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Segmentation and classification of medical big data on brain tumor using bacteria foraging optimization algorithm along with learning automata

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INTERNET TECHNOLOGY LETTERS 2024年第6期7卷

作者： Keerthi, Devarahatty Shivakumar Maithri, Chandrashekhar Dayanidhi, Mohapatra Kavitha, Malali Vishveshwarappa Gowda Babu, Jayachandra Ananda Malnad Coll Engn Hassan Dept Elect & Commun Engn Hassan India Kalpataru Inst Technol Dept Comp Sci & Engn Tiptur India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaadeswaram India Gopalan Coll Engn & Management Dept Elect & Commun Engn Bangalore India Malnad Coll Engn Dept informat Sci & engn Hassan India Malnad Coll Engn Dept informat sci & engn Hassan 573202 India

Detecting the early stage of brain tumors is significant for an effective therapy that can probably minimize the death rate of patients affected from brain tumors. Magnetic resonance imaging is the benchmark standard for diagnosing brain cancers but, it was difficult to split and categorize different kinds of brain tumors due to the delicate arrangement of the brain's anatomy. To overcome these difficulties and provide an effective classification, this research introduced a hybridized optimization technique which enhance the performance of the classifier. The hybridization is done between bacteria foraging optimization algorithm (BFOA) along with Learning Automata (LA), these two techniques improve the search speed and automate the learning capability of Convolutional Neural Network (CNN) classifier. The obtained results show that proposed BFOA LN-CNN classifier better than the existing Deep Convolutional Neural Network (DCNN) based on Improved Harris Hawks optimization (HHO) known as G-HHO and Improved Invasive Bat (IIB)-based Deep Residual network model. The classification accuracy of the proposed method is 99.41% whereas the DCNN-G-HHO and IIB deep residual neural network provides accuracy of 97% and 91.9% respectively.

关键词： bacteria foraging optimization algorithm brain tumor classification learning automata magnetic resonance imaging segmentation

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Study on optimization of bacteria foraging optimization algorithm combined with Roulette Wheel Method 18

Study on Optimization of Bacteria Foraging Optimization Algo...

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2nd International Conference on Business and Information Management (ICBIM)

作者： Sun Chunsheng Xian Univ Sci & Technol Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9781450365451

Among all the intelligent algorithms, bacteria foraging optimization algorithm (BFO) is the most representative one in microorganism imitation area. There are some shortcomings in its prototype. So this study focused on the improvement on BPO based on Roulette Wheel Method in GA. Roulette Wheel Method was brought in to prevent the algorithm from local optima and enhance the accuracy. The results showed that the improved algorithm has effectively raised the chance of finding the global optimal solution.

关键词： bacteria foraging optimization algorithm Roulette Wheel Method Simulation Test Functions

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Reactive power optimization based on improved bacteria foraging optimization algorithm

Reactive power optimization based on improved bacteria forag...

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2nd International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2017)

作者： Ying Aa Yuanjie Gao Hui Sun Jiwei Zhu Lizhi Cao State grid Wuhan donghu new technology development zone power supply company State grid Hubei procurement company

ISBN: (纸本)9781510845008

bacteria foraging optimization algorithm have the disadvantages of falling into optimal local optimum easily, and the speed of convergence become to decline obvious in later optimization, this paper established a mathematical model based on minimize the loss of the power network and maintain a good voltage level,to solve the problem of reactive power optimization for electric power system. Proposed an improved bacteria foraging algorithm, combining the chaotic theory to initialize the basic population of bacteria, so as to improve their global search ability, and adjust the migration operation adaptively, optimizing the efficiency of late convergence, Finally, test on the IEEE-14 node power system, the result improved that the algorithm proposed in this paper has Faster convergence and higher accuracy in the process of the problem of reactive optimization.

关键词： Reactive power optimization bacteria foraging optimization algorithm chaos theory migration operation

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Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations

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JOURNAL OF MANUFACTURING SYSTEMS 2013年第1期32卷 20-31页

作者： Nouri, Hossein Hong, Tang Sai Islamic Azad Univ Fac Engn Sanandaj Branch Sanadaj Iran Univ Putra Malaysia Fac Engn Selagor 43400 Darul Ehsan Malaysia

The cellular manufacturing system (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families on the basis of pertinent similarity measures. The bacteria foraging optimization (BFO) algorithm is a modern evolutionary computation technique derived from the social foraging behavior of Escherichia coli bacteria. Ever since Kevin M. Passino invented the BFO, one of the main challenges has been the employment of the algorithm to problem areas other than those of which the algorithm was proposed. This paper investigates the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem. In addition, for this purpose matrix-based bacteria foraging optimization algorithm traced constraints handling (MBATCH) is developed. In this paper, an attempt is made to solve the cell formation problem while considering cell load variations and a number of exceptional elements. The BFO algorithm is used to create machine cells and part families. The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as K-means clustering, the C-link clustering and genetic algorithm using a well-known performance measure that combined cell load variations and a number of exceptional elements. The results lie in favor of better performance of the proposed algorithm. (C) 2012 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Cellular manufacturing system Cell formation Cell load variations bacteria foraging optimization algorithm

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Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot

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SENSORS 2023年第22期23卷 9277-9277页

作者： Jutharee, Wisanu Kaewkamnerdpong, Boonserm Maneewarn, Thavida King Mongkuts Univ Technol Thonburi Inst Field Robot Bangkok 10140 Thailand King Mongkuts Univ Technol Thonburi Fac Engn Biol Engn Program Human Factors Engn Res Grp Bangkok 10140 Thailand Yannix Co Ltd Bangkok 10150 Thailand Raina Robotech Co Ltd Bangkok 10150 Thailand

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures;there was no statistical difference. However, ABC yielded more reliable reconfigured gestures;there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints;hence, it could be used for joint failure recovery in real applications.

关键词： artificial bee colony algorithm bacteria foraging optimization algorithm bio-inspired computing failure recovery genetic algorithm joint reconfiguration humanoid robots redundant robots

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Self-adaptive bacterial foraging algorithm based on estimation of distribution

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 5595-5607页

作者： Ni, Na Zhu, Yuanguo Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China

bacteria foraging optimization (BFO) algorithm is easy to fall into the local optimal solution and slow in convergence. In this paper, we have come up with a self-adaptive bacterial foraging algorithm based on estimation of distribution to overcome the mentioned shortages. First, in the chemotactic operator, the swimming step size of bacterium is adaptively adjusted by its fitness value and bacteria move in a random direction. Second, the bacteria obtain the probability of replication based on the fitness value. We choose half of the population for replication by the roulette wheel method. Finally, the possibility of elimination-dispersal is adjusted by the fitness value. Selected bacteria are dispersed to the new locations produced by BOX-Muller formula. Compared with some relative heuristic algorithms on finding the optimal value of ten benchmark functions, the proposed algorithm shows higher convergence speed and accuracy.

关键词： bacteria foraging optimization algorithm self-adaptive estimation of distribution benchmark function

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A hybrid GA-BFO algorithm for the profit-maximizing capacitated vehicle routing problem under uncertain paradigm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 8709-8725页

作者： Barma, Partha Sarathi Dutta, Joydeep Mukherjee, Anupam Kar, Samarjit NSHM Knowledge Campus Dept Comp Sci & Engn Durgapur India Natl Inst Technol Durgapur Dept Math Durgapur India

This study designs a new variant of the capacitated vehicle routing problem (CVRP) under a fuzzy environment. In CVRP, several vehicles start their journey from a central depot to provide services to different cities and finally return to the depot. This paper introduces an additional time beyond the service time at each city to fulfill the pre-ordered demands. The need for this excess service time is to provide the services to new customers who are not enlisted at the start of the process. It is a market enhancement step. The proposed model's main objective is to find the maximum time-dependent profit by using the optimum number of vehicles in an appropriate route and spending optimum excess service time in each city. The model considers travel time and travel cost as fuzzy numbers. An expected value model (EVM) is formulated using the credibility approach on fuzzy variables. A hybrid meta-heuristic method combining a genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) is designed to solve the proposed model. The proposed model is explained with the help of some numerical examples. Sensitivity analyses based on different independent parameters of the algorithms are also conducted.

关键词： Capacitated vehicle routing problem profit maximization fuzzy credibility theory hybrid algorithm genetic algorithm bacteria foraging optimization algorithm

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The Application of Improved bacteria foraging algorithm to the optimization of Aviation Equipment Maintenance Scheduling

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2018年第4期25卷 1103-1109页

作者： Cui, Jia Naval Aeronaut Univ Dept 3188 Ermalu Rd Zhifu Dist 264001 Yantai Peoples R China

Taking the aviation equipment scheduled maintenance as a prototype, this paper improves a bionic global random search algorithm -bacteria foraging optimization algorithm to solve the task-scheduling problem. Inspired by gene mutation, the activity of bacteria is dynamically adjusted to make good bacteria more capable of action. In addition, a bacterial quorum sensing mechanism is established, which allows bacteria to guide their swimming routes by using their peer experience and enhance their global search capability. Its application to the engineering practice can optimize the scheduling of the maintenance process. It is of great application value in increasing the aviation equipment maintenance efficiency and the level of command automation. In addition, it can improve the resource utilization ratio to reduce the maintenance support cost.

关键词： bacteria foraging optimization algorithm job scheduling quorum sensing mechanism scheduled maintenance variable step size

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Using bacteria foraging optimization in Gesture Reconfiguration from Joint Failure for Semi-Humanoid Robot 19

Using Bacteria Foraging Optimization in Gesture Reconfigurat...

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19th International Conference on Control, Automation and Systems (ICCAS)

作者： Jutharee, Wisanu Kaewkamnerdpong, Boonserm Polvichai, Jumpol Maneewarn, Thavida King Mongkuts Univ Technol Thonburi Inst Field Robot Bangkok Thailand King Mongkuts Univ Technol Thonburi Fac Engn Biol Engn Program Bangkok Thailand King Mongkuts Univ Technol Thonburi Fac Engn Comp Engn Dept Bangkok Thailand

ISBN: (纸本)9788993215182

Joint failure problem in humanoid robots could often occur in real application. An approach for fault recovery is performing gesture reconfiguration on a redundant manipulator of humanoid robots. In this study, we proposed a gesture reconfiguration method based on bacteria foraging optimization algorithm. After joint failure, we locked the failed joint in place and used the BFOA-based gesture reconfiguration to find a new suitable gesture configuration for the remaining joints. We demonstrated the gesture reconfiguration from joint failure on the Namo robot, which is a semi-humanoid robot for performing emblematic gestures as a receptionist robot. Four emblematic gestures, including the Thai greeting, salute, bye, and side invite gestures, were demonstrated. The experimental results showed that the BFOA-based gesture reconfiguration could generate a suitable gesture configuration for all four gestures;the resulting gestures are close to the original gestures. We investigated further on tuning the number of population size in BFOA. We varied the number of population size as 15, 25, 50, and 100 and found that although the larger population size could result in better gestures than the smaller size, the improvement in gesture similarity were not significant so it may be more efficient to use the smaller size. This could be owing to the performance of bacteria foraging optimization algorithm.

关键词： Joint Failure Fault Recovery Humanoid Robot Redundant Manipulator bacteria foraging optimization algorithm

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A method for virtual machine migration in cloud computing using a collective behavior-based metaheuristics algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第2期32卷 e5441-e5441页

作者： Sha, Jing Ebadi, Abdol Ghaffar Mavaluru, Dinesh Alshehri, Mohmmed Alfarraj, Osama Rajabion, Lila Shandong Univ Sci & Technol Sch Comp Sci & Engn Qingdao 266590 Shandong Peoples R China Islamic Azad Univ Dept Agr Jouybar Branch Jouybar Iran Saudi Elect Univ Coll Comp & Informat Dept Informat Technol Riyadh Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Al Majmaah Saudi Arabia King Saud Univ Community Coll Comp Sci Dept Riyadh Saudi Arabia Univ South Florida Sarasota Manatee Coll Business Sarasota FL USA

Due to the growth of applications and the integration of new customers into the world of computing systems, computing needs to be changed and to become more powerful and flexible than before. Meanwhile, cloud computing is presented as a model beyond a system that is currently capable of answering most request needs. Flexible infrastructure for cloud computing and virtualization technology provide new features to support business activities. Clouds are a very important topic that used secure management tools for storage, security and securing data centers in a flexible manner. One of the important matters in cloud technologies is virtual machine migration (VMM). There are different ways to implement the VMM, but because of the limitation of resources' energy, energy management is also very important and challenging. Due to the NP-hard nature of this problem, this paper presents an energy-aware VMM engine for cloud computing using the discrete bacterial foraging algorithm as a new collective behavior-based metaheuristics algorithm. The CloudSim simulator is employed to investigate the efficiency of this method. The obtained results have shown that the proposed method improves the energy consumption and the migration count.

关键词： bacteria foraging optimization algorithm cloud computing energy consumption migration virtual machine virtualization

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