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Self-adaptive bat algorithm for large scale cloud manufacturing service composition

PEER-TO-PEER NETWORKING AND APPLICATIONS 2018年第5期11卷 1115-1128页

作者： Xu, Bin Qi, Jin Hu, Xiaoxuan Leung, Kwong-Sak Sun, Yanfei Xue, Yu Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Automat Nanjing Jiangsu Peoples R China Chinese Univ Hong Kong Comp Sci & Engn Dept Shatin Hong Kong Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China

In order to cope with the current economic situation and the trend of global manufacturing, Cloud Manufacturing Mode (CMM) is proposed as a new manufacturing model recently. Massive manufacturing capabilities and resources are provided as manufacturing services in CMM. How to select the appropriate services optimally to complete the manufacturing task is the Manufacturing Service Composition (MSC) problem, which is a key factor in the CMM. Since MSC problem is NP hard, solving large scale MSC problems using traditional methods may be highly unsatisfactory. To overcome this shortcoming, this paper investigates the MSC problem firstly. Then, a Self-Adaptive bat algorithm (SABA) is proposed to tackle the MSC problem. In SABA, three different behaviors based on a self-adaptive learning framework, two novel resetting mechanisms including Local and Global resetting are designed respectively to improve the exploration and exploitation abilities of the algorithm for various MSC problems. Finally, the performance of the different flying behaviors and resetting mechanisms of SABA are investigated. The statistical analyses of the experimental results show that the proposed algorithm significantly outperforms PSO, DE and GL25.

关键词： Manufacturing service composition Self-adaptive learning bat algorithm Dual resetting

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Spatial prediction of shallow landslide using bat algorithm optimized machine learning approach: A case study in Lang Son Province, Vietnam

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ADVANCED ENGINEERING INFORMATICS 2019年 42卷 100978-000页

作者： Dieu Tien Bui Nhat-Duc Hoang Hieu Nguyen Xuan-Linh Tran Ton Duc Thang Univ Geog Informat Sci Res Grp Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam Duy Tan Univ Fac Civil Engn Inst Res & Dev P809-03 Quang Trung Danang Vietnam Duy Tan Univ Inst Res & Dev P809-03 Quang Trung Danang Vietnam

This study develops a machine learning method that hybridizes the Least Squares Support Vector Classification (LSSVC) and bat algorithm (BA), named as BA-LSSVC, for spatial prediction of shallow landslide. To construct and verify the hybrid method, a Geographic Information System (GIS) database for the study area of Lang Son province (Vietnam) has been employed. LSSVC is used to separate data samples in the GIS database into two categories of non-landslide (negative class) and landslide (positive class). The BA metaheuristic is employed to assist the LSSVC model selection process by fine-tuning its hyper-parameters: the regularization coefficient and the kernel function parameter. Experimental results point out that the hybrid BA-LSSVC can help to achieve a desired prediction with an accuracy rate of more than 90%. The performance of BA-LSSVC is also better than those of benchmark methods, including the Convolutional Neural Network, Relevance Vector Machine, Artificial Neural Network, and Logistic Regression. Hence, the newly developed model is a capable tool to assist local authority in landslide hazard mitigation and management.

关键词： Shallow landslide Least Squares Support Vector Classification bat algorithm GIS Susceptibility map

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Projection pursuit model of vehicle emission on air pollution at intersections based on the improved bat algorithm

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Journal of Southeast University(English Edition) 2019年第3期35卷 389-392页

作者： Hu Qizhou Deng Wei Tan Minjia Bian Lishuang School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Transportation Southeast UniversityNanjing 210096China

The projection pursuit model is used to study the assessment of air pollution caused by vehicle emissions at intersections. Based on the analysis of the characteristics and regularities of vehicle emissions at intersections, a vehicle emission model based on projection pursuit is established, and the bat algorithm is used to solve the optimization function. The research results show that the projection pursuit model can not only measure the air pollution of vehicle emissions at intersections, but also effectively evaluate the level of vehicle exhaust emissions at intersections. Taking the air pollution caused by vehicle emissions at intersections as the research object and considering the influence factors of vehicle emissions on air pollution comprehensively, the evaluation index system of vehicle emissions at intersections on air pollution is constructed. Based on large data analysis, a prediction model of air pollution caused by vehicle emissions at intersections is constructed, and an improved bat algorithm is used to realize the assessment process. The application results show that the prediction model of vehicle emissions at intersections can define the degree of air pollution caused by vehicle emissions, and it has good guiding significance and practical value for solving the problem of air pollution caused by vehicle emissions.

关键词： intersection vehicle emission pollutants projection pursuit bat algorithm

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A novel adaptive neural network integral sliding-mode control of a biped robot using bat algorithm

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JOURNAL OF VIBRATION AND CONTROL 2018年第10期24卷 2045-2060页

作者： Rahmani, Mehran Ghanbari, Ahmad Ettefagh, Mir Mohammad Univ Tabriz Dept Mech Engn Tabriz Iran Univ Tabriz Sch Engn Emerging Technol Mechatron Lab Tabriz Iran

This paper proposes a new adaptive neural network integral sliding-mode controller using a bat algorithm (BA-ANNISMC) to control a biped robot. The conventional integral sliding-mode controller (ISMC) is discontinuous in nature due to the combination of nominal control and discontinuous feedback control. The phenomenon of chattering occurs when there is a discontinuity in feedback control. An adaptive neural network is applied to estimate the unknown disturbances to the system. Therefore, by using an adaptive neural network, the chattering phenomena will be eliminated. The proposed controller parameters are tuned using a bat algorithm. The stability of the adaptive neural network integral sliding-mode controller (ANNISMC) is proved by Lyapunov theory. In order to show the effectiveness of the proposed controller, its performance is compared with three other controllers such as a conventional sliding-mode controller (SMC), ISMC and ANNISMC. The results of the numerical simulation clearly indicate the effectiveness of the BA-ANNISMC controller when considering chattering reduction.

关键词： Biped robot integral sliding-mode controller adaptive neural network chattering bat algorithm

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Exploring fractional order 2-D Helmholtz equation using finite difference scheme through the bat optimization algorithm

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2021年第0期

作者： Khan, Najeeb Alam Ahmed, Samreen Razzaq, Oyoon Abdul Kamil Mahmood, Ahmad Univ Karachi Dept Math Karachi 75270 Pakistan Bahria Univ Dept Humanities & Social Sci Karachi Pakistan Univ Teknol PETRONAS Dept Comp & Informat Sci High Performance Cloud Comp Ctr Seri Iskandar Perak Malaysia

This endeavor proposes an effective implementation of a hybrid technique for computing the approximate solution of fractional order Helmholtz equation, with Dirichlet boundary conditions. The novel scheme is an amalgamation of the traditional finite difference method with the bat optimization algorithm (BOA). This nature-inspired optimization technique simulates the echolocation behavior of foraging bats, which ascertain the surroundings through the echo of their emitted sound pulse. Systematically, the deliberated fractional order system is altered into an integer order partial differential equation by virtue of linearized expansion of Laplace transformation. Subsequently, the attained system is processed through the proposed innovative finite difference optimization technique (FDOT) the numerical discussions. Furthermore, some experiments are carried out in order to expound the effective execution and application of the technique. In addition, the convergence and accuracy of the scheme are also delineated numerically, via statistical inference. The significant outcomes of the analysis reveal the advantageousness of the proposed numerical scheme, which can efficiently handle the complexities of the fractional order 2-D Helmholtz equation.

关键词： bat algorithm Caputo derivative finite difference method Helmholtz equation Laplace transforms

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Design of Fuzzy Controller rule base using bat algorithm

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Energy Procedia 2019年 162卷 241-250页

作者： Nesrine Talbi Department of Electronic LAJ Research Laboratory Faculty of sciences and Technology Jijel University Jijel ALGERIA

The work in this paper revolves fundamentally around the main axes of fuzzy control of the type Takagi-Sugeno (T-S) zero order for dynamic, complex nonlinear systems. In this paper, we present method for designing Fuzzy controller rule base using a new swarm intelligence algorithm, which is based on the bat algorithm. The bat algorithm is one of the most recent swarm intelligence based algorithms that simulates the intelligent hunting behavior of the bats found in nature. The main objective is to design the fuzzy rule base of fuzzy controller respecting the desired performance. To demonstrate the efficiency of the suggested approach, a control of a Magnetic Ball Suspension System is selected. Simulation results shows that the proposed approach could be employed as a simple and effective optimization method for achieving optimum determination of fuzzy rule base parameters.

关键词： Fuzzy rule base bat algorithm Fuzzy controller Magnetic Ball Suspension System

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The improved elite multi-parent hybrid optimization algorithm based on bat algorithm to optimum design of automobile gearbox

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JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY 2018年第2期21卷 513-517页

作者： Wang, Chao Hunan Univ Arts & Sci Coll Mech Engn Changde 415000 Peoples R China

bat algorithm is a new intelligent optimization algorithm that is simple and easy to implement. But bat algorithm is easy to fall into local optimum and will appear premature convergence to lead to poor convergence precision. The elite multi-parent hybrid optimization algorithm is better than other optimization algorithms when solving complex function optimization problems. However, the algorithm consists of hybrid operation without mutation so as not to keep the diversity of population in the search process. Combining bat algorithm with elite multi-parent evolutionary optimization algorithm, the improved elite multi-parent hybrid optimization algorithm optimizing hybrid discrete variables was proposed. In this algorithm, firstly the rough optimization is carried out by bat algorithm, and then the accurate optimization is implemented by the elite multi-parent hybrid optimization algorithm. 'This kind of algorithm takes advantage of two algorithms and overcomes their shortcomings. The procedure as DIEMPCOA1.0 is to optimum design for three-shaft four-speed automobile gearbox with 20 design variables, 50 inequality constraints and eight equations. Optimization example shows that this algorithm has characteristics of no special requirements for the optimization design problems, better universality, reliable operation, higher calculation efficiency and stronger global convergence ability, so as to shorten the design cycle, reduce quality, reduce cost and improve quality.

关键词： bat algorithm elite multi-parent evolutionary optimization algorithm gearbox optimization elite-preservation hybrid discrete variable

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Dynamic frequency based parallel k-bat algorithm for massive data clustering (DFBPKBA)

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2018年第4期9卷 866-874页

作者： Tripathi, Ashish Kumar Sharma, Kapil Bala, Manju Delhi Technol Univ Delhi India IP Coll Women Delhi India

In the past one decade there has been significant increase in the growth of digital data. Therefore, good data mining techniques are important for the better decision making. Clustering is one of the key element in the field of data mining. K-means is a very popular algorithm present in the literature which is widely used for the clustering purpose. However k-means algorithm suffers from the problem of stucking into local optimum solution because of it's dependency on the random initialization of initial cluster center. In this paper a novel variant of bat algorithm based on dynamic frequency is introduced. Further the proposed variant is hybridized with K-means to present a new approach for clustering in distributed environment. Since evolutionary computation is very computation intensive, traditional sequential algorithms are not able to provide satisfactory results within the reasonable amount of time for the large scale data problems. To mitigate this problem the proposed variant is parallelized using the MapReduce model in the Hadoop framework. The experimental results show that the proposed algorithm has outperformed K-means, PSO and bat algorithm on eighty percent of the benchmark datasets in terms of intra-cluster distance. Further DBPKBA has also achieved significant speedup for dealing with massive datasets with increase in the number of nodes.

关键词： bat algorithm Hadoop MapReduce Large data sets DFBPKBA

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The Stock Index Prediction Based on SVR Model with bat Optimization algorithm

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algorithmS 2021年第10期14卷 299页

作者： Zheng, Jianguo Wang, Yilin Li, Shihan Chen, Hancong Donghua Univ Glorious Sun Sch Business & Management Shanghai 200051 Peoples R China Fudan Univ Sch Econ Shanghai 200433 Peoples R China

Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China's capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

关键词： bat algorithm support vector regression (SVR) stock index prediction financial market optimal strategy

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Feature Extraction of Remote Sensing Images Based on bat algorithm and Normalized Chromatic Aberration

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IFAC-PapersOnLine 2019年第24期52卷 318-323页

作者： Yi Cao Yuting Xun Yu Han Jian Chen Shubo Wang Zichao Zhang Nannan Du Hao Meng College of Engineering China Agricultural University Beijing 100083 China College of Water Resources & Civil Engineering China Agricultural University Beijing 100083 China Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment Beijing China

These accurate extraction of specific objects in remote sensing images has become a research hotspot. For remote sensing image feature extraction, shape, color and other features can be selected to extract objects from complex scenes. In this paper, a method of remote sensing image feature extraction based on bat algorithm and normalized chromatic aberration is proposed. Firstly, the contrast of remote sensing images is enhanced by using bat algorithm. After enhancement, it can be seen from the histogram that the optimized images contrast is significantly enhanced compared with the traditional histogram equalization. Then, the normalized chromatic aberration method is adopted to extract features. The normalized chromatic aberration is calculated by normalizing the RGB three-channel component and compared with the fixed threshold. Finally, the feature binary graphs are obtained, and then the region of interest (ROI) in the remote sensing image is extracted. The algorithm proposed in this paper can realize remote telematics sensing images processing and obtain complete and accurate target areas. The highest extraction rate was reached 96%.

关键词： remote sensing image bat algorithm normalized chromatic aberration feature extraction

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