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检索条件"主题词=nondominated sorting genetic algorithm"

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Biobjective NSGA-II for Optimal Spread Spectrum Watermarking of Color Frames: Evaluation Study

Biobjective NSGA-II for Optimal Spread Spectrum Watermarking...

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IEEE Symposium on Computational Intelligence in Cyber Security

作者： El-Alfy, El-Sayed M. Ghaleb, Asem A. King Fahd Univ Petr & Minerals Coll Comp Sci & Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781479945214

In this work, a spread spectrum watermarking optimization algorithm is explored for digital color images using biobjective genetic algorithms and full-frame discrete-cosine transform. The aim of optimization is to generate the trade-off curve, a.k.a. optimal Pareto points, of watermark imperceptibility and robustness. The watermark imperceptibility is evaluated using the Structural SIMilarity (SSIM) index between the original image and the watermarked image whereas the watermark robustness is evaluated in terms of the Normalized Correlation Coefficient (NCC) between the original watermark and the recovered watermark. The watermarked image is susceptible to various types of attacks or processing distortions such as additive Gaussian noise, pepper-and-salt noise, JPEG compression, camera motion and median filtering. For the biobjective genetic algorithm, we used the fast elitist Non-dominated sorting genetic algorithm (NSGA-II). We reviewed related work and investigated two color spaces (YCbCr and HSV) in addition to gray scale images where embedding is conducted in different frames and various distortions are applied before the extraction of the watermark. The results are compared for various cases under similar conditions.

关键词： discrete cosine transforms genetic algorithms image colour analysis image watermarking biobjective NSGA-II biobjective genetic algorithms color frames digital color images full-frame discrete-cosine transform nondominated sorting genetic algorithm normalized correlation coefficient optimal Pareto points optimal spread spectrum watermarking spread spectrum watermarking optimization algorithm structural similarity index trade-off curve watermark imperceptibility Discrete cosine transforms Discrete wavelet transforms genetic algorithms Image color analysis Optimization Robustness Watermarking discrete cosine transforms image colour analysis Watermarking genetic algorithm (GA) discrete wavelet transform color image digital image watermarking Evaluation Studies Species diversity SPREAD SPECTRUM

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Multiobjective Virtual Machine Placement in Cloud Environment

Multiobjective Virtual Machine Placement in Cloud Environmen...

引用

International Conference on Cloud and Ubiquitous Computing and Emerging Technologies (CUBE)

作者： Adamuthe, Amol C. Pandharpatte, Rupali M. Thampi, Gopakumaran T. RIT Dept CSE Islampur MS India TSEC Dept IT Bombay Maharashtra India

ISBN: (纸本)9781479922345;9781479922352

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of genetic algorithms, Non-dominated sorting genetic algorithm and Non-dominated sorting genetic algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.

关键词： nondominated sorting genetic algorithm Resource allocation Multiobjective Optimization Problem

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A Multi-Objective Method to Balance Energy Consumption and Performance for Energy-Based Target Localization in Wireless Sensor Networks

A Multi-Objective Method to Balance Energy Consumption and P...

引用

IEEE SoutheastCon

作者： Luo, Zhenxing Jannett, Thomas C. Univ Alabama Birmingham Dept Elect & Comp Engn Birmingham AL 35294 USA

ISBN: (纸本)9781467313759

In this paper, we utilized a multi-objective approach to balance energy consumption and performance in wireless sensor networks (WSNs) that use a maximum likelihood estimation (MLE) approach for energy-based target localization. First, we developed measures that allow energy consumption and performance to be balanced in one-dimensional sensor arrays. Next, we extended these methods for two-dimensional arrays, employing approximations that facilitate computation of the energy consumption. Simulations were run to generate the Pareto-optimal fronts for both one-dimensional and two-dimensional sensor arrays. The Pareto-optimal fronts are useful in determining optimum points in practice.

关键词： Wireless sensor networks target localization multi-objective optimization energy consumption nondominated sorting genetic algorithm

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SIMULATION-BASED MULTIOBJECTIVE OPTIMIZATION OF BRIDGE CONSTRUCTION PROCESSES USING PARALLEL COMPUTING

SIMULATION-BASED MULTIOBJECTIVE OPTIMIZATION OF BRIDGE CONST...

引用

Winter Simulation Conference

作者： Salimi, Shide Mawlana, Mohammed Hammad, Amin Concordia Univ 1455 Maisonneuve Blvd West Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781479974863

Conventionally, efforts are made to optimize the performance of simulation models by examining several possible resource combinations. However, the number of possible resource assignments increases exponentially with the increase of the range of available resources. Many researchers combined genetic algorithms (GAs) and other optimization techniques with simulation models to reach the Pareto solutions. However, due to the large number of resources required in complex and large-scale construction projects, which results in a very large search space, and the limittion of the GA capability in fast convergence to the optimum results, parallel computing is required to reduce the computational time. This paper proposes the usage of Non-dominated sorting genetic algorithm (NSGA-II) as the optimization engine integrated with Discrete Event Simulation (DES) to model the bridge construction processes. The parallel computing platform is applied to reduce the computation time necessary to deal with multiple objective functions and the large search space.

关键词： bridges (structures) discrete event simulation genetic algorithms parallel processing structural engineering computing DES NSGA-II Pareto solution bridge construction process construction projects nondominated sorting genetic algorithm objective functions optimization techniques parallel computing resource assignment simulation models simulation-based multiobjective optimization Bridges Computational modeling genetic algorithms Optimization Parallel processing Sociology Statistics PARALLEL PROCESSING (COMPUTERS) discrete event simulation genetic algorithm (GA) Sociology Simulation models DES gene Diethylstilbestrol Computational modeling Objective function bridges Bridge construction

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Optimal Set Points Regulation of Distributed Generation Units in Micro-grids under Islanded Operation

Optimal Set Points Regulation of Distributed Generation Unit...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Graditi, G. Ippolito, M. G. Sanseverino, E. Riva Zizzo, G. ENEA Portici Napoli Naples Italy DIEET Univ Palermo Palermo Italy

ISBN: (纸本)9781424463916

The present work studies the problem of optimizing the power production levels of dispersed generation units in islanded microgrids. The problem is intrinsically multi-objective with non linear objectives and constraints, thus the solution approach is based on evolutionary optimization and uses the Non dominated sorting genetic algorithm II. The objectives are calculated based on the solution of the load flow problem. The latter problem is more complicated when in the considered system a physical node with a sufficiently large production capability is not available, because all the generation node of the systems have similar and limited generation capability. In this paper, the issue has been solved including into the optimization string also the slack bus identifier. This idea, together with the use of a suitable multiobjective technique, allows to reach interesting results and to optimally dispatch the power from the different generating units.

关键词： dispersed generation units distributed generation units distributed power generation evolutionary optimization generation node genetic algorithms islanded operation load flow load flow problem microgrids nondominated sorting genetic algorithm optimal set points regulation optimization string physical node power production levels slack bus identifier

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Design and Implementation of Fuzzy-PI Controllers for PMSM Based on Multi-Objective Optimization algorithms 5

Design and Implementation of Fuzzy-PI Controllers for PMSM B...

引用

5th International Conference on Mechanical, Automotive and Materials Engineering (CMAME)

作者： Kao, I-Hsi Lu, Kuna-Chung Perng, Jau-Woei Natl Sun Yat Sen Univ Dept Mech & Electromech Engn Kaohsiung Taiwan

ISBN: (纸本)9781538604328

Various intelligent algorithms have been applied to our daily lives, such as fuzzy theory, neural networks, and machine learning. These methods are widely used for solving many real-world problems;however, these algorithms also exhibit deficiencies and limitations. This paper introduces the recently improved algorithm, known as multi-objective particle swarm optimization, based on decomposition and dominance (D<^>2 MOPSO) in order to design the permanent magnet synchronous motor (PMSM) fuzzy controller for different objects. This means that the user can easily change the customized controller, according to their requirements. Furthermore, this paper compares the final decision of the controller parameter with other algorithms: the multi-objective particle swarm optimization with crowding distance (MOPSO-CD), and nondominated sorting genetic algorithm II (NSGA-II). The simulation results of the three algorithms indicate the optimum PMSM controller parameter in the computing software MATLAB. Finally, we implement the fuzzy controller in an embedded system (DSP28069) to demonstrate that our design matches the reality system response and meets the user's demands with ease.

关键词： fuzzy control multi-objective optimization algorithm multi-objective particle swarm optimization MOPSO nondominated sorting genetic algorithm NSGA-II

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A Multi-objective Optimization Application in Friction Stir Welding: Considering Thermo-mechanical Aspects

A Multi-objective Optimization Application in Friction Stir ...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Tutum, Cem Celal Hattel, Jesper Henri Tech Univ Denmark Dept Mech Engn DK-2800 Lyngby Denmark

ISBN: (纸本)9781424481262

The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2-dimensional sequentially coupled thermomechanical model implemented in the FE-code, ANSYS. The thermal model is based on a heat source description which in essence is governed by the rotational speed and the temperature dependent yield stress of the work piece material. This model in turn delivers the temperature field, in order to compute thermal strain field which is the main driver for the mechanical model predicting both transient and finally residual stresses in the work piece. This thermo-mechanical model is then used in the aforementioned constrained MOO case where the two objectives are conflicting. Following this, two reasonable design solutions among those multiple trade-off solutions have been selected based on the cost and the quality preferences.

关键词： FE-code finite element analysis friction stir welding friction welding genetic algorithms multiobjective optimization application nondominated sorting genetic algorithm optimisation residual stresses thermo-mechanical aspects yield stress yield stress

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Pareto Optimization of Wideband Circular Ring Arrays

Pareto Optimization of Wideband Circular Ring Arrays

引用

2010 IEEE International Symposium Antennas and Propagation/CNC-USNC/URSI Radio Science Meeting

作者： Bianchi, Davide Genovesi, Simone Corucci, Alessandro Monorchio, Agostino Univ Pisa Dept Informat Engn Microwave & Radiat Lab I-56122 Pisa Italy

ISBN: (纸本)9781424449682

Our aim is to synthesize a wideband planar array which have to fulfill constraints imposed to the radiation pattern in terms of side lobe level (SLL) within a specified angular range. More in detail, we focus on a particular 2D configuration of antennas, namely the class of Concentric Ring Arrays (CRAs). Planar arrays present advantages with respect to linear ones in case of three dimensional beamforming and CRAs are also symmetric and guarantee a nearly invariant pattern for a full azimuthal coverage. We also require that the proposed solution is physically feasible, thus the minimum distance between the elements is fixed to be not lower than the dimensions of antenna (being this latter dictated by its physical size and by the need of mitigating the effect of e.m. coupling). Since we are interested in developing an efficient code for more than two objectives a dedicated version of a multi-objective genetic algorithm, called nondominated sorting genetic algorithm (NSGA-II) has been implemented to optimize the array factor of CRAs at three different frequencies.

关键词： NSGA-II Pareto optimisation Pareto optimization antenna radiation pattern antenna radiation patterns broadband antennas concentric ring arrays genetic algorithms multiobjective genetic algorithm nondominated sorting genetic algorithm planar antenna arrays side lobe level sorting three dimensional beamforming wideband circular ring arrays wideband planar array synthesis

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Two Objective Matching Pursuit for Seismic Data Decomposition

Two Objective Matching Pursuit for Seismic Data Decompositio...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Li, Yongqing Wang, Jun Li, Hui Ren, Peng China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China

ISBN: (纸本)9781728124858

Matching pursuit is an effective method for seismic data decomposition. The traditional matching pursuit has one single objective that successively maximizes the inner products between signal residuals and atoms at different approximating levels. However, such inner product strategy does not guarantee the consistency of the peak of an atom and that of its corresponding signal residual. To address this limitation, we propose a two objective matching pursuit strategy that not only optimizes the traditional matching pursuit but also minimizes peak distance. Compared with the traditional matching pursuit with one single objective, the two objective matching pursuit has higher search ability. In this scenario, the genetic algorithm (GA) used for solving the traditional matching pursuit is not suitable for the two objective matching pursuit. Therefore, we propose to solve the two objective optimization problem by exploiting a nondominated sorting genetic algorithm (NSGA-II) algorithm, which not only increases the diversity of searching optimization and but also reduces the reconstruction error over the GA. Experiments for seismic data validate the advantages of the proposed two objective matching pursuit.

关键词： matching pursuit nondominated sorting genetic algorithm multiobjective optimization

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An Improved NSGA-II to Solve Multi-Objective Optimization Problem

An Improved NSGA-II to Solve Multi-Objective Optimization Pr...

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26th Chinese Control and Decision Conference (CCDC)

作者： Fu, Yaping Huang, Min Wang, Hongfeng Jiang, Guanjie Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479937066

NSGA-II(nondominated sorting genetic algorithm II) is a popular multi-objective evolution algorithm (MOEA), which applies binary tournament selection, elitist preserving strategy, nondominated sorting and crowding distance mechanism to obtain a good quality and uniform spread nondominated solution set. In this paper, an improved version of NSGA-II (INSGA-II) is proposed aiming to increase the diversity and enhance the local search ability. The INSGA-II has two populations: interior population and external population. The external population is used to store the nondominated solution found in the search process, while the interior population takes part in generation evolution. When the interior population tends to converge, it is updated by the individuals in the external population and generated randomly. A local search based on the amount of domination is applied to enhance the local search ability. In order to demonstrate the effectiveness of the proposed INSGA-II, comparisons with NSGA-II is carried out by ten functions, and the results show the quality and spread of INSGA-II are better than NSGA-II.

关键词： Multi-objective evolution algorithm nondominated sorting genetic algorithm Local search acceptance with probability

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