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检索条件"主题词=Particle swarm optimization algorithm"

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Dynamic Scheduling of Multi-task for Hybrid Flow-shop Based on Idle Time Windows

Dynamic Scheduling of Multi-task for Hybrid Flow-shop Based ...

引用

21st Chinese Control and Decision Conference

作者： Zeng Ling-li Zou Feng-xing Gao Zheng Xu Xiao-hong Natl Univ Def Technol Coll Mechatron & Automat Dept Automat Control Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781424427239

This paper presents an approach based on idle time windows (ITWs) and particle swarm optimization (PSO) algorithm to solve dynamic scheduling of multi-task for hybrid flow-shop. The idea of ITW is introduced, then the dynamic updating rules of the sets of ITWs are explained in detail. With the sets of ITWs of machines as constraints, the mathematical model is presented for dynamic scheduling of multi-task for hybrid flow-shop. The PSO algorithm is proposed in order to solve this problem. The results of simulation indicate that this approach satisfies the demand of dynamic scheduling of multi-task.

关键词： Idle Time Window Hybrid Flow-shop Multi-task Dynamic Scheduling particle swarm optimization algorithm

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Non-uniformly spaced linear antenna array design by means of PEEC approach applying Cheetah optimization algorithm

Non-uniformly spaced linear antenna array design by means of...

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15th Biennial International Workshop on optimization and Inverse Problems in Electromagnetism (OIPE)

作者： Moreno, Sinvaldo R. Klein, Carlos E. Mariani, Viviana C. Coelho, Leandro dos S. Alotto, Piergiorgio Torchio, Riccardo Bauernfeind, Thomas Baumgartner, Paul Univ Fed Parana Dept Elect Engn Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind Syst Engn Grad Program Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Curitiba Parana Brazil Univ Padua Dipartimento Ingn Ind Padua Italy Graz Univ Technol Inst Fundamentals & Theory Elect Engn Graz Austria

This paper presents the application of the Partial Element Equivalent Circuit (PEEC) approach, which is a full wave electromagnetic modelling technique for conductors embedded in arbitrary dielectrics based on equivalent circuits, to the optimal design of antennas with non-uniform spacing between the array elements. The design optimization problem is solved by means of the new nature-inspired Cheetah metaheuristic. The main aim of this paper is to introduce the Cheetah optimization algorithm to the electromagnetics and antenna community. The results are compared to two well-known optimization algorithms and to show the effectiveness of the proposed algorithm on a realistic benchmark problem.

关键词： Antenna array Cheetah optimization algorithm genetic algorithm partial element equivalent circuit particle swarm optimization algorithm

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Application of PSO algorithm with Dynamic Inertia Weight in Medical Image Thresholding Segmentation 19

Application of PSO algorithm with Dynamic Inertia Weight in ...

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19th Annual IEEE International Conference on e-Health Networking, Applications and Services (Healthcom)

作者： LiuNa JiangYan LiShu Hubei Univ Med Ctr Hlth Adm & Dev Studies Shiyan Peoples R China

ISBN: (纸本)9781509067046

Medical image segmentation is an important application in medical image processing. In order to improve the efficiency of Medical image segmentation, a method to select optimal threshold by PSO algorithm with Dynamic Inertia Weight(DW-PSO) is proposed. It makes the proportion of the local and global searching ability can be effectively controlled in the whole process of optimal searching. Compared with PSO algorithm, DW-PSO gets better effects of image segmentation. The data of experiment shows that DW-PSO has obvious advantage in computing efficiency, the precision and stability of searching optimal threshold, as well as the effect of image segmentation.

关键词： particle swarm optimization algorithm Inertia weight Optimal threshold Medical image segmentation

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Performance Evaluation of optimization algorithms based on GPU using CUDA Architecture

Performance Evaluation of Optimization Algorithms based on G...

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5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Kawano, Yunkio Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Baja California Mexico

ISBN: (纸本)9781538646267

In this paper, an evaluation was made on the performance of two of the most commonly used optimization algorithms, which are the particle swarm optimization (PSO) algorithm and the Genetic algorithm (GA). In this case, average time of execution is compared, executing the algorithm using the original code on the traditional processor against the modified algorithm where certain processes of the algorithm are integrated into the video card. These changes demonstrate a significant improvement in execution time. On the other hand a graphical interface was made for each one of the optimization algorithms to facilitate the process of handling the parameters.

关键词： particle swarm optimization algorithm Genetic algorithm CUDA Architecture

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PMD monitoring with PSO-based DOP ellipsoid

PMD monitoring with PSO-based DOP ellipsoid

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Conference on Optical Transmission, Switching and Subsystems III

作者： Zhang, XG Duan, GY Yu, L Yu, ZY Yang, BJ Beijing Univ Posts & Telecommun Sch Sci Dept Phys Beijing 100876 Peoples R China

ISBN: (纸本)0819460524

The polarization mode dispersion (PMD) monitoring system is the key integral part of an adaptive PMD compensator. The degree of polarization (DOP) ellipsoid obtained by using a polarization scrambler can be used as either a feedback or feedforward monitoring signal for automatic polarization mode dispersion compensation. Generally, more than several thousands of sampling data of states of polarization (SOP) must be collected to insure getting a correct DOP ellipsoid. This would result in an unacceptable time consuming for adaptive PMB compensation. In this paper, for the first time, we introduce the particle swarm optimization (PSO) algorithm in determining the real-time DOP ellipsoid with high precision, requiring only 100 sampling data of SOPs. Experimental results confirm that the PSO algorithm is effective for ellipsoid data fitting with high precision within 250ms using our hardware environment.

关键词： polarization mode dispersion PMD monitoring techniques degree of polarization ellipsoid particle swarm optimization algorithm

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Power System Stabilization Improvement by Using PSO-based UPFC

Power System Stabilization Improvement by Using PSO-based UP...

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Joint International Conference on Power Systems Technology (POWERCON)/IEEE Power India Conference

作者： Vahidi, B. Jazebi, S. Baghaee, H. R. Tahan, M. Asadi, M. R. Amirkabir Univ Technol Dept Elect Engn Hafez Ave Tehran Iran Univ Tehran Sch Elect & Comp Engn Tehran 14174 Iran

ISBN: (纸本)9781424417636

A robust control strategy is proposed to control UPFC in different operating conditions. PI regulators used for UPFC suffer from the inadequacies of providing suitable control for transient stability enhancement over a wide range of system operating conditions. In this paper an optimization method based on particle-swarm optimization (PSO) algorithm is presented to optimize the parameters of the PI controller of UPFC in order to enhance the power system transient stability. This scheme dispenses the gain dependency of the proportional or integral gains and generates independent control actions. The effectiveness of the proposed method is demonstrated using simulations of a multi-machine power system.

关键词： Inter-area Oscillations particle swarm optimization algorithm Power System Stability UPFC

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A Decision Support System Based on Metaheuristic Model for Aircrafts Landing Problems

A Decision Support System Based on Metaheuristic Model for A...

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Confederated International Workshops and Posters: EI2N plus NSF ICE, ICSP plus INBAST, ISDE, ORM, OTMA, SWWS plus MONET plus SeDeS, and VADER

作者： Hbaieb, Sonda Dhouib, Souhail Chabchoub, Habib Univ Sfax Res Unit Logist Ind Management & Qual Higher Inst Ind Management Sfax Tunisia

ISBN: (纸本)9783642251252

This paper presents a Decision Support System (DSS) designed to aid the manager to optimize the scheduling aircrafts (planes) landings at an airport. This problem is one of deciding a landing time for each plane such that each plane lands within a predetermined time window and the landings of all successive planes are respected. This DSS is developed using 4D language, it is composed of three components. 1) The Model Subsystem;the particle swarm optimization metaheuristic allows the optimization model for identification of aircraft that respect the preferences of the decision maker. 2) The Data Subsystem;the DSS uses a knowledge base composed of data publicly available from OR-LIBRARY, involving from 10 to 50 aircrafts (we consider 25 problems in total). 3) The Dialog Subsystem;a visual interactive simulation model will allow the interactive and convivial resolution.

关键词： DSS simulation metaheuristic particle swarm optimization algorithm transport scheduling

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Application of Hybrid Salp swarm optimization Method for Solving OPF Problem

Application of Hybrid Salp Swarm Optimization Method for Sol...

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International Conference on Advanced Electrical Engineering (ICAEE)

作者： Khelifi, Aboubakr Bentouati, Bachir Chettih, Saliha El Sehiemy, Ragab A. Amar Telidji Univ LMSF Lab Laghouat Algeria Univ Kafrelsheikh Dept Elect Engn Fac Engn Kafr Al Sheikh Egypt

ISBN: (纸本)9781728122205

In this paper, a new hybrid solution to the Optimal Power Flow (OPF) problem is proposed. In order to achieve this goal, a new hybrid Salp swarm algorithm (HSSA) is proposed to find the optimal frontier of OPF problem. The proposed hybrid algorithm combines the advantages of the salp swarm algorithm (SSA) and particle swarm optimization (PSO) algorithm. The proposed HSSA provides more efficient solutions even for conflict constraints. This method is applied on five objective functions called power generation cost, environmental pollution emissions, active power loss, voltage deviation and voltage stability. The tests and results of the proposed HSSA have been applied to IEEE 30 bus test system to demonstrate the high performance compared with other optimization methods in the literature. Single and bi-objectives studied cases are tested to prove the capability of the proposed HSSA compared with the original SSA and PSO as well as the existing methods in the literature.

关键词： Optimal power flow salp optimization algorithm particle swarm optimization algorithm HSSA multi-objective HSSA emission minimization enhancing voltage profile

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SVM Classification: optimization with the SMOTE algorithm for the Class Imbalance Problem 6

SVM Classification: Optimization with the SMOTE Algorithm fo...

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6th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Klyueva, Irina Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781509067428

In this paper a new approach to selection of the optimal parameters values for the SMOTE (Synthetic Minority Over-sampling Technique) algorithm in the problem of the SVM (Support Vector Machine) classification of imbalanced datasets has been suggested. This approach allows reducing the time expenditures for the search of the optimum parameters values of the SMOTE algorithm. The experimental results show that the offered approach allows increasing the classification quality of the SVM classifier.

关键词： sampling imbalanced dataset SVM classifier particle swarm optimization algorithm hybridization

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Locational marginal pricing prediction in a competitve electrical market using computational intelligence

Locational marginal pricing prediction in a competitve elect...

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Canadian Conference on Electrical and Computer Engineering

作者： Bashir, Z. A. El-Hawary, M. E. Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3J 2X4 Canada

ISBN: (纸本)9781424416424

Deregulation has created a competitive market among power market participants, and the pricing system plays an important role. Locational marginal pricing (LMP) provides clear market signals that identify the locations where power market participants could make their decisions so as to maximize their profits. In this work, artificial neural networks (ANNs) models are used to predict hourly LMP. ANN is trained using the particle swarm optimization (PSO) algorithm. PSO aims to minimize the error function by adjusting neural network's weights and biases using a stochastic optimal search. Wavelet transformed data is fed into neural network as pre-processing stage in order to get a better price pattern that will be reliable for forecasting. The historical LMP and corresponding load demand and temperature are trained, validated and tested over a period of one season. The efficient generalization of proposed model is investigated using early stopping technique. The results were compared with neural models using conventional back-propagation (BP) algorithm and radial basis function (RBF) and yielded encouraging results.

关键词： predicted hourly LMP neural networks wavelet transform particle swarm optimization algorithm

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