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检索条件"主题词=BAT algorithm"

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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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Performance Analysis of Chaotic Levy bat algorithm and Chaotic Cuckoo Search algorithm for Gray Level Image Enhancement 2

Performance Analysis of Chaotic Levy Bat Algorithm and Chaot...

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2nd International Conference on Information Systems Design and Intelligent Applications (INDIA)

作者： Dhal, Krishna Gopal Quraishi, Md. Iqbal Das, Sanjoy Univ Kalyani Dept Engn & Technol Studies Kalyani 741235 W Bengal India Kalyani Govt Engn Coll Dept Informat Technol Kalyani W Bengal India

ISBN: (纸本)9788132222507;9788132222491

Dark images can be enhanced in a controlled manner with the help of nature inspired metaheuristic algorithm. In this case image enhancement has been taken as a nonlinear optimization problem. bat algorithm (BA) and Cuckoo Search (CS) algorithm is one of the most powerful metaheuristic algorithms. In this paper these two algorithms have been modified by chaotic sequence and levy flight. In BA levy flight with chaotic step size helps to do intensification. In CS algorithm the random walk has been done via chaotic sequence. Entropy and edge information has been used as objective function. From quantitative and visual analysis it is clear that chaotic levy BA outperforms the chaotic CS algorithm.

关键词： bat algorithm Cuckoo search Particle swarm optimization Levy flight Chaotic sequence Enhancement factor

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Optimal Power Dispatch Using bat algorithm

Optimal Power Dispatch Using BAT Algorithm

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International Conference on Energy Efficient Technologies for Sustainability (ICEETS)

作者： Biswal, S. Barisal, A. K. Behera, A. Prakash, T. VSSUT Dept Elect Engn Burla Odisha India

ISBN: (纸本)9781467361507;9781467361491

bat algorithm is a recent addition to the bio-inspired algorithms, considered as a new metaheuristic algorithm based on bat behaviour. This work presents, the optimal solution of economic load dispatch (ELD) is obtained using the proposed bat algorithm. Here the operating cost of a thermal power plant is optimized using bat algorithm. Numerical results show that the proposed method has good convergence property and better in quality of solution than PSO and IWD reported in recent literature. The main advantage of the proposed technique is easy is implement and capable of finding feasible near global optimal solution with less computational effort. bat algorithm is easy to implement and priory in terms of accuracy and efficiency compared to other algorithms. In order to illustrate the effectiveness of the proposed method, it has been tested on 3 and 6-unit system.

关键词： bat algorithm Economic load dispatch power balance Mathematical modeling Prohibited Zone Particle swarm optimization Valve point loading

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Fuzzy bat algorithm for improved Clustering of Convolutional Neural Network Data 37

Fuzzy Bat Algorithm for improved Clustering of Convolutional...

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37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Tiacharoen, Somporn King Mongkuts Univ Technol North Bangkok Elect Engn Technol Coll Ind Technol Bangkok Thailand

ISBN: (数字)9781665485593

ISBN: (纸本)9781665485593

This article is expanded the technique for clustering the images from Convolutional Neural Network (CNN) with bat algorithm (BA). Varian images extract the features from CNN for identifting each image data. BA categorize those image data for improved clustering, assist with fuzzy logic systems. Computational result are comparative experiment results between CNN and Fuzzy BA, mean squared error and root mean squared error respectively.

关键词： Convolutional Neural Network bat algorithm Fuzzy Clustering Machine learning

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Multi-user Detection Based on Improved KICA with bat algorithm

Multi-user Detection Based on Improved KICA with Bat Algorit...

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2nd International Conference on Measurement, Instrumentation and Automation (ICMIA 2013)

作者： Liu, Xiaozhi Li, Jing Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783037857519

In this paper, an improved kernel independent component analysis (KICA) algorithm is proposed for multi-user detection (MUD). In this algorithm, a new hybrid kernel function is adopted. In addition, the bat algorithm is applied to the optimizing process of independent component separation. Simulation results show that the new hybrid kernel function performs better in MUD than other kernel functions,and the improved KICA with bat algorithm has the smallest bit error rate(BER) when compared with classical FastICA and KICA algorithms.

关键词： multi-user detection hybrid kernel function bat algorithm improved KICA

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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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A Novel Instantaneous Exploitation Based bat algorithm 24

A Novel Instantaneous Exploitation Based Bat Algorithm

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24th Iranian Conference on Electrical Engineering (ICEE)

作者： Ahmadi, A. H. Nikravesh, S. K. Y. Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran

ISBN: (纸本)9781467387897

The use of meta -heuristic algorithms for solving real world problems increases day by day. bat algorithm is a meta heuristic optimization algorithm based on the echolocation behavior of microbats. bat algorithm has advantage which claimed to provide very quick convergence at a very initial stage by automatic switching from exploration to exploitation. Hereby, algorithm loses exploration capability highly at the following iterations, and it may lead to premature convergence. To cope with this deficiency, this paper proposes a novel version of bat algorithm based on instantaneous exploitation feature. Conducted experiments on ten well-known benchmark test functions have shown that the proposed Instantaneous Exploitation Based bat algorithm can outperform the standard bat algorithm.

关键词： bat algorithm Good combination Levy flight Instantaneous exploitation Worst solution

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Research & Analysis of Advancements In bat algorithm 3

Research & Analysis of Advancements In BAT Algorithm

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

作者： Sharma, Shabnam Luhach, Ashish Kr. Jyoti, Kiran Lovely Profess Univ Phagwara India Guru Nanak Dev Engn Coll Ludhiana Punjab India

ISBN: (纸本)9789380544199

Optimization is defined as finding the alternate solutions to a problem, tinder the given constraints. Maximizing the performance level is one of the objectives of optimization, which can be achieved by satisfying desirable factors and minimizing the undesirable ones. When a problem cannot be solved in polynomial time or consumes long time to solve, then alternative solutions of the problem will be explored and near optimal solution is accepted. To tackle these napes of problems, one of the three approaches are used: Heuristics, Meta-Heuristics and Hyper-Heuristics. bat algorithm is one of the meta-heuristic algorithms, which optimizes the solution using echolocation and applicable in solving various combinatorial problems. In this research work, analysis of various bat variants is done and further research areas are explored, in the field of meta heuristic approaches.

关键词： bat algorithm Exploration Exploitation Heuristic Meta-Heuristic

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Robust design of fractional order PID sliding mode based power system stabilizer in a power system via a new metaheuristic bat algorithm

Robust design of fractional order PID sliding mode based pow...

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International Workshop on Recent Advances in Sliding Modes

作者： Abdelghanil, Choucha Lakhdar, Chaib Salem, Arif Djameleddine, Bougrine Med Lakhdar, Mokrani Amar Telidji Univ Laghouat LACoSERE Lab Dept Elect Engn Laghouat Algeria

ISBN: (纸本)9781479989478

In this paper, Power System Stabilizer (PSS) based on hybrid Sliding Mode Controller (SMC) and fractional order PID controller ((PID mu)-D-lambda) is proposed for optimal control of power system, using a new metaheuristic optimization bat algorithm (BA) inspired by the echolocation behavior to improve power system stability. The aim of this study is to find robust controller for demonstrating the availability of the proposed controller, and to achieve best desired output. Here, we have chosen SMC as one of the most effective control methodologies for adjusting the states of the system to their preferred values to supply the excellent damping under severe disturbances. The problem of SMC-FOPID design is transformed to an optimization problem based on performance index, which is Integral of the Time Weighted Absolute Error (ITAE). Where, BA has been employed to adjust the optimal controller parameters. The effectiveness of SMC-FOPID has been tested on a Single Machine Infinite Bus (SMIB) power system under different cases, including operating. The performance of SMC-FOPID controller in power system is compared with a conventional PSS. The simulation results greatly indicate the validity and effectiveness of the proposed controller, and superior robust performance for improvement power system stability compared with the PSS controller for each case.

关键词： SMC-FOPID PSS SMIB power system bat algorithm

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COEBA: A Coevolutionary bat algorithm for Discrete Evolutionary Multitasking 20th

COEBA: A Coevolutionary Bat Algorithm for Discrete Evolution...

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20th Annual International Conference on Computational Science (ICCS)

作者： Osaba, Eneko Del Ser, Javier Yang, Xin-She IgIesias, Andres Galvez, Akemi Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Spain Univ Basque Country UPV EHU Bilbao 48013 Spain Middlesex Univ London London NW4 4BT England Univ Cantabria Santander 39005 Spain Toho Univ Funabashi Chiba Japan

ISBN: (纸本)9783030867973;9783030504250

Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary bat algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic bat algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary bat algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.

关键词： Transfer Optimization Evolutionary Multitasking bat algorithm Multifactorial Optimization Traveling Salesman Problem

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