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Bucharest PowerTech Conference

作者： Tangpatiphan, Kritsana Yokoyama, Akihiko Univ Tokyo Dept Elect Engn Tokyo 1138656 Japan Univ Tokyo Dept Adv Energy Chiba 2778586 Japan

ISBN: (纸本)9781424422340

This paper presents an Improved Evolutionary Programming (IEP) algorithm for the Optimal Power Flow (OPF) with steady-state voltage stability consideration. The objective function is formulated as a trade-off between an economic concern, i.e. fuel cost and a system security issue, i.e. voltage stability margin. The indicator L is adopted to estimate the steady-state voltage stability margin in this paper. The proposed IEP algorithm borrows an idea of crossover techniques from real-coded genetic algorithm (RCGA) to enhance the offspring generation process. The IEEE 30-bus system is used as the test system. Three types of cost curves are considered to verify that the proposed algorithm works well with both convex and non-convex objective functions. The results of the proposed method are compared with those of the EP algorithm, which solely relies on the mutation process for offspring generation. Moreover, a series of experiments are conducted to properly tune the main IEP parameters.

关键词： Evolutionary programming optimal power flow real-coded genetic algorithm voltage stability

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The Performance Measurement of a Canonical Particle Swarm Optimizer with Diversive Curiosity 1

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1st International Conference on Swarm Intelligence

作者： Zhang, Hong Zhang, Jie Kyushu Inst Technol Dept Brain Sci & Engn 2-4 Hibikino Kitakyushu Fukuoka 8080196 Japan Wuxi Bowen Software Technol Co Ltd Wuxi Jiangsu Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

For improving the search performance of a canonical particle swarm optimizer (CPSO), we propose a newly canonical particle swarm optimizer with diversive curiosity (CPSO/DC). A crucial idea here is to introduce diversive curiosity into the CPSO to comprehensively manage the trade-off between exploitation and exploration for alleviating stagnation. To demonstrate the effectiveness of the proposed method, computer experiments on a suite of five-dimensional benchmark problems are carried out. We investigate the characteristics of the CPSO/DC, and compare the search performance with other methods. The obtained results indicate that the search performance of the CPSO/DC is superior to that by EPSO, ECPSO and RGA/E, but is inferior to that by PSO/DC for the Griewank and Rastrigin problems.

关键词： canonical particle swarm optimizer evolutionary particle swarm optimization real-coded genetic algorithm exploitation and exploration model selection specific and diversive curiosity

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LS-SVM Based Soft Sensoring

LS-SVM Based Soft Sensoring

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7th World Congress on Intelligent Control and Automation

作者： Peng, Zhenrui Yang, Xijuan Qi, Wenzhe Lanzhou Jiaotong Univ Sch Mechatron Engn Lanzhou Gansu Peoples R China Lanzhou Jiaotong Univ Sch math Phys & Software Lanzhou Gansu Peoples R China

ISBN: (纸本)9781424421138

An improved Least Squares Support Vector Machine (LS-SVM) approach was proposed to overcome the drawback of "losing sparsity" in original LS-SVM. At the same time, real-coded genetic algorithm (RC-GA) was introduced to solve the difficult problem of parameters selection in LS-SVM. By discarding most data points with too large or too small training errors in the trained LS-SVM model, a more sparse and anti-noise LS-SVM model was obtained. The parameters selection of LS-SVM was regard as an optimization problem. The objective function of the optimization problem was established. And a RC-GA with high global searching capability was used to search the optimal parameters of LS-SVM. Both simulation and experiment results demonstrate the success of the improved LS-SVM and the effectiveness of RC-GA parameters optimization method.

关键词： Least squares support vector machine (LS-SVM) real-coded genetic algorithm parameter optimization two-phase flow

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Optimization of Broadband Wireless Networks with Centralized Control Using Memetic algorithm

Optimization of Broadband Wireless Networks with Centralized...

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International Conference on Information Networking (ICOIN)

作者： Horng, Shih-Cheng Yang, Feng-Yi Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan Natl Yang Ming Univ Dept Biomed Imaging & Radiol Sci Taipei 112 Taiwan

ISBN: (纸本)9781479936892

In this paper, a k-limited polling system enabling an adequate description of broadband wireless networks with centralized control is presented. A memetic algorithm (MA) is proposed to solve the k-limited polling system to obtain a good enough solution (k-limited threshold) using limited computation time. The proposed MA combines the global search and local search to achieve a balance between the exploration and exploitation in searching through the solution space. Firstly, a crude evaluation based on a small amount of transmitted packets is constructed to approximately evaluate the performance of a solution. In global search, we apply the real-coded genetic algorithm (GA) associated with crude evaluation to select N roughly good solutions from huge solution space to construct the selected subset. In local search, the simulated annealing (SA) assisted by crude evaluation is utilized to search for N neighboring optima to form the candidate subset. Finally, a ranking and selection (R&S) technique is used to select the best solution among the N neighboring optima in the candidate subset. As for the performance of minimizing the average of total expected waiting and loss cost, we have demonstrated that our approach drastically outperforms the developed service disciplines. The good enough solution obtained by our method is promising in the aspects of solution quality and computational efficiency.

关键词： k-limited polling system centralized broadband wireless networks memetic algorithm real-coded genetic algorithm simulated annealing ranking and selection

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Evolutionary Canonical Particle Swarm Optimizer - A Proposal of Meta-optimization in Model Selection

Evolutionary Canonical Particle Swarm Optimizer - A Proposal...

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18th International Conference on Artificial Neural Networks (ICANN 2008)

作者： Zhang, Hong Ishikawa, Masumi Kyushu Inst Technol Dept Brain Sci & Engn Kitakyushu Fukuoka 8080196 Japan

ISBN: (纸本)9783540875352

We proposed Evolutionary Particle Swarm Optimization (EPSO) which provides a new paradigm of meta-optimization for model selection in swarm intelligence. In this paper, we extend the technique of online evolutionary computation of EPSO to Canonical Particle Swarm Optimizer (CPSO), and propose Evolutionary Canonical Particle Swarm Optimizer (ECPSO) for optimizing CPSO. In order to effectually evalute the performance of CPSO, a temporally cumulative fitness function of the best particle is adoped in ECPSO as the behavioral representative for entire swarm. Applications of the proposed method to a suite of 5-dimensional benchmark problems well demonstrate the effectiveness. Our experimental results clearly indicate that (1) the proper parameter sets in CPSO for solving various optimization problems are not unique;(2) the values of parameters in them are quite different from that of the original CPSO;(3) the search performance of the optimized CPSO is superior to that of the original CPSO, and to that of RGA/E except for the result of the Rastrigin's benchmark problem.

关键词： canonical particle swarm optimization evolutionary particle swarm optimization temporally cumulative fitness real-coded genetic algorithm model selection

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Parameter Tuning of real-Valued Crossover Operators for Statistics Preservation

Parameter Tuning of Real-Valued Crossover Operators for Stat...

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7th International Conference on Simulated Evolution and Learning

作者： Someya, Hiroshi Inst Stat Math Minato Ku Tokyo 106 Japan

ISBN: (纸本)9783540896937

Parameters of real-valued crossover operators ha,,e been of ten tuned under a constraint for preserving statistics of infinite parental population. For applications in actual scenes, in a previous study, all alternative constraint, called unbiased constraint, considering finiteness of the population has been derived. To clarify the wide applicability of the unbiased constraint, this paper presents two additional Studies: (1) applying it to various crossover operators in higher dimensional search space, and (2) generalization of it, for preserving statistics of overall population. Appropriateness of the parameter setting based oil the unbiased constraint has been supported in discussion on robust search.

关键词： real-coded genetic algorithm functional specialization hypothesis statistics preservation parameter tuning

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A Physics Modeling of Butterfly's Flight Control by GA and ANN and Its Over-evolution Problem

A Physics Modeling of Butterfly's Flight Control by GA and A...

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16th International Symposium on Artificial Life and Robotics (AROB 16th '11)

作者： Ooe, R. Suzuki, I. Yamamoto, M. Furukawa, M. Hokkaido Univ Grad Sch Informat & Sci Sapporo Hokkaido Japan

ISBN: (纸本)9784990288051

We describe a simple physics model of a butterfly and an approach to its flight control by the genetic algorithm (GA) and the artificial neural network (ANN). A physics model consists of two kinetic equations which are led by a simplification of fluid force. A butterfly's flight is controlled by an ANN. The GA optimizes weights of the ANN for the suitable flight. This approach resulted in the flight which is obtained by maximizing the prepared fitness. However, the optimized ANN did not have a generality and a butterfly fell down in changing the initial height. A transition of fitness throughout processes of evolution showed that too much optimization tends to break the generality. We call this phenomenon "over-evolution". Changing conditions of experiments prevented the over-evolution.

关键词： Physical simulation Drag force Butterfly real-coded genetic algorithm Artificial neural network

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A Two-stage Traffic Sensor Location Method for Low-volume Road Incident Detection 13

A Two-stage Traffic Sensor Location Method for Low-volume Ro...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Liu, Xiao-Feng Zhang, Ji-Sheng Gao, Ting-Ting Guan, Zhi-Wei Song, Yu-Qing Tianjin Univ Technol & Educ Sch Automot & Transportat Tianjin 300222 Peoples R China Minist Commun Key Lab Intelligent Transportat Syst Technol Beijing 100088 Peoples R China

ISBN: (纸本)9781538621653

Low-volume road always lied at the area with few inhabitants and few traffic monitoring equipment, thus it's difficult to detect traffic incident and take traffic rescue in a timely manner. However, most research focused on high-volume road traffic sensor location for OD matrix estimation, travel time estimation and traffic volume estimation, not aiming to lowvolume road for incident detection. Firstly, a two-stage traffic sensor location method was proposed for low-volume road incident detection, with the optimization objective of maximizing incident detection rate. At the first stage, high-resolution camera Automatic Vehicle Identification (AVI) was used to monitor closed road segments, at the second stage, video was used to monitor both the closed road segments and intersection segments. Then, reliability theory was adopted to model the incident detection rate of AVI and video parallel detection system. Next, a real-coded genetic algorithm was proposed to optimize the traffic sensor location problem. Finally, a case study was conducted and the case study result shows that incident detection rate of the first and second stage increase by 36.39% and 7.68% respectively, this demonstrates that the proposed method is effective.

关键词： low-volume road incident detection traffic sensor location optimization real-coded genetic algorithm

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A Bootstrap Study of Variance Estimation under Heteroscedasticity using genetic algorithm

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JOURNAL OF STATISTICAL THEORY AND PRACTICE 2008年第1期2卷 55-69页

作者： Ghosh, Himadri Iquebal, M. A. Prajneshu Indian Agr Res Inst New Delhi India

The conventional ordinary least squares (OLS) variance-covariance matrix estimator for a linear regression model under heteroscedastic errors is biased and inconsistent. Accordingly, several estimators have so far been proposed by various researchers. However, none of these perform well under the finite-sample situation. In this paper, the powerful optimization technique of genetic algorithm (GA) is used to modify these estimators. Properties of these newly developed estimators are thoroughly studied by Monte Carlo method for various sample sizes. It is shown that GA-versions of the estimators are superior to corresponding non-GA versions as there are significant reductions in the Total relative bias as well as Total root mean square error.

关键词： Linear regression model Least squares estimators Heteroscedasticity real-coded genetic algorithm Bootstrap methods Total relative bias Total root mean square error

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Signal quality improvement of holographic data storage by adaptive two-dimensional filter

Signal quality improvement of holographic data storage by ad...

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Conference on Information Optics and Optical Data Storage

作者： Takahata, Yosuke Kondo, Yo Yoshida, Shuhei Yamamoto, Manabu Tokyo Univ Sci City Noda Chiba Japan

ISBN: (纸本)9780819483812

In this paper, we examine whether the use of a two-dimensional finite impulse response (FIR) filter in two presently available recording/reproduction methods, the Off-axis method, results in an improvement in the signal quality. The linear minimum mean square error (LMMSE) method is first tested to examine its effectiveness in optimizing the coefficients of the FIR filter. Subsequently, the real-coded genetic algorithm (RCGA), which has the capability of searching a wide range of coefficients, is applied and the result obtained is compared with that for the LMMSE method to see if the coefficient evaluation leads to a local solution or the minimum solution. We also applied quadratic minimum mean-square-error (QMMSE) Equalizer as a nonlinear equalizer.

关键词： holographic data storage finite impulse response linear minimum mean square error real-coded genetic algorithm quadratic minimum mean-square-error

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