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An Improved bat algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

SN Computer Science 2021年第4期2卷 1-10页

作者： Sahoo, Biswa Mohan Amgoth, Tarachand Department of Computer Science and Engineering Amity University Uttar Pradesh Greater Noida India Indian Institute of Technology (Indian School of Mines) Dhanbad India

Clustering is one of the imperative solutions to the problem of energy unbalancing prevailing in the network. The major issues of wireless sensor network with unequal clustering and multi-hop communication are the energy stability and network longevity. In this paper, we consider the improved bat optimization algorithm to improve the unequal clustering techniques by forming groups before selecting Cluster Heads. In terms of saving energy, knowing the positions of sink nodes in WSNs plays a vital role. bat algorithm (BA), optimization of particle swarm, differential evolution, and whale optimization algorithm is now becoming efficient clustering methods as per the metaheuristic approach. Evaluate the life span of the entire network;this paper proposes an improved BA. The BA’s primary goal of unequal clustering is to reduce energy consumption and extend the lives of the WSNs. An objective function has been formulated to reduce energy consumption and increase the network’s lifespan to achieve these goals. Compared with three existing optimization methods, the experimental results showed that the proposed improved BA achieved better efficiency in reducing total power consumption: whale optimization for topology control, and improved particle swarm optimization algorithm. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： bat algorithm Heterogeneous WSNs Meta-heuristic Network performance Unequal clustering

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Improving Convergence Speed of bat algorithm Using Multiple Pulse Emissions along Multiple Directions

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SENSORS 2022年第23期22卷 9513-9513页

作者： Younas, Waqar Ali, Gauhar Ahmad, Naveed Abbas, Qamar Masood, Muhammad Talha Munir, Asim ElAffendi, Mohammed Int Islamic Univ Islamabad Dept Comp Sci & Software Engn Islamabad 44000 Pakistan Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci & Blockchain Lab Riyadh 11586 Saudi Arabia

Metaheuristic algorithms are effectively used in searching some optical solution space. for optical solution. It is basically the type of local search generalization that can provide useful solutions for issues related to optimization. Several benefits are associated with this type of algorithms due to that such algorithms can be better to solve many issues in an effective way. To provide fast and accurate solutions to huge range of complex issues is one main benefit metaheuristic algorithms. Some metaheuristic algorithms are effectively used to classify the problems and bat algorithm (BA) is one of them is more popular in use to sort out issues related to optimization of theoretical and realistic. Sometimes BA fails to find global optima and gets stuck in local optima because of the absence of investigation and manipulation. We have improved the BA to boost its local searching ability and diminish the premature problem. An improved equation of search with more necessary information through the search is set for the generation of the solution. Test set of benchmark functions are utilized to verify the proposed method's performance. The results of simulation showed that proposed methods are best optimal solution as compare to others.

关键词： bat algorithm pulse emission function optimization levy flights

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A novel resource management framework in a cloud computing environment using hybrid cat swarm bat (HCSbat) algorithm

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DISTRIBUTED AND PARALLEL DATABASES 2023年第1-2期41卷 53-63页

作者： Senthil Kumar, A. M. Padmanaban, K. Velmurugan, A. K. Asha Shiny, X. S. Anguraj, Dinesh Kumar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India Nalla Malla Reddy Engn Coll Dept Comp Sci & Engn Hyderabad Telangana India

Resource management is an important issue in the cloud computing paradigm. The maximum Utilization of resources leads the service providers to get maximum profit. The suitable resources in the cloud data centre are needed to satisfy the user needs. The selection of the right resource is very essential in the data centre. Metaheuristic algorithms are used to perform the task allocation in the cloud computing environment. In this paper, a novel task allocation algorithm is proposed using Cat Swarm Optimization and bat algorithm. bat algorithm helps CSO algorithm to escape from a pre convergence issue. The proposed HGCSbat algorithm results are evaluated and compared with the popular algorithms CSO and bat algorithms. The HGCSbat results are better than Genetic algorithm, Cat Swarm Optimization, and bat algorithms in terms of availability and throughput.

关键词： Cloud computing Resource management Cat Swarm optimization bat algorithm Availability Throughput

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DESIGN OF STATE FEEDBACK CONTROLLER WITH OPTIMAL PARAMETERS USING bat algorithm FOR REACTION WHEEL PENDULUM

DESIGN OF STATE FEEDBACK CONTROLLER WITH OPTIMAL PARAMETERS ...

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International Conference on Advanced Technologies for Communications (ATC)

作者： Chiem Nguyen Xuan Thang Le Tran Le Quy Don Tech Univ Dept Automat & Comp Tech Hanoi Vietnam Controls Automat Prod & Improvement Technol Inst Hanoi Vietnam

ISBN: (纸本)9781665433792

This paper presents a method of designing a positional stability control system for the Reaction Wheel Pendulum. The Reaction Wheel Pendulum is a single-input, multiple-output (SIMO) nonlinear system. The state feedback controller is designed for this system by choosing the distribution of the solutions of the above characteristic polynomial in the solution space. This original distribution is optimized by the bat algorithm through the ITAE objective function. The results of the synthetic control law are proven through simulation results and comparison with other control laws. The effectiveness of the rule with optimized parameters is also shown in the results on the embedded control system.

关键词： Reaction Wheel Pendulum LQR controller SIMO bat algorithm pole-assignment controller ITAE

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Optimization of User Comfort Index for Ambient Intelligence Using Enhanced bat algorithm 8th

Optimization of User Comfort Index for Ambient Intelligence ...

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8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020

作者： Abdul Malek, Mohamad Razwan Ab. Aziz, Nor Azlina Baharudin, Farah Nur Arina Ibrahim, Zuwairie Faculty of Engineering and Technology Multimedia University Melaka Malaysia College of Engineering Universiti Malaysia Pahang Pekan Pahang Malaysia

ISBN: (纸本)9789811648021

Ambient Intelligence (AmI) is an attribute to the environment that can be controlled and monitored by the smart electronic systems that are responsive to the users’ behavior and cater for their needs. AmI is spreading rapidly to many discipline areas and beneficial to the society. Specifically, AmI is achieved through five technology areas which are sensors, networks, artificial intelligence, human computer interfaces and pervasive ubiquitous computing. One of the objective of AmI is to maximize the user comfort index. In this paper three parameters namely;temperature, illumination and indoor air quality are used to determine the user comfort index. Metaheuristics methods such as bat algorithm (BA) are suitable for the maximization of user comfort index. Three variants of enhanced bat algorithm (EBA) are used here to optimized these parameters while balancing occupant’s preference and environmental aspect. The EBA adopted random, linear and nonlinear inertia weight. The findings show that the performance of the EBA is better than the original BA in optimizing the user comfort index. Specifically, the EBA with nonlinear inertia weight provides the best average user comfort index for the set of experimental data. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ambient intelligence bat algorithm User comfort index

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Fetal state health monitoring using novel Enhanced Binary bat algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Sharma, Prerna Sharma, Kapil Delhi Technol Univ Delhi India Delhi Technol Univ Informat Technol Delhi India

The timely assessment of hypoxic fetuses by cardiotocography is of utmost significance as deficiency of oxygen in fetuses' leads to fetal distress which can further prove to be fatal or cause neurological diseases. This paper puts forward an Enhanced Binary bat algorithm, the altered form of Binary bat algorithm for the multi-classification problem of Cardiotocography. Subset of optimal and relevant features is selected using the optimized and Enhanced BBA algorithm from cardiotocography dataset. Features selected by various evolutionary algorithms EBBA (Enhanced Binary bat algorithm), qGWO (quantum Grey Wolf Optimization), Genetic algorithm are 11, 15, 12 simultaneously. EBBA efficiently selects most reduced set of features. The proposed EBBA can be used in feature selection and classification of cardiotocography dataset under different fetus state i.e. normal, suspect and pathologic with an accuracy of 96.21% using machine learning classifier i.e. random forest.

关键词： Cardiotocography Classification Feature selection Evolutionary algorithm bat algorithm Binary bat algorithm Fetal distress

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Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight

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JOURNAL OF BUILDING ENGINEERING 2022年 47卷

作者： Malek, Mohamad Razwan Abdul Ab Aziz, Nor Azlina Alelyani, Salem Mohana, Mohamed Baharudin, Farah Nur Arina Ibrahim, Zuwairie Multimedia Univ Fac Engn & Technol Melaka 75450 Malaysia King Khalid Univ CAI Abha 61421 Saudi Arabia King Khalid Univ Coll Comp Sci Abha 61421 Saudi Arabia Univ Malaysia Pahang Coll Engn Kuantan 26300 Pahang India

Smart home is a concept that aims to maximize the comfort of occupant while consuming energy as low as possible. The comfort and energy consumption are contradicting factors in smart homes. Enhancing comfort often requires considerable energy. On the other hand, minimizing energy may result in less comfort to the residence. Thus, maximizing comfort while minimizing energy consumption is a challenging process. In this paper, bat algorithm (BA) based solution is proposed to tackle this problem. Three main parameters that influence the occupant's comfort, namely, temperature, illumination, and indoor air quality, are considered. The algorithm optimizes towards the best set of values for the three parameters. In this work, exponentially increasing inertia weight is introduced to BA for performance improvement. A secondary dataset consisting of 48 environmental values is optimized using the proposed algorithm. The performance of BA with exponential inertia weight is proven as significantly better than the original BA and other variants of inertia weight;random, linearly decreasing, and nonlinearly decreasing. Moreover, the comfort level achieved by BA with exponential inertia weight is found to be better than previously reported works using firefly algorithm, genetic algorithm, ant colony optimization, and artificial bee colony algorithm. The superior performance is achieved due to better convergence behaviour. Summarily, applying BA with exponential inertia weight is a novel contribution that is essential for providing smart home system a solution that ensures optimum comfort at minimal energy consumption.

关键词： bat algorithm Comfort Inertia weight Energy consumption Optimization

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A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by bat algorithm for Mobile Heterogeneous Wireless Sensor Networks

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IEEE ACCESS 2020年 8卷 16057-16072页

作者： Cao, Li Cai, Yong Yue, Yinggao Cai, Shaotang Hang, Bo Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang 441053 Peoples R China Tianjin Univ Elect Automat & Informat Engn Sch Tianjin 300072 Peoples R China

In order to effectively reduce the redundant information transmission in the network, a data fusion algorithm based on extreme learning machine optimized by bat algorithm for mobile heterogeneous wireless sensor networks is proposed. In this paper, the data fusion process of mobile heterogeneous wireless sensor networks is mainly studied, and regards the nodes of wireless sensor networks as neurons in the neural network of extreme learning machines. The neural network of the extreme learning machine extracts the sensory data collected by mobile heterogeneous wireless sensor network and combines the collected sensor data with the clustering route to greatly reduce the amount of network data sent to the sink node. Aiming at the problem that the extreme learning machine randomly generates the input layer weight and the hidden layer threshold before training, the output result is unstable, affecting the data fusion efficiency and the long delay, a new method of data fusion for mobile heterogeneous wireless sensor networks based on extreme learning machine optimized by bat algorithm is proposed. Simulation experiments are carried out from two aspects: mobile heterogeneous wireless sensor networks and heterogeneous mobile heterogeneous wireless sensor networks. The simulation results show that compared with the traditional SEP algorithm, BP neural network algorithm and ELM algorithm, the proposed bat-ELM-based data fusion algorithm can effectively reduce network traffic, save network energy, improve network work efficiency, and significantly prolong network & x2019;s lifetime.

关键词： Mobile heterogeneous wireless sensor networks data fusion extreme learning machine bat algorithm energy efficient reliability

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A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第7期27卷 7353-7365页

作者： Cui, Yanbin Huang, Chenchen Cui, Yanping North China Elect Power Univ Dept Mech Engn Baoding 071000 Peoples R China North China Elect Power Univ Dept Econ Management Baoding 071000 Peoples R China Univ Victoria Victoria BC Canada

Wind power, a clean and renewable resource, is regarded as one of the most promising and economical resources during the transformation from fossil fuels to new energy resources. Thus, the accuracy of wind speed forecasting work is very important to integrate the wind resource into electrical power system on a large scale. To improve the short-term wind speed forecasting accuracy, a novel compound model is introduced in this paper. For the proposed model, the fast ensemble empirical mode decomposition method was employed to do the data preprocessing. After the data preprocessing, phase space reconstruction was used for choosing each sub-series' input and output vectors for the forecasting model dynamically. Then, the bat algorithm was applied to optimize the connection weights and thresholds of the traditional back propagation neural network. The forecasting results can be obtained through the aggregation of sequential prediction. The performance evaluation of this proposed model indicates that it can capture the nonlinear characteristics of the wind speed signal efficiently. The proposed model shows better performance when being compared with the parallel models.

关键词： Wind speed forecasting Fast ensemble empirical mode decomposition Phase space reconstruction bat algorithm Back propagation neural network

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A novel modified bat algorithm hybridizing by differential evolution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 141卷 112949-000页

作者： Ylidizdan, Gulnur Baykan, Omer Kaan Selcuk Univ Kulu Vocat Sch Kulu Konya Turkey Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn Konya Turkey

The bat algorithm (BA) is one of the metaheuristic algorithms that are used to solve optimization problems. The differential evolution (DE) algorithm is also applied to optimization problems and has successful exploitation ability. In this study, an advanced modified BA (MBA) algorithm was initially proposed by making some modifications to improve the exploration and exploitation abilities of the BA. A hybrid system (MBADE), involving the use of the MBA in conjunction with the DE, was then suggested in order to further improve the exploitation potential and provide superior performance in various test problem clusters. The proposed hybrid system uses a common population, and the algorithm to be applied to the individual is selected on the basis of a probability value, which is calculated in accordance with the performance of the algorithms;thus, the probability of applying a successful algorithm is increased. The performance of the proposed method was tested on functions that have frequently been studied, such as classical benchmark functions, small-scale CEC 2005 benchmark functions, large-scale CEC 2010 benchmark functions, and CEC 2011 real-world problems. The obtained results were compared with the results obtained from the standard BA and other findings in the literature and interpreted by means of statistical tests. The developed hybrid system showed superior performance to the standard BA in all test problem sets and produced more acceptable results when compared to the published data for the existing algorithms. In addition, the contribution of the MBA and DE algorithms to the hybrid system was examined. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Heuristic algorithms bat algorithm Differential evolution algorithm Continuous optimization Large-scale optimization

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