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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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Design of a Supplementary Controller for Power System Stabilizer Using bacterial foraging optimization algorithm

Design of a Supplementary Controller for Power System Stabil...

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1st International Conference on Computational Intelligence and Information Technology (CIIT 2011)

作者： Kiani, Mohammad Soloklo, Hasan Nasiri Mohammadi, M. Ali Farsangi, Malihe M. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9783642257339

Power system stabilizers (PSSs) are the most efficient device to damp the power system oscillation. Recently designing of PSSs by heuristic algorithms is the conventional method for damping the power system oscillation. This work proposes a bacterial foraging optimization algorithm (BFOA) to determine the PSS parameters to damp the power system inter area oscillation. The results are presented on a 2 area 4 machine system to feasibility of the proposed method. To show the effectiveness of the designed controller, a three phase fault is applied at a bus. The simulation results show that the designed controller by BFOA algorithm performs better than well known genetic algorithm (GA) in damping the power oscillation.

关键词： bacterial foraging optimization algorithm Power System Stabilizer supplementary control Genetic algorithm

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Softmax Regression Based on bacterial foraging optimization algorithm with t-Distribution Parameters 11

Softmax Regression Based on Bacterial Foraging Optimization ...

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11th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Qiao, Xinyi Yang, Zan Hua, Zilin Wang, Yingxuan Nai, Wei Li, Dan Xing, Yidan Tongji Zhejiang Coll Dept Accounting Jiaxing 314051 Zhejiang Peoples R China Tongji Zhejiang Coll Fac Sci Jiaxing 314051 Zhejiang Peoples R China Tongji Zhejiang Coll Dept Jewelry Jiaxing 314051 Zhejiang Peoples R China Tongji Zhejiang Coll Dept Elect & Informat Engn Jiaxing 314051 Zhejiang Peoples R China

ISBN: (纸本)9780738111353

Softmax regression is a supervised multi-class nonlinear classification algorithm in machine learning. Sometimes it is also used in regression problems. It is mainly used in transfer learning, knowledge distillation and meta learning in artificial intelligence. Softmax optimizer is gradient descent method or random gradient descent method, but its loss function is a multi peak, strong nonlinear function, and the optimal solution is only the local optimal solution rather than the global optimal solution, and depends on the gradient. So we use a swarm intelligence optimization algorithm which is independent of gradient and can find the global optimal solution to optimize This article will introduce the bacterial foraging optimization algorithm.

关键词： component softmax regression bacterial foraging optimization algorithm t-distribution

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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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Performance enhancement of SRM using smart bacterial foraging optimization algorithm based speed and current PID controllers

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 95卷 107398-107398页

作者： Manjula, A. Kalaivani, L. Gengaraj, M. Maheswari, R. V. Vimal, S. Kadry, Seifedine Mohamed Sathak Engn Coll Dept EEE Ramanathapuram Tamil Nadu India Natl Engn Coll Dept EEE Kovilpatti Tamil Nadu India Ramco Inst Technol Dept CSE Rajapalayam Tamil Nadu India Noroff Univ Coll Fac Appl Comp & Technol Kristiansand Lebanon

In modern era, the switched reluctance motors (SRM) are gaining attraction due to its inherent features such as robustness, low cost, simple, rugged structure, excellent fault tolerance and temperature withstanding capability. With these specialized advantages, the accurate and efficient energy analysis is still a challenging one since they operated at variable reluctance and oscillating excitation characteristics resulting in nonlinear characteristics. This paper focused on the energy improvement and performance analysis inclusive of smooth control in speed and minimized torque ripples of SRM by introducing the novel Bio-inspired methodology named bacterial foraging optimization algorithm (BFOA) for the selection of optimal parameters of the PID speed and current controllers. By minimizing the torque ripples, it increases the average torque which in turn increases the energy conversion. The performance of the SRM is measured in terms of speed, current, power and efficiency and BFOA efficiency has been verified with genetic algorithm (GA) and conventional PID controller using Euler forward approximation method. This paper discusses the modeling, controlling strategy using BFOA, GA and Euler forward approximation method and a comprehensive analysis of energy using optimized selection of controlling parameters. The performance evaluation objectives in this work, is to minimize Integral Square Error of both current and speed controller and also the reduction of torque ripples. The results obtained in this method are also compared with the Genetic algorithm and Euler forward approximation-based controllers. The proposed algorithm employs individual and social intelligences, which in turn search the responses between the local optima along with global optimums of the problem adaptively. The outmost dynamic response increased average torque and minimized current ripple can be obtained when the parameters of PID controllers are optimized using BFOA.

关键词： Switched Reluctance Motor Torque Minimization Power Equation Speed Control bacterial foraging optimization algorithm Genetic algorithm AI

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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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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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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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SOLVING VEHICLE ROUTING PROBLEM WITH SIMULTANEOUS DELIVERY AND PICK-UP USING AN algorithm BASED ON bacterial foraging optimization

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2013年第2期28卷 373-382页

作者： Hezer, Seda Kara, Yakup Selcuk Univ Endustri Muhendisligi Bolumu Konya Turkey

In Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRP_SDP), each customer has both delivery and pick-up demand simultaneously. VRP_SDP is very difficult combinatorial optimization problem. For this reason, in recent years, it is observed studies focused on metaheuristic methods. In this study, a heuristic solution approach based on bacterial foraging optimization algorithm (BFOA) has been improved and its performance has been evaluated. In the scope of this study VRP_SDP has been solved in order to minimize the total distanced travelled and the results have been tested with the insertion based heuristic that is known in the literature. BFOA obtained good solutions about 24 problems of 40 test problems.

关键词： Vehicle Routing Problem With Simultaneous Delivery And Pick-Up bacterial foraging optimization algorithm

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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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