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检索条件"主题词=Particle Swarm Optimization algorithm"

共 1417 条记录，以下是171-180 订阅

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Hardware Implementation of the particle swarm optimization algorithm 24

Hardware Implementation of the Particle Swarm Optimization A...

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24th International Conference Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Talaska, Tomasz Dlugosz, Rafal Pedrycz, Witold UTP Univ Sci & Technol Fac Telecommun Comp Sci & Elect Engn Ul Kaliskiego 7 PL-85796 Bydgoszcz Poland Delphi Poland SA Ul Podgorki Tynieckie 2 PL-30399 Krakow Poland Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

ISBN: (纸本)9788363578121

In this paper, we present a concept of a transistor level implementation of the particle swarm optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN that operates in accordance with the PSO algorithm is composed of a set of cooperating particles (agents) that explore an input data space and communicate information on the best found solution to other particles. The PSO algorithm is usually implemented in software. We in our investigations focus on its transistor level realization. Such an approach enables parallel data processing, in which the overall data rate only moderately depends on the number of particles. Most of the operations and components of such implemented PSO algorithm may be reused considering our former CMOS realizations of other self-organizing learning algorithms. This allowed us to assess main parameters of the PSO.

关键词： particle swarm optimization algorithm CMOS Artificial Neural Networks Analog signal processing

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A Novel Multiobjective particle swarm optimization algorithm with Dynamic Resource Allocation

A Novel Multiobjective Particle Swarm Optimization Algorithm...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Li, Lingjie Lin, Qiuzhen Wang, Jia Chen, Jianyong Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 510860 Guangdong Peoples R China

ISBN: (纸本)9781728121536

This paper proposes a novel multiobjective particle swarm optimization algorithm with dynamic resource allocation, showing promising performance especially for tackling some complicated multiobjective optimization problems. With the decomposition approach, each particle is assigned to optimize one subproblem with a novel velocity update strategy to speed up the convergence. Moreover, a dynamic resource allocation strategy is designed based on the relative improvement of subproblems, which can reasonably allocate computational resource to the particles that are able to search superior solutions. By this way, the proposed algorithm not only has strong exploratory capability, but also can converge quickly to the true Pareto-optimal front. The experimental results fully demonstrate the superiority of our proposed algorithm over four state-of-the-art multiobjective optimization algorithms, when tackling thirty-five test problems.

关键词： Multiobjective optimization particle swarm optimization algorithm Resource allocation strategy

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Short-term load forecasting using artificial neural network based on particle swarm optimization algorithm

Short-term load forecasting using artificial neural network ...

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20th Annual 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)9781424410200

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. In this work, for determining the competitive learning model, the particle swarm optimization (PSO) technique is used as a training algorithm to adjust the weights of the artificial neural networks (ANNs) model to predict hourly loads. The feature of PSO is to fly potential solutions through hyperspace, accelerating toward better solutions. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. The historical load and weather information were trained and tested over a period of one season through two years. Generalized error estimation is done by using the reverse part of the data as a "test" set. The results were compared with conventional back-propagation algorithm and yielded encouraging results.

关键词： predicted hourly load neural networks particle swarm optimization algorithm

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State Variable Filter Design Using Improvised particle swarm optimization algorithm

State Variable Filter Design Using Improvised Particle Swarm...

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International Conference on Artificial Intelligence and Evolutionary algorithms in Engineering Systems (ICAEES)

作者： Indoria, Aakash Varrun, Varatharajan Akshay Reddy, Murali Krishna Sathyasai, Tejaswi Anand, Baskaran Devi, Nirmala M. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9788132221357;9788132221340

State variable filter design using particle swarm optimization algorithm proves to be better when compared to the conventional design method. It gives several solutions to the component values which are useful in designing the state variable filter. The automatic termination technique gives the best possible solution in lesser time. This technique has several advantages in terms of a quicker convergence rate and efficient computation toward the suitable output, where an added advantage gives the user a control over the output's precision. The performance parameter here can be defined as the trade-off between the convergence time and accuracy of the resulting solution, which is determined by the precision value. The results also indicate that the solution with a predefined precision level can be obtained with the minimum number of iterations in minimum time.

关键词： particle swarm optimization algorithm Real-world optimization problems Complex optimization problems PSO algorithm

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The Application of particle swarm optimization algorithm on Absorbent Materials

The Application of Particle Swarm Optimization Algorithm on ...

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2nd International Conference on Mechanics and Control Engineering (ICMCE 2013)

作者： Feng, Hongquan Wu, Bingheng Liu, Yuanyun Liao, Yi Gu, Hao Yu, Xing Shanghai Radio Equipment Res Inst 203 Liping RD Shanghai 200090 Peoples R China Shanghai Key Lab Elect Environm Effects Aerosp Ve Shanghai 200438 Peoples R China

ISBN: (纸本)9783037859087

The thickness of multi-layer absorbing material is optimized to obtain lower electromagnetic reflection coefficient by using particle swarm optimization (PSO) algorithm in this paper. Two examples are employed to validate the excellent performance of PSO. The results show that the reflection coefficient of absorbing material is less than-20 dB over the bandwidth of 2GHz similar to 18GHz, less than -25 dB over the narrowband of 9 GHz similar to 11GHz, less than-30 dB during the bandwidth of 9.5 GHz similar to 10.5 GHz. It also shows that the minimum value approaches to -48 dB in a certain range.

关键词： particle swarm optimization algorithm Absorbing material optimization design

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Hybrid particle swarm optimization algorithm for Area Minimization in 65 nm Technology 53

Hybrid Particle Swarm Optimization Algorithm for Area Minimi...

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Rashid, Ria Nambath, Nandakumar Indian Inst Technol Goa Sch Elect Sci Ponda 403401 India

ISBN: (纸本)9781728192017

Analog design can be considered as a multidimensional optimization problem since it involves trade-offs between several circuit parameters. Various optimization techniques have been proposed to reduce the cycle time of analog design. We propose a hybrid particle swarm optimization algorithm with linearly decreasing inertia weight for the optimization of analog circuit design. The proposed method is validated in a differential amplifier circuit with a current mirror load. Promising simulation results demonstrate that the proposed method can significantly reduce the design time required for analog circuits.

关键词： Analog circuit design area optimization particle swarm optimization algorithm

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Trajectory tracking control strategy of manipulator based on improved particle swarm optimization algorithm 4

Trajectory tracking control strategy of manipulator based on...

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4th Artificial Intelligence and Cloud Computing Conference (AICCC)

作者： Gang, Mingyi Pan, Xiaobo Tang, Kaiyuan Xia, Xingguo Feng, Benxiu Maanshan Tech Coll Dept Elect Engn Maanshan Peoples R China Sinosteel Maanshan Gen Inst Min Res Co LTD Maanshan Peoples R China

ISBN: (纸本)9781450384162

In view of the manipulator system is a highly coupled, nonlinear dynamic characteristics and the system structure and parameters, such as there are many unpredictable factors in the practical work of multiple input multiple output system, designed a fuzzy neural network controller, and combined with particle swarm optimization algorithm for fuzzy neural network controller parameter setting. Through MATLAB simulation, it is proved that the scheme has strong robustness and stability for the control system, and effectively solves the trajectory tracking problem of manipulator.

关键词： Manipulator Fuzzy neural network particle swarm optimization algorithm

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Higher-order Multivariate Markov Chains based on particle swarm optimization algorithm for Air Pollution Forecasting

Higher-order Multivariate Markov Chains based on Particle Sw...

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Asia-Pacific Conference on Information Processing (APCIP 2009)

作者： Wang, Zhilong Gong, Zengtai Zhao, Weigang Zhu, Wenjin Lanzhou Univ Sch Math & Stat Lanzhou 730000 Peoples R China Lanzhou Polytech Coll Dept Basic Courses Lanzhou 730050 Peoples R China Northwest Normal Univ Sch Math & Inforamt Sci Lanzhou 7330070 Peoples R China

ISBN: (纸本)9780769536996

This paper presents a higher-order multivariate Markov chain model combined with particle swarm optimization algorithm. Due to some deficiencies, such as only considering the maximum probability while ignoring the effect of the other probabilities, the traditional method of probability distribution has been replaced by the level characteristics value of fuzzy set theory;further more particle swarm optimization algorithm has been employed to optimize the coefficient of level characteristics value. In recent years, air pollution acutely aggravates chronic diseases in mankind, such as sulfur dioxide pollution which plays a most important role in acid rain. In order to confront air pollution problems and to plan abatement strategies, both the scientific community and the relevant authorities have focused on monitoring and analyzing the atmospheric pollutants concentration. Taking the forecast of air pollutants as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows the new method is predominant in forecasting of multivariate and non-linear data.

关键词： Mean-Standard deviation division method Higher-order multivariate Markov chain Level characteristics value particle swarm optimization algorithm

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Optimizing the Operation Sequence of Chip Shooter Machine Based on the particle swarm optimization algorithm

Optimizing the Operation Sequence of Chip Shooter Machine Ba...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Liang, Wanlei Guan, Xiaodan North China Inst Aerosp Engn Dept Elect Engn Langfang 065000 Peoples R China

ISBN: (纸本)9783037855034

The mounting process is the key factor of the placement efficiency, it is also important for the improvement of the efficiency of whole production line and decrease of the cost. This paper analyzed the mounting process of the Chip Shooter machine, applied the PSO algorithm, constructed the corresponding coding system, proposed the corresponding particle update mechanism, introduced the partially matched crossover idea of the genetic algorithm into the PSO algorithm, and designed the new re-scheduling method of feeder position assignment to optimize the position assignment of feeders and the pickup and placement sequence of components, thus improved the placement efficiency. After comparing the results before and after the simulation test for selected 8 pieces of PCB, the average efficiency of this algorithm is 7.09% higher than genetic algorithm method that is based on sort encoding. The experimental result shows that, this algorithm is more efficiency on the improvement placement efficiency and decrease of the placement time for the chip shooter machine.

关键词： Chip shooter machine Operation sequence optimization particle swarm optimization algorithm

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Optimizing Effective Fusion Representation by particle swarm optimization algorithm for Protein Sub-nuclear Location 6

Optimizing Effective Fusion Representation by Particle Swarm...

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6th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Yue, Yaoting Wang, Shunfang Yunnan Univ Sch Informat Sci & Engn Kunming 650504 Yunnan Peoples R China

ISBN: (纸本)9781538604939

Feature representation contains the more plentiful information of original protein sequence, the more beneficial for protein sub-nuclear localization. Inspired by this idea, this paper proposed a novel two-feature integration method, whose fusion parameter was optimized via the particle swarm optimization algorithm (PSO), for obtaining a more effective representation. Therefore, a new fusion representation, called AACPSSM, would be formed by integrating two kinds of single feature expression, amino acid composition (AAC) and position specific scoring matrix (PSSM). Due to the high dimensional characteristics of protein data, kernel linear discriminant analysis (KLDA) was used to conduct the data dimension reduction. Last, to evaluate validity of our proposed approach, a benchmark dataset and KNN classifier were used to carry out the numerical experiments. And the final Jackknife test experimental results prove that our proposed fusion representation AACPSSM largely outperforms the single one, AAC and PSSM.

关键词： Sub-nuclear location particle swarm optimization algorithm fusion parameter AACPSSM KLDA

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