The present paper presents an alternative methodology to determine frequency-dependent network equivalents from the frequency response of electric power networks. According to this methodology, one can substitute part...
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The present paper presents an alternative methodology to determine frequency-dependent network equivalents from the frequency response of electric power networks. According to this methodology, one can substitute part of the power network through a simplified electric circuit, reducing the number of elements to be modeled without introducing any inaccuracy to the precise evaluation of large power networks. The main challenge relays on determining a rational function capable of producing the same frequency response characteristic of the original circuit that would be replaced by the equivalent. The rational function determines the impedance to be used in the network equivalent. The methodology is based on evolutionary algorithms (EAs). Basically, different set of values for the rational function parameters are evaluated. During the convergence process, the EAs modify the values for the function's parameters suitably, reducing the RMS error between the original frequency response and the one produced by the equivalent circuit. The results obtained through this methodology were compared with those obtained through the "Vector Fitting" methodology, which is the methodology commonly used to solve similar problems.
This paper presents a comparative analysis of three evolutionary algorithms, namely, Backtracking Search Algorithm, Cuckoo Search Algorithm and Artificial Bee Colony algorithms for synthesis of a scanned linear array ...
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This paper presents a comparative analysis of three evolutionary algorithms, namely, Backtracking Search Algorithm, Cuckoo Search Algorithm and Artificial Bee Colony algorithms for synthesis of a scanned linear array of uniformly spaced parallel half wavelength dipole antennas. Here, antenna parameters, namely Side Lobe Level, reflection coefficient and wide null depth are taken into consideration for comparison between algorithms. In addition to it, statistical parameters, namely best fitness value, mean and standard deviation of the fitness values obtained from algorithms are compared. Mutual coupling that exists among the antenna elements is included in obtaining radiation patterns and the self-impedances along with the mutual impedances are calculated by induced Electro-Motive Force method. Two different examples are shown in this paper to validate the effectiveness of the utilized approach. Although, this approach is applied to a linear array of dipole antennas;this can be utilized for other array geometries as well.
The study of production systems taxonomy not only provides a good description of organization dominant groups but also provides the ground for more specialized studies such as a study of performance, the proper form o...
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The study of production systems taxonomy not only provides a good description of organization dominant groups but also provides the ground for more specialized studies such as a study of performance, the proper form of production decisions in each group, and the theorizing in it. In some taxonomic studies, due to high speed and ease of implementation, K-means cluster analysis was used to analyze data but the convergence took place in local optimum. For this reason, Hybrid Clustering algorithms were used for the clustering of manufacturing companies. The clustering results of these methods were compared using validation indicators. According to the results of the comparisons, the best clustering algorithm was chosen, based on which cluster naming was done. Then, using the results of Discriminant Analysis, the distinctive dimensions of clusters were identified and the results showed that the manufacturing systems in Iran can be introduced in two dimensions of green production planning and resource capacity.
The theory of evolutionary computation for discrete search spaces has made significant progress since the early 2010s. This survey summarizes some of the most important recent results in this research area. It discuss...
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Mobile applications require dynamic reconfiguration services (DRS) to self-adapt their behavior to the context changes (e.g., scarcity of resources). Dynamic Software Product Lines (DSPL) are a well-accepted approach ...
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Mobile applications require dynamic reconfiguration services (DRS) to self-adapt their behavior to the context changes (e.g., scarcity of resources). Dynamic Software Product Lines (DSPL) are a well-accepted approach to manage runtime variability, by means of late binding the variation points at runtime. During the system's execution, the DRS deploys different configurations to satisfy the changing requirements according to a multiobjective criterion (e.g., insufficient battery level, requested quality of service). Search-based software engineering and, in particular, multiobjective evolutionary algorithms (MOEAs), can generate valid configurations of a DSPL at runtime. Several approaches use MOEAs to generate optimum configurations of a Software Product Line, but none of them consider DSPLs for mobile devices. In this paper, we explore the use of MOEAs to generate at runtime optimum configurations of the DSPL according to different criteria. The optimization problem is formalized in terms of a Feature Model (FM), a variability model. We evaluate six existing MOEAs by applying them to 12 different FMs, optimizing three different objectives (usability, battery consumption and memory footprint). The results are discussed according to the particular requirements of a DRS for mobile applications, showing that PAES and NSGA-II are the most suitable algorithms for mobile environments. (C) 2015 Elsevier Inc. All rights reserved.
The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-po...
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The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-point format can improve performance and reduce energy consumption, the process of tuning the right bit length is known as a time-consuming task, since it is a combinatorial optimisation problem guided by the accumulative arithmetic computation error. A novel evolutionary approach to accelerate the process of converting algorithms from the floating-point to fixed-point format is presented. Results are demonstrated by converting three computing-intensive algorithms from the mobile robotic scenario, where data error accumulated during execution is influenced by external factors, such as sensor noise and navigation environment characteristics. The proposed evolutionary algorithm accelerated the conversion process by up to 2.5 x against the state-of-the-art methods, allowing even further bit-length optimisations.
This paper examines the incorporation of useful information extracted from the evolutionary process, in order to improve algorithm performance. In order to achieve this objective, we introduce an efficient method of e...
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This paper examines the incorporation of useful information extracted from the evolutionary process, in order to improve algorithm performance. In order to achieve this objective, we introduce an efficient method of extracting and utilizing valuable information from the evolutionary process. Finally, this information is utilized for optimizing the search process. The proposed algorithm is compared with the NSGAII for solving some real-world instances of the fuzzy portfolio optimization problem. The proposed algorithm outperforms the NSGAII for all examined test instances.
The docking of ligands to proteins can be formulated as a computational problem where the task is to find the most favorable energetic conformation among the large space of possible protein-ligand complexes. Stochasti...
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The docking of ligands to proteins can be formulated as a computational problem where the task is to find the most favorable energetic conformation among the large space of possible protein-ligand complexes. Stochastic search methods such as evolutionary algorithms (EAs) can be used to sample large search spaces effectively and is one of the commonly used methods for flexible ligand docking. During the last decade, several EAs using different variation operators have been introduced, such as the ones provided with the AutoDock program. In this paper we evaluate the performance of different EA settings such as choice of variation operators, population size, and usage of local search. The comparison is performed on a suite of six docking problems previously used to evaluate the performance of search algorithms provided with the AutoDock program package. The results from our investigation confirm that the choice of variation operators has an impact on the search-capabilities of EAs. The introduced DockEA using the best settings found obtained the overall best docking solutions compared to the Lamarckian GA (LGA) provided with AutoDock. Furthermore, the DockEA proved to be more robust than the LGA (in terms of reproducing the results in several runs) on the more difficult problems with a high number of flexible torsion angles. (C) 2003 Elsevier Ireland Ltd. All rights reserved.
Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current *** proposed application is for engineering design ***/methodology/approach–T...
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Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current *** proposed application is for engineering design ***/methodology/approach–This study proposes two novel approaches which focus on faster convergence to the Pareto front(PF)while adopting the advantages of Strength Pareto evolutionary Algorithm-2(SPEA2)for better *** first method,decision variables corresponding to the optima of individual objective functions(Utopia Point)are strategically used to guide the search toward *** second method,boundary points of the PF are calculated and their decision variables are seeded to the initial ***–The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance *** evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms(such as NSGA-II and SPEA2)and recent ones such as NSLS and E-NSGA-II in most of the benchmark *** is also tested on an engineering design problem and compared with a currently used *** implications–The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives.A complex example of Welded Beam has been shown at the end of the *** implications–The algorithm would be useful for many design problems and social/industrial problems with conflicting ***/value–This paper presents two novel hybrid algorithms involving SPEA2 based on:local search;and Utopia point directed search *** concept has not been investigated before.
This paper introduces LEAC, a new C++ partitioning clustering library based on evolutionary computation. LEAC provides plenty of elements (individual encoding schemes, genetic operators, evaluation metrics, among othe...
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This paper introduces LEAC, a new C++ partitioning clustering library based on evolutionary computation. LEAC provides plenty of elements (individual encoding schemes, genetic operators, evaluation metrics, among others) which allow an easy and fast development of new clustering algorithms. Furthermore, it includes 23 algorithms which represent the state-of-the-art in evolutionary algorithms for partial clustering. The paper describes through examples the main features and the design principles of the software, as well as how to use LEAC to carry out a comparison between different proposals and how to extend it by including new algorithms. (C) 2019 Elsevier B.V. All rights reserved.
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