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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Khalid, Bushra Sharma, Kapil Delhi Technol Univ Dept Comp Engn Delhi India

ISBN: (纸本)9789380544168

Developingnew software is an easy task but is really very difficult when it comes to change existing software, because the new changes can affect the old functionalities of the software, hence making the software unreliable. It doesn't matter how much testing is done, but the correct result is known once the product is delivered to the customer. Hence, we need to know whether our products are reliable or not before delivering them to customers. This can be achieved by Software Reliability Models. In this paper, bacterial foraging optimization algorithm (BFOA) technique is operated on Tandem Computer data set to estimate parameters of thirteen software reliability growth models based on Least Square Estimation fitting technique. These models were then compared against eleven comparison criteria like BIAS, Variance etc. Then Distance Based Approach (DBA) is applied to rank all these models based on the comparison criteria. Among the thirteen models under study inflection s-shaped model and goel-okumoto model were ranked one and two respectively.

关键词： Software reliability growth models bacterial foraging optimization algorithm Least Square Estimation Distance Based Approach rank

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bacterial foraging optimization algorithm with Quorum sensing Mechanism

Bacterial Foraging Optimization Algorithm with Quorum sensin...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Shen, Hai Zhang, Mo Shenyang Normal Univ Coll Phys Sci & Technol Shenyang Peoples R China

ISBN: (纸本)9783038351153

Quorum sensing is widely distributed in bacteria and make bacteria are similar to complex adaptive systems, with intelligent features such as emerging and non-linear, the ultimate expression of the adaptive to changes in the environment. Based on the phenomenon of bacterial quorum sensing and bacterial foraging optimization algorithm, some new optimization algorithms have been proposed. In this paper, it presents research situations, such as environment-dependent quorum sensing mechanism, quorum sensing mechanism with quantum behavior, cell-to-cell communication, multi-colony communication, density perception mechanism. Areas of future emphasis and direction in development were also pointed out.

关键词： Communication Mechanism Quorum Sensing bacterial foraging optimization algorithm

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Array Synthesis Based on Mutational bacterial foraging optimization algorithm

Array Synthesis Based on Mutational Bacterial Foraging Optim...

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2015 International Conference on Computer Science and Information Engineering(CSIE 2015)

作者： Yang MENG Zuo-lin REN Yu-bo TIAN School of Electronics and Information Jiangsu University of Science and Technology

In order to improve the performance of bacterial foraging optimization(BFO) algorithm,mutational operators are injected into the *** each iteration,the bacterias are selected to mutate at a certain probability,which can overcome the phenomenon that the BFO algorithm may appear premature convergence and slow convergence *** numerical simulation results show that the performance of the BFO algorithm based on the mutational operator is better than that of the basic BFO algorithm in searching accuracy,convergence speed and *** performance of the BFO with wavelet mutation(BFOWM) is the best in all the mutational BFO ***,the BFOWM algorithm is applied to the array synthesis *** synthesis effect is excellent.

关键词： bacterial foraging optimization algorithm Mutational operator Premature convergence Convergence speed Array synthesis

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Selection of Optimal Software Cost Estimation Model Using bacterial foraging optimization algorithm

Selection of Optimal Software Cost Estimation Model Using Ba...

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作者： Neha Mittal DELHI TECHNOLOGICAL UNIVERSITY

学位级别：硕士

The main challenge that the software industry faces today is to estimate the cost required to develop the project in the early phase of software development life cycle. Cost estimation is difficult in the early phase because cost depends on factors like Line of Code, Methodology adopted etc. which cannot be stated accurately in the beginning. Various techniques had already been developed by the researchers which can be categorized into algorithmic and Non- algorithmic methods to determine the cost of the software precisely. In this thesis, bacterial foraging optimization algorithm (BFOA) technique is operated on NASA data set to estimate the cost for the NASA project. BFOA is a one of the soft computing techniques which is tolerant to factors like imprecision, approximation, partial truth, and uncertainty. It has drawn attention of many of the researchers and has been used in various engineering application. BFOA is employed to generate parameters of the COCOMO model and four of its variants. These five models were compared against the comparison criteria like BIAS, MSE, MAE, MEOP, PRR, Variance, RMPSE, RSQ, SSE, TS, ED, PA, SD, MD, MMRE, RMSE and, NRMS. Finally distance based approach (DBA) is used for optimal selection and ranking of COCOMO models. DBA do recognizes the importance of relative importance of the criteria for the given application, without it inter-criterion comparison could not have been accomplished. What all it requires is set of criteria for selection of model, set of model, and their level for the purpose of optimal selection; and finally it successfully rank on position one COCOMO_model1 as the best one with LSE of 651.2720.

关键词： bacterial foraging optimization algorithm Least Mean Square COCOMO Distance Based Approach Optimal

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Adaptive bacterial foraging optimization algorithm with Social foraging Strategy for Multi-modal optimization problem

Adaptive Bacterial Foraging Optimization Algorithm with Soci...

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2014年国际演变算法大会(EVOLVE 2014)

作者： Hai shen Yunlong Zhu College of Physics Science and Technology Shenyang Normal University Laboratory of Information Service and Intelligent Control Shenyang Institute of Automation Chinese Academy of Sciences

In 2002,*** proposed bacterial foraging optimization algorithm(BFOA) for distributed optimization and *** foraging strategies are *** on social and intelligent foraging theory,this paper proposed an adaptive bacterial foraging optimization algorithm,and introduced six foraging operators:chaos run operator,assimilation run operator,tumble operator,swimming operator,reproduction operator and elimination-dispersal *** those operators,chaos run operator,assimilation run operator and reproduction operator were redefined in accordance with social foraging *** others were same with the original *** were conducted on 6 multimodal unconstrained benchmark optimization problems for demonstration the effectiveness and *** results demonstrate remarkable performance of the proposed algorithm on all chosen benchmark functions when compared to several successful optimization techniques.

关键词： bacterial foraging optimization algorithm Social foraging Multimodal Numerical optimization

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Optimal selection of cloud manufacturing resources based on bacteria foraging optimization

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第1-2期37卷 165-182页

作者： Hu, Yanjuan Pan, Leiting Lv, Wenjun Wang, Zhanli Changchun Univ Technol Sch Mechatron Engn Changchun Peoples R China Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

As a service-oriented manufacturing paradigm, cloud manufacturing (CMfg) combines the computing resources the manufacturing resources respectively from the Internet and enterprises into a huge shared resource pool for network-based distribution of manufacturing resources and manufacturing tasks. In order to reduce the number and complexity of resource pools in CMfg, it is necessary to simplify the process, save manufacturing resources, shorten the production time and improve the productivity of enterprises. Therefore, a method for the optimal selection of cloud Manufacturing resources based on bacterial foraging optimization was proposed in this paper. First, a scheme evaluation system based on the evaluation factors of production cost, production time and processing quality was established, and a mathematical model of resource optimization selection was built. Secondly, a combination of Analytic Hierarchy Process (AHP) and entropy weight method was used to obtain the weights of different evaluation indicators, and a bacterial foraging optimization algorithm (BFO) was used to optimize the scheme. Finally, an example study of the method was conducted with the NGW51 reducer as an example. By comparing with existing methods, the experimental results verify the advantages of BFO in CMfg resource optimization selection.

关键词： Cloud manufacturing resource optimization evaluation indicators bacterial foraging optimization algorithm analytic hierarchy process entropy weight method

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An optimization-based Adaptive Sliding Mode Control for an Inverted Pendulum

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IETE JOURNAL OF RESEARCH 2024年第1期70卷 542-557页

作者： Pathak, Rajeev Ranjan Mohanty, Sidhanta Sengupta, Anindita Indian Inst Engn Sci & Technol Dept Elect Engn Howrah 711103 W Bengal India

The work proposes an optimization-based robust adaptive gain sliding mode control technique to handle bounded behavior of uncertainties/perturbations without having the exact knowledge of the bound a priori. The constant of the sliding surface follows dynamical adaptive nature using three optimization techniques, i.e. Harmony Search algorithm, bacterial foraging optimization algorithm and Gradient Search algorithm to extenuate the extent of chattering. The gain of the switching control law, which is a part of the sliding mode control design, is calculated using the gain adaption technique. Lyapunov Stability Criterion is applied to assure the stability of the closed-loop system with adaptable gain and the optimized value of the sliding constant. An inverted Pendulum system has been taken to validate the proposition both in the simulation environment and the hardware platform.

关键词： Adaptive sliding mode control bacterial foraging optimization algorithm Gradient search algorithm Harmony search algorithm Inverted pendulum Sliding mode control

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Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Kateb, Faris Ragab, Mahmoud Abukhodair, Felwa Abdulkader, Omar Ahmed Maghrabi, Louai A. Binyamin, Sami Saeed Al-Hanawi, Mohammed Khaled King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Smart Environm Res Jeddah 21589 Saudi Arabia Arab Open Univ Fac Comp Studies Riyadh Saudi Arabia Univ Business & Technol Coll Engn Dept Software Engn Jeddah Saudi Arabia King Abdulaziz Univ Appl Coll Comp & Informat Technol Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Hlth Serv & Hosp Adm Dept Fac Econ & Adm Jeddah 21589 Saudi Arabia

With an increased chronic disease and an ageing population, remote health monitoring is a substantial method to enhance the care of patients and decrease healthcare expenses. The Internet of Things (IoT) presents a promising solution for remote health monitoring by collecting and analyzing vital data like body temperature, ECG, and heart rate, giving real-time insights to medical professionals. However, maintaining effectual monitoring in environments with bandwidth or energy constraints presents crucial threats. While machine analysis and human insight performance must be content, conveying extra data to gratify both would be evaded for efficient resource application. Therefore, this article proposes an Enhanced Security Mechanism for Human-Centered Systems using Deep Learning with Jellyfish Search Optimizer (ESHCS-DLJSO) approach for IoT healthcare applications. The projected ESHCS-DLJSO approach allows IoT devices in the healthcare field to securely convey medical data and early recognition of health problems in the human-machine interface. To achieve this, the ESHCS-DLJSO approach utilizes a min-max normalization technique to transform the input data into a more suitable format. The bacterial foraging optimization algorithm (BFOA) method is used for feature extraction. Moreover, a convolutional neural network with long short-term memory (CNN-LSTM-Attention) technique is used for disease detection and classification. Finally, the ESHCS-DLJSO technique employs the jellyfish search optimizer (JSO) technique for hyperparameter tuning. The simulation of the ESHCS-DLJSO technique is examined on an IoT healthcare security dataset. The performance validation of the ESHCS-DLJSO technique portrayed a superior accuracy value of 99.43% over existing approaches.

关键词： Internet of things Deep learning Jellyfish search optimizer Human-centered systems Healthcare bacterial foraging optimization algorithm

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FDI Detection Method for Smart Grid Based on IBFO-CNN

FDI Detection Method for Smart Grid Based on IBFO-CNN

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9th International Conference on Signal and Image Processing (ICSIP)

作者： Wang, Hao Zhang, Xiangcong Wang, Zhiying Zhang, Chan Zhang, Feifei Lu, Weizhi Wang, Lei Wang, Bingjie State Grid Henan Informat & Telecommun Co Data Ctr Zhengzhou Peoples R China

ISBN: (纸本)9798350350920

In recent years, false data injection (FDI) poses a serious security threat to the operational status of smart grid. Aiming at the current difficulty of smart grid to detect FDI attacks, this paper proposes a FDI detection method for smart grid based on IBFO-CNN. Firstly, the simulation modeling of FDI is completed based on the state estimation of smart grid. Then the convolutional neural network (CNN) is introduced as the basic FDI detection model, and for the problem of inefficient parameter optimization, the bacterial foraging optimization (BFO) algorithm improved by the multivariate updating strategy is used to realize automatic parameter optimization of the CNN, so as to obtain the FDI detection model of smart grid based on IBFO-CNN. Finally, the designed model is applied to the IEEE 14 bus system to test the model's detection performance for FDI. The experimental results show that the IBFO-CNN model proposed in this paper has high precision for FDI detection in the IEEE 14 bus system, which has a better detection performance and anti-noise performance compared with other models. The proposed method has good performance in engineering practice can effectively guarantee the data security of the smart grid.

关键词： false data injection smart grid convolutional neural network bacterial foraging optimization algorithm multivariate updating strategy

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Meta Heuristic and Nature Inspired Hybrid Approach for Home Energy Management using Flower Pollination algorithm and bacterial foraging optimization Technique 32

Meta Heuristic and Nature Inspired Hybrid Approach for Home ...

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32nd IEEE International Conference on Advanced Information Networking and Applications (AINA)

作者： Awais, Muhammad Javaid, Nadeem Mateen, Abdul Khan, Nasir Mohiuddin, Ali Rehman, Malik Hassan Abdul COMSATS Inst Informat Technol Islamabad 44000 Pakistan

ISBN: (纸本)9781538621950

Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases day by day. This increase in LD is causing serious energy crises to the utility and DS. As the usage of energy increases with the increase in user's demand respectively, the peak is increased in these hours which affect the customer's in term of high-cost prices. This issue is tackled using some schemes and their proper integration. Two-way communication is done by the utility through Smart Grid (SG) between utility and customers. Customers that show some good behavior and helps the utility to control this LD, can perform a key role here. In this paper, our main focus is to control the Customer Side Management (CSM) by reducing the peak generation from on-peak hours. In our scenario, we focus on saving the cost expenditure of users by giving them comfort and shifting the load of appliances from high LD hours to low LD hours. In this study, we adopt the optimization algorithms, like bacterial foraging optimization algorithm (BFOA), Flower Pollination algorithm (FPA) and proposed our Hybrid bacterial Flower Pollination algorithm (HBFPA) to optimize the solution of our problem using the famous electricity scheme named as Critical Peak Pricing(CPP) with three different Operational Time intervals (OTIs). Simulations and results show that our scheme reduces the cost and peak to the average ratio by proper shifting the appliances from highly load demanding hours to the low demanding hours with the negligibly small difference between the maximum and minimum 90% of confidence interval.

关键词： Demand side management bacterial foraging optimization algorithm Flower pollination algorithm Hybrid bacterial flower pollination algorithm Smart grid Home energy management

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