might otherwise be overlooked. Application of these methods to private data requires finding strategies for sharing useful chemical data without revealing chemical structures. This problem has been examined in the context of ADME prediction models, with results from information theory suggesting it is impossible to share useful chemical information without revealing structures. In contrast, we present a new strategy for encoding the relationships between molecules instead of their structures, based on anonymized scaffold networks and trees, that safely shares enough chemical information to be useful in analyzing chemical data, while also sufficiently blinding structures from discovery. We present the details of this encoding, an analysis of the usefulness of the information it conveys, and the security of the structures it encodes. This approach makes it possible to share data across institutions, and may securely enable collaborative analysis that can yield insight into both specific projects and screening technology as a whole.
Dynamic voltage restorer (DVR) is one of the custom power devices for compensating power quality indices which is used. The main function of DVR that is discussed in many studies is to compensate voltage sag at times ...
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Dynamic voltage restorer (DVR) is one of the custom power devices for compensating power quality indices which is used. The main function of DVR that is discussed in many studies is to compensate voltage sag at times when faults occur. For the first time, a new control structure is presented for considering the voltage sag as the main objective and voltage total harmonic distortion (THD) as the second objective of DVR controller. In this strategy, a new and powerful optimisation algorithm (known as chaotic accelerated particle swarm optimisation (CAPSO)) which is an improved version of particle swarm optimisation algorithm is used for determining the coefficients of the proportional-integral controller of DVR. These coefficients are determined in a way that voltage sag is considered as the main objective of optimisation algorithm and voltage THD is considered as its second objective. By fuzzifying the objectives, an appropriate objective function is proposed for the optimisation process. Results obtained from simulation and a comparison made between these results and those of other controllers show that the proposed strategy outperforms other strategies.
DBSCAN is one of the most common density-based clustering algorithms. While multiple works tried to present an appropriate estimate for needed parameters we propose an alternating optimization algorithm, which finds a...
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ISBN:
(纸本)9781479975617
DBSCAN is one of the most common density-based clustering algorithms. While multiple works tried to present an appropriate estimate for needed parameters we propose an alternating optimization algorithm, which finds a locally optimal parameter combination. The algorithm is based on the combination of two hierarchical versions of DBSCAN, which can be generated by fixing one parameter and iterating through possible values of the second parameter. Due to monotonicity of the neighborhood sets and the core-condition, successive levels of the hierarchy can efficiently be computed. An local optimal parameter combination can be determined using internal cluster validation measures. In this work we are comparing the measures edge-correlation and silhouette coefficient. For the latter we propose a density-based interpretation and show a respective computational efficient estimate to detect non-convex clusters produced by DBSCAN. Our results show, that the algorithm can automatically detect a good DBSCAN clustering on a variety of cluster scenarios.
This paper focus on studying techniques that allow optimization of a communication system by analyzing the patterns of antenna configurations and digital modulations. Antenna design methods are discussed, taking into ...
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ISBN:
(纸本)9781467394932
This paper focus on studying techniques that allow optimization of a communication system by analyzing the patterns of antenna configurations and digital modulations. Antenna design methods are discussed, taking into account parameters such as the number of elements in arrays and modulation types. Computer simulations are made with the use of LTE system simulation and antenna packages freely available. The main goal is to investigate if the modulation scheme influences the antenna design.
optimization of discrete event systems conventionally uses simulation as a black-box oracle to estimate performance at design points generated by a separate optimization algorithm. This decoupled approach fails to exp...
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ISBN:
(纸本)9781467397414
optimization of discrete event systems conventionally uses simulation as a black-box oracle to estimate performance at design points generated by a separate optimization algorithm. This decoupled approach fails to exploit an important advantage: simulation codes are white-boxes, at least to their creators. In fact, the full integration of the simulation model and the optimization algorithm is possible in many situations. In this contribution, a framework previously proposed by the authors, based on the mathematical programming methodology, is presented under a wider perspective. We show how to derive mathematical models for solving optimization problems while simultaneously considering the dynamics of the system to be optimized. Concerning the solution methodology, we refer back to retrospective optimization (RO) and sample path optimization (SPO) settings. Advantages and drawbacks deriving from the use of mathematical programming as work models within the RO (SPO) framework will be analyzed and its convergence properties will be discussed.
Microwave tomography (MWT) is exploited for the detection of haemorrhagic stroke by using a nonlinear iterative imaging algorithm. An anatomically realistic two-dimensional (2D) head model is simulated using a finite ...
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Microwave tomography (MWT) is exploited for the detection of haemorrhagic stroke by using a nonlinear iterative imaging algorithm. An anatomically realistic two-dimensional (2D) head model is simulated using a finite difference time-domain numerical ...
In welding processes, welding parameters have a significant impact on weld quality and mechanical properties of welded joints. For example, if the welding current is not tuned properly, the welding arc becomes unstabl...
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ISBN:
(纸本)9781467381840
In welding processes, welding parameters have a significant impact on weld quality and mechanical properties of welded joints. For example, if the welding current is not tuned properly, the welding arc becomes unstable which will cause an unacceptable weld. Therefore welding parameters must be optimized in order to achieve best weld quality. However current methods have many limitations in exploring optimal welding parameters. In this paper, Gaussian Process Regression is applied to model the relationship between the welding performance indices and welding parameters. Bayesian optimization Algorithm is adopted to balance the modeling and optimization processes and optimize welding parameters. Experiments were performed for the Gas tungsten arc welding (GTAW) process and the results demonstrate the effectiveness of the proposed algorithm. Compared to the existing methods, the proposed method greatly improves the welding parameter optimization process;moreover it can be applied with fewer experiments compared with existing methods which will reduce the testing cost and effort.
Purpose - In this paper, a new method to improve the performance of particle swarm optimization is proposed. Design/methodology/approach - This paper introduces hypothesis testing to determine whether the particles tr...
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Purpose - In this paper, a new method to improve the performance of particle swarm optimization is proposed. Design/methodology/approach - This paper introduces hypothesis testing to determine whether the particles trap into the local minimum or not, then special re-initialization was proposed, finally, some famous benchmarks and constrained engineering optimization problems were used to test the efficiency of the proposed method. In the revised manuscript, the content was revised and more information was added. Findings - The proposed method can be easily applied to PSO or its varieties. Simulation results show that the proposed method effectively enhances the searching quality. Originality/value - This paper proposes an adaptive particle swarm optimization method ( APSO). A technique is applied to improve the global optimization performance based on the hypothesis testing. The proposed method uses hypothesis testing to determine whether the particles are trapped into local minimum or not. This research shows that the proposed method can effectively enhance the searching quality and stability of PSO.
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