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bat algorithm with triangle-flipping strategy for numerical optimization

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2018年第2期9卷 199-215页

作者： Cai, Xingjuan Wang, Hui Cui, Zhihua Cai, Jianghui Xue, Yu Wang, Lei Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang 330099 Jiangxi Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China

bat algorithm (BA) is a novel population-based evolutionary algorithm inspired by echolocation behavior. Due to its simple concept, BA has been widely applied to various engineering applications. As an optimization approach, the global search characteristics determine the optimization performance and convergence speed. In BA, the global search capability is dominated by the velocity updating. How to update the velocity of bats may seriously affect the performance of BA. In this paper, we propose a triangle-flipping strategy to update the velocity of bats. Three different triangle-flipping strategies with five different designs are introduced. The optimization performance is verified by CEC2013 benchmarks in those designs against the standard BA. Simulation results show that the hybrid triangle-flipping strategy is superior to other algorithms.

关键词： bat algorithm Random triangle-flipping strategy Directing triangle-flipping strategy Hybrid triangle-flipping strategy

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bat algorithm for dam-reservoir operation

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ENVIRONMENTAL EARTH SCIENCES 2018年第13期77卷 510页

作者： Ethteram, Mohammad Mousavi, Sayed-Farhad Karami, Hojat Farzin, Saeed Deo, Ravinesh Othman, Faridah Binti Chau, Kwok-wing Sarkamaryan, Saeed Singh, Vijay P. El-Shafie, Ahmed Semnan Univ Dept Water Engn & Hydraul Struct Fac Civil Engn Semnan Iran Univ Southern Queensland Sch Agr Computat & Environm Sci Inst Agr & Environm Toowoomba Qld Australia Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Chamran Univ Dept Water Engn & Hydraul Struct Fac Civil Engn Ahvaz Iran Texas A&M Univ Dept Biol & Agr Engn Zachry Dept Civil Engn College Stn TX USA Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur Malaysia

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, bat algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.

关键词： Water supply Hydropower generation Optimization models Reservoir operation Rule curve bat algorithm

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Hybrid Binary bat algorithm with Cross-Entropy Method for Feature Selection 4

Hybrid Binary Bat Algorithm with Cross-Entropy Method for Fe...

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4th International Conference on Control and Robotics Engineering (ICCRE)

作者： Li, Guocheng Le, Chengyi West Anhui Univ Sch Finance & Math Luan Peoples R China East China Jiaotong Univ Sch Econ & Managment Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781728115931

Feature selection aims to find an optimal subset from a given set of features. As this task can be seen as a challenging combinatorial optimization problem while the classical optimization techniques have some limitations in solving it. In this paper we propose a novel hybrid metaheuristic, improved Binary bat algorithm with Cross-Entropy method (BBACE), for feature selection. In the proposed BBACE algorithm, the Cross-Entropy method is embedded in bat algorithm to make good balance between exploitation and exploration based on co-evolution. The performance of the proposed method is evaluated on 10 standard benchmark datasets from UCI repository and compared with some well-known wrapper feature selection techniques such as GA, PSO, and ALO. The experimental results demonstrate the efficiency of the proposed approach in selecting the most informative attributes for classification and improving the classification accuracy.

关键词： feature selection hybrid optimization bat algorithm cross-entropy method co-evolution

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Application of bat algorithm on the Design of a Linear Microstrip Antenna Array for Pattern Nulling 12

Application of Bat Algorithm on the Design of a Linear Micro...

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12th International Conference on Advanced Technologies for Communications (ATC)

作者： Luong Xuan Truong Truong Vu Bang Giang Tran Minh Tuan Univ Engn & Technol Hanoi Vietnam Vietnam Natl Univ Hanoi Vietnam MIC Natl Inst Informat & Commun Hanoi Vietnam

ISBN: (纸本)9781728123929

this paper presents an application of bat optimization algorithm on the pattern nulling technique for imposing single null at side lobes. A linear microstrip antenna array has been proposed to deploy the theory from bat algorithm. The proposed array consists of 10 printed vertical antenna elements based on RO4003C substrate with the size of 160 x 450 x 1.524 mm(3). When the array is fed by an ideal source, the simulated radiation pattern meets the requirement in term of null placement. A full design of the array for imposing a single null at the first side lobe has been proposed and investigated. Amplitude excitation weights are divided by a series feeding network. To improve the gain, a back reflector constructed on Fr-4 substrate with the size of 213 x 503 x 1.6 mm(3) has been used. The simulation results show that the first side lobe level can be suppressed to -40 dB at the frequency of 3.5 GHz while the maximum gain is 17.7 dBi. The simulated and measured reflection coefficients indicate that S-1,S-1 is equal -10 dB in the frequency range of 3.26-3.78 GHz. With these characteristics, the proposed antenna array is a good candidate for wireless systems in the C band.

关键词： linear mircostrip antenna array printed antenna bat algorithm nulling pattern side lobe suppression serial feeding network

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Optimal Design of Colpitts Oscillator Using bat algorithm and Artificial Neural Network (BA-ANN) 39th

Optimal Design of Colpitts Oscillator Using Bat Algorithm an...

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39th International Conference on Information Systems Architecture and Technology (ISAT)

作者： Onwuka, E. N. Aliyu, S. Okwori, M. Salihu, B. A. Onumanyi, A. J. Bello-Salau, H. Fed Univ Technol Dept Telecommun Engn Minna Niger State Nigeria

ISBN: (纸本)9783319999968;9783319999951

Oscillators form a very important part of RF circuitry. Several oscillator designs exist among which the Colpitts oscillator have gained widespread application. In designing Colpitts oscillator, different methods have been suggested in the literature. These ranges from intuitive reasoning, mathematical analysis, and algorithmic techniques. In this paper, a new meta-heuristic bat algorithm (BA) is proposed for designing Colpitts oscillator. It involves a combination of BA and Artificial Neural Network (ANN). BA was used for selecting the optimum pair of resistors that will give the maximum Thevenin voltage while ANN was used to determine the transient time of the optimized pairs of resistors. The goal is to select, among the several optimized pairs of resistors, the pair that gives the minimum transient response. The results obtained showed that BA-ANN gave a better transient response when compared to a Genetic algorithm based (GA-ANN) technique and it also consumed less computational time.

关键词： Artificial Neural Network bat algorithm Colpitts oscillator Genetic algorithm RF circuit Transient response

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epibat: Multi-objective bat algorithm for detection of epistatic interactions 15

epiBAT: Multi-objective bat algorithm for detection of epist...

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IEEE 15th International Scientific Conference on Informatics (INFORMATICS)

作者： Sitarcik, Jozef Lucka, Maria Slovak Univ Technol Bratislava Fac Informat & Informat Technol Bratislava Slovakia

ISBN: (纸本)9781728131795

Detection of epistatic interactions associated with diseases can improve prevention and diagnosis of those diseases. Epistatic interactions are nonlinear interaction effects of single nucleotide polymorphisms (SNPs), which are substitution mutations occurring at some specific position in the genome. Detecting associations between them is very computationally expensive, as more complex diseases can be associated only with epistatic interactions of two and more SNPs, thus making a very large quantity of possible SNP combinations needed to test. To cope with such high computational complexity, current methods are also based on bio-inspired algorithms. In this paper we propose epibat, a new algorithm based on bat algorithm with multiple objectives and tabu search. We apply our algorithm on different testing data sets and compare it with other existing methods. The experiments have shown that the new epibat method achieves similar or better results than the compared methods.

关键词： bat algorithm epistatic interactions single nucleotide polymorphisms

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Clustering Social Networks using Nature-inspired bat algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第4期11卷 115-125页

作者： Rani, Seema Mehrotra, Monica Jamia Millia Islamia Dept Comp Sci New Delhi India

The widespread extent of internet availability at low cost impels user activities on social media. As a result, a huge number of networks with a lot of varieties are easily accessible. Community detection is one of the significant tasks to understand the behavior and functionality of such real-world networks. Mathematically, community detection problem has been modeled as an optimization problem and various meta-heuristic approaches have been applied to solve the same. Progressively, many new nature-inspired algorithms have also been explored to handle the diverse optimization problems in the last decade. In this paper, nature-inspired bat algorithm (BA) is adopted and a new variant of Discrete bat algorithm (NVDBA) is recommended to identify the communities from social networks. The recommended scheme does not require the number of communities as a prerequisite. The experiments on a number of real-world networks have been performed to assess the performance of the proposed approach which in turn confirms its validity. The results confirm that the recommended algorithm is competitive with other existing methods and offers promising results for identifying communities in social networks.

关键词： Community detection nature inspired optimization bat algorithm discrete particle swarm optimization social network

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A Performance Enhanced Niching Multi-objective bat algorithm for Multimodal Multi-objective Problems

A Performance Enhanced Niching Multi-objective Bat algorithm...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Yan, L. Li, G. S. Jiao, Y. C. Qu, B. Y. Yue, C. T. Qu, S. K. Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Henan Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China China Mobile Grp Design Inst Co Ltd Beijing 100000 Peoples R China

ISBN: (纸本)9781728121536

A modified multi-objective bat algorithm called Performance Enhanced Niching Multi-objective bat algorithm (PEN-MOBA) is proposed to solve multimodal multi-objective optimization problems. It adopts a dynamic ring topology to form stable niches for maintaining the population diversity, and integrates the stagnation detection strategy to improve the searching ability. The algorithm is compared with a number of state-of-the-art multimodal multi-objective optimizers on twelve multimodal multi-objective test functions. The experimental results verify that the proposed algorithm is effective multimodal multi-objective optimizers and outperforms the existing algorithms on the test functions.

关键词： Evolutionary algorithm multimodal multi-objective problems bat algorithm ring topology

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An Enhanced bat algorithm with Random Walk for Solving Continuous Optimization Problems 20

An Enhanced Bat Algorithm with Random Walk for Solving Conti...

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20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2019)

作者： Tansui, Daranat Thammano, Arit Prince Songkla Univ Fac Humanities & Social Sci Pattani Campus Pattani 94000 Thailand King Mongkuts Inst Technol Ladkrabang Fac Informat Technol Computat Intelligence Lab Bangkok 10520 Thailand

ISBN: (纸本)9781728116518

bat algorithm (BA) has been a successful algorithm for continuous optimization problems. However, its exploitation and exploration powers still leaves something to be desired. This work enhanced BA's exploitation power by introducing two new random walk processes that made its local search more thorough and enhanced it's exploration power by introducing inertia weight to intensify its global search near the end of the optimization process. We called this new algorithm an enhanced BA. The performance of enhanced BA on 15 widely accepted benchmark functions was compared with those of the original BA and genetic algorithm and found to be better than those achieved by the original BA and genetic algorithm on most of those benchmark functions. The directions of our future works are toward applying this enhanced BA to practical, real-world engineering problems and toward hybridizing BA with some other meta-heuristic algorithms.

关键词： bat algorithm Random Walk Inertia Weight Continuous Optimization Problems

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Distributed Text Feature Selection Based On bat algorithm Optimization 10

Distributed Text Feature Selection Based On Bat Algorithm Op...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Chen, Hongwei Hou, Qiao Han, Lin Hu, Thou Ye, Zhiwei Zeng, Jun Yuan, Jiansen Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Peoples R China

ISBN: (纸本)9781728140698

The feature selection effect directly affects the classification accuracy of the text. This paper introduces a new text feature selection method based on bat optimization. This method uses the traditional feature selection method to pre-select the original features, and then uses the bat group algorithm to optimize the pre-selected features in binary code form, and uses the classification accuracy as the individual fitness. However, when the amount of text information is large, the execution time of the single machine is long. According to this shortcoming, combining the bat algorithm and the Spark parallel computing framework, the text feature selection algorithm SbatFS is proposed. The algorithm combines the good search performance of the bat algorithm with the distributed and efficient calculation speed to realize the efficient solution of the text feature selection optimization model. The results show that compared with the traditional feature selection method, after SbatFS is used for feature optimization, the classification accuracy is effectively improved.

关键词： text classification feature selection bat algorithm Spark

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