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Optimal sheet metal fixture locating layout by combining radial basis function neural network and bat algorithm

ADVANCES IN MECHANICAL ENGINEERING 2016年第12期8卷 1-10页

作者： Wang, Zhongqi Yang, Yuan Yang, Bo Kang, Yonggang Northwestern Polytech Univ Minist Educ Key Lab Contemporary Design & Integrated Mfg Tech 127 Youyi Rd West Xian 710072 Peoples R China

Considering that sheet metal part has the properties of thin wall, low rigidity, easy to deform, and difficult to locate, this article proposes a new approach to optimizing sheet metal fixture locating layout by combining radial basis function neural network and bat algorithm. First, taking fixture locating layout as design variables based on the "N-2-1'' locating principle, this article generates limited training and testing sample sets by Latin hypercube sampling and finite element analysis. Second, the radial basis function neural network prediction model with the description of the nonlinear mapping relationship between the fixture locating layout and the corresponding sheet metal deformation is constructed through learning from the training sample sets. Third, bat algorithm is applied to search the optimal layout of the "N'' fixture locators for the minimum sheet metal deformation. Finally, two case studies are presented to demonstrate the optimization procedure and the effectiveness of the proposed method.

关键词： Sheet metal part fixture layout optimization Latin hypercube sampling radial basis function neural network bat algorithm

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A variable selection method based on bat algorithm for near spectral analysis

A variable selection method based on bat algorithm for near ...

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中国化学会第14届全国计算(机)化学学术会议暨分子模拟国际论坛

作者： Peng Liu Xihui Bian Junfu Wei Wenqiang Wang State Key Laboratory of Separation Membranes and Membrane Processes School of Environmental and Chemical Engineering Tianjin Polytechnic University

Near infrared（NIR） spectroscopy combined with multivariate calibration has been widely used for the quantitative analysis of complex samples in petrochemical, agricultural, pharmaceutical and other *** quality of a multivariate calibration model mainly depends on the quality of both samples and ***,NIR spectra are typically consisted of weak, non-specific and overlapped bands and in each NIR spectrum,thousands of variables are *** of the variables may be irrelevant variables for multivariate *** calibration models may be obtained by selecting characteristic variables instead of the *** essence of variable selection for multivariate calibration is to determine a variable subset with which the established model gives the minimum predictive *** many methods such as genetic algorithm（GA）,uninformative variable elimination（UVE）, Monte Carlo-UVE（MC-UVE）, randomization test（RT） have been proposed, the variable selection problem is a NP-hard *** is necessary to develop new variable selection *** algorithm（BA）, as a new nature-inspired meta-heuristic algorithm, was proposed by Yang in 2010,based on the echolocation behaviour of *** spite of high abilities of BA, this algorithm was entered to the field of chemistry with some delay, and only a few publications based on BA have been reported in the chemical *** work firstly introduced BA for NIR spectral variable *** iteration times, sound, frequency and bat number of the method are investigated *** BA is used to determine variable subset by the optimal ***, the selected variables are used to build PLS *** performance of BA is compared with UVE, MC-UVE and RT by four NIR spectral *** shows that the PLS model built using the variables selected by BA can give much better predictive accuracy than those of UVE, MC-UVE and ***, BA takes more time than its competitors for a

关键词： Near infrared Multivariate calibration Variable selection bat algorithm

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A hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem

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ARTIFICIAL LIFE AND ROBOTICS 2016年第1期21卷 112-119页

作者： Pravesjit, Sakkayaphop Univ Phayao Sch Informat & Commun Technol Phayao 56000 Thailand

This paper proposes a hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem. In this study, the proposed algorithm combines the reproduction step from weed algorithm and genetic algorithm. The reproduction step is applied to clone each bat population by fitness values and the genetic algorithm is applied in order to expand the population. The algorithm is evaluated on eighteen benchmark problems. The computational results of the proposed algorithm are compared with the methods in the literature which are self-adaptive differential evolution (DE), traditional DE algorithm, intersection mutation differential evolution (IMDE) algorithm, and the JDE self-adaptive algorithm. Findings show that the algorithm produces several solutions obtained by the previously published methods especially for the continuous unimodal function, the quartic function, the multimodal function and the discontinuous step function. In addition, the finding shows that the proposed algorithm can produce optimal solutions efficiently on benchmark instances within short computational time.

关键词： bat algorithm Natural-inspired algorithm Reproduction step Optimization problem

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Localization of WSN Using Fuzzy Inference System with Optimized Membership Function by bat algorithm

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2016年第5期9卷 19-+页

作者： Shi, Hao Wang, Wanliang Lu, Liangjin Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

Localization is one of the most important research topics in the wireless sensor network applications. To improve the indoor localization accuracy, the centroid localization algorithm based on Mamdani fuzzy system has been adopted to attain the weight between sensor node and anchor node. This paper proposes a novel optimized input membership function by bat algorithm in fuzzy inference system using the data of received signal strength in real indoor condition. The author has realized the algorithm on Zigbee platform and the experimental comparison on other different centroid localization algorithms indicates that Mamdani fuzzy inference adopting the membership function optimized by bat algorithm renders smaller mean localization errors.

关键词： Wireless sensor network (WSN) weighted centroid localization algorithm fuzzy inference system bat algorithm received signal strength (RSS)

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Design of Robust PID Controller for Buck Converter using bat algorithm 1

Design of Robust PID Controller for Buck Converter using Bat...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Omer, Prabhu Kumar, Jagdish Surjan, Balwinder Singh PEC Univ Technol Elect Engn Dept Chandigarh India

ISBN: (纸本)9781467385879

Buck converters are still used as an efficient step down DC-DC converters for constant load voltage for many power supply applications under variable input voltage and different loading conditions. In most of the applications DC-DC converters are controlled by either voltage or by current mode controller. DC-DC converters exhibit non-linear behavior due to switching, hence controller design comes with complexities. With ordinary PI controller, dynamic response of Buck converter is sluggish and exhibits large deviations. In this paper robust PID controller based on H infinity paradigm has been designed for voltage mode controlled buck converter with both variable input voltage and load variation. The controller design problem has been formulated as mixed sensitivity minimization problem and solved using bat algorithm. From the results obtained, the designed controller exhibits robust behavior by satisfying the robust stability criterion. The design and simulation has been carried out using MATLAB/SIMULINK and results verify the robustness of the controller for large input voltage and load variations.

关键词： Robust Control DC-DC Converters Buck Converter bat algorithm Continuous Current Mode

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Median-Oriented bat algorithm for Function Optimization 12th

Median-Oriented Bat Algorithm for Function Optimization

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12th International Conference on Intelligent Computing (ICIC)

作者： Zhao, Limin Li, Haifeng Tianshui Normal Univ Sch Elect Informat & Elect Engn Tianshui 741001 Peoples R China

ISBN: (纸本)9783319422916;9783319422909

The bat algorithm is easily trapped into local optima, the population diversity is poor, and the optimizing precision is bad. In order to overcome these disadvantages, this paper presents a median bat algorithm (MBA) to avoid local optima and carry out a global search over entire search space. The proposed algorithm adopts the median position of the bats. And the median and worst bats are combined to the basic bat algorithm to achieve a better balance between the global search ability and local search ability. The simulation results of 10 standard benchmark functions show that the proposed algorithm is effective and feasible in both low-dimensional and high-dimensional case. Compared to the basic bat algorithm, particle swarm optimization and CLSPSO, the proposed algorithm can get high precision and can almost reach the theoretical value.

关键词： bat algorithm Median-oriented Function optimization

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Optimal Placement and Sizing of DG for Power Loss Minimization and VSI Improvement using bat algorithm

Optimal Placement and Sizing of DG for Power Loss Minimizati...

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National Power Systems Conference (NPSC)

作者： Prakash, Ram Sujatha, B. C. Bangalore Univ UVCE Dept Elect Engn Bengaluru India

ISBN: (纸本)9781467399685

Over the last few years, Distribution Generations (DGs) are fast finding their importance in solving growing environmental problems and rising energy demands. However, installation of DG in distribution network may have positive or negative impacts on power system depending on system configuration and local issues. bat algorithm (BA) with varying loudness and pulse rate is proposed in this paper for the optimal location and sizing of DG in radial distribution system in order to minimize real power losses and maximize voltage stability index (VSI), along with improving voltage profile within the range of the voltage constraint. Two cases based on either real or both real and reactive power generating capability of DG is considered. Both the cases include single as well as multiple DG units for performance analysis of DG on IEEE 69 bus systems. To verify the efficiency of proposed method a comparison is made with Standard Particle Swarm Optimization (PSO). The simulation results reveal that bat algorithm (BA) is better than PSO in terms of power losses, VSI and quality of solution.

关键词： Distribution Generation (DG) Power losses voltage stability index (VSI) Particle Swarm Optimization bat algorithm

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EDGE DETECTION USING bat algorithm 3

EDGE DETECTION USING BAT ALGORITHM

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Dwivedi, Rinky Sethi, Hargim Kaur Rohilla, Monisha Garg, Vaani Nagpal, Yashika Maharaja Surajmal Inst Technol New Delhi India

ISBN: (纸本)9789380544199

The bat algorithm (BA) is a bio-inspired algorithm authored by Xin-She Yang in 2010[8]. The bats use echolocation behavior, fluctuating pulse rates of ejaculation and modulation, to locate their dupe and hurdles. Same data set is also used to identify non-identical class of insects in the complete black out situation. The present research focuses on solving the Edge detection problem and for the purpose, the research inherits the concepts of bat algorithm (BA) from the pool of swarm intelligence algorithms. The relevance of bat algorithm is centered on the positioning of the bats and for the current problem;the bats are directed by the confined deviations in the image's acuteness assessment. Empirical outcomes have been feathered to exhibit the attainment of the contingent approach.

关键词： bat algorithm Edge Detection Fitness Function Optimization Swarm Intelligence

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GPU-powered bat algorithm for the Parameter Estimation of Biochemical Kinetic Values 13

GPU-powered Bat Algorithm for the Parameter Estimation of Bi...

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13th IEEE Annual Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)

作者： Tangherloni, Andrea Nobile, Marco S. Cazzaniga, Paolo Univ Milano Bicocca Dept Informat Syst & Commun I-20126 Milan Italy SYSBIO IT Ctr Syst Biol I-20126 Milan Italy Univ Bergamo Dept Human & Social Sci I-24129 Bergamo Italy

ISBN: (纸本)9781467394727

The emergent behavior of biochemical systems can be investigated by means of mathematical modeling and computational analyses, which usually require the automatic inference of the unknown values of the model's parameters. This problem, known as Parameter Estimation (PE), is usually tackled with bio-inspired meta-heuristics for global optimization, most notably Particle Swarm Optimization (PSO). In this work we assess the performances of PSO and bat algorithm with differential operator and Levy flights trajectories (DLBA). In particular, we compared these meta-heuristics for the PE using two biochemical models: the expression of genes in prokaryotes and the heat shock response in eukaryotes. In our tests, we also evaluated the impact on PE of different strategies for the initial positioning of individuals within the search space. Our results show that DLBA achieves comparable results with respect to PSO, but it converges to better results when a uniform initialization is employed. Since every iteration of DLBA requires three fitness evaluations for each bat, the whole methodology is built around a GPU-powered biochemical simulator (cupSODA) which is able to parallelize the process. We show that the acceleration achieved with cupSODA strongly reduces the running time, with an empirical 61 x speedup that has been obtained comparing a Nvidia GeForce Titan GTX with respect to a CPU Intel Core i7-4790K. Moreover, we show that DLBA always outperforms PSO with respect to the computational time required to execute the optimization process.

关键词： bat algorithm GPGPU Computing Parameter Estimation Particle Swarm Optimization

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An Improved bat algorithm with Variable Neighborhood Search for Global Optimization

An Improved Bat Algorithm with Variable Neighborhood Search ...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Wang, Gai-Ge Lu, Mei Zhao, Xiang-Jun Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

ISBN: (纸本)9781509006229

bat algorithm (BA) is newly proposed bio-inspired metaheuristic algorithm with the inspiration of the echolocation of bats in nature. Several experimental results have proven to the effectiveness and performance of BA. However, BA may fail to find the global optimal solution occasionally. In this paper, a kind of classical search technology, called variable neighborhood search (VNS), is incorporated into BA as a local search tool. An improved version of BA namely variable neighborhood bat algorithm (VNBA), is thus proposed. In VNBA, the classic BA as a global search tool searches the whole space globally, and this can significantly shrink the search space. Subsequently, VNS as a local search tool is implemented to find the final best solution within the small promising area. After that, the VNBA is benchmarked by sixteen standard benchmark functions. The experimental results imply that VNBA takes the absolute advantage over the basic BA.

关键词： Swarm intelligence Optimization bat algorithm Variable neighborhood search Benchmark functions

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