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Enhanced health states recognition for electric rudder system using optimized twin support vector machine

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2025年第1期41卷 274-292页

作者： Guo, Chenxia Qin, Hao Yang, Ruifeng North Univ China Sch Instrument & Elect Taiyuan 030051 Peoples R China Automat Test Equipment & Syst Engn Res Ctr Shanxi Taiyuan Peoples R China Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China

Safety and reliability represent indispensable prerequisites for electric rudder systems (ERS), while health states recognition serves as a potent technology that fortifies and optimizes these essential aspects. To address this problem, we present a health-state recognition muti-class model BAFAO-IPBT-TWSVM for ERS considering several typical operating parameters obtained from intelligent electric rudder system test platform. The twin support vector machine (TWSVM) not only possesses the ability of traditional fault diagnosis methods based on SVM to handle unbalanced data, but also introduces two non-parallel hyperplanes to replace single hyperplane of traditional SVM. Traditional TWSVM simplifies and streamlines the problem-solving, but it is limited to binary classification problem. Therefore, the improved separability principle weighting intra-class distance and inter-class distance generates the best decision tree structure named improved partial binary tree (IPBT) is to effectively decompose multi-classification problem into multiple binary classification problems. A novel intelligent algorithms called bat algorithm-based fruit fly optimization algorithm (BAFOA) is utilized to self-adaptively optimize the parameters of each sub-classifier TWSVMi. This strategic integration makes the model more flexible in adapting to the characteristics of electric rudder system and enhances the accuracy and robustness of the model. The performance of the proposed model is validated under real-world datasets by the results of health states recognition experiments. The Accuracy, Precision, TPR, TNR, F1-score, G-mean, and Kappa of the BAFOA-IPBT-TWSVM are 0.972, 0.987, 0.982, 0.959, 0.985, 0.970, and 0.954 respectively. The reserved BAFOA-IPBT-TWSVM is capable of recognizing the health status with preferable performance compared with other nine models, which could introduce a novel idea for future rudder maintenance approaches.

关键词： Bat algorithm electric rudder system fruit fly optimization algorithm Health states recognition twin support vector machine

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Rationalized fruit fly optimization with sine cosine algorithm: A comprehensive analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 157卷 113486-113486页

作者： Fan, Yi Wang, Pengjun Heidari, Ali Asghar Wang, Mingjing Zhao, Xuehua Chen, Huiling Li, Chengye Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Pulm & Crit Care Med Wenzhou 325000 Peoples R China

The fruit fly optimization algorithm (FOA) is a well-regarded algorithm for searching the global optimal solution by simulating the foraging behavior of fruit flies. However, when solving high dimensional mathematical and practical application problems, FOA is not competitive in convergence speed, and it may quickly fall into the local optimum. Therefore, in this paper, an enhanced fruit fly optimizer, termed SCA_FOA, is developed by introducing the logic of the sine cosine algorithm (SCA). Specifically, in the process of searching for food utilizing the osphresis organ, the individual fruit fly adopts the way inspired by the SCA to fly outward or inward to find the global optimum. A comprehensive set of 28 benchmark functions were used to measure the exploitation and exploration abilities of the proposed SCA_FOA. The results demonstrate that SCA_FOA is superior to other competitive algorithms. Moreover, 10 practical problems from IEEE CEC 2011, three engineering problems, three shifted and asymmetrical functions, and optimization problems of kernel extreme learning machines (KELM) were also solved, effectively. The results and observations indicate that not only the proposed SCA_FOA can be used for simulated problems as a very efficient method, but also it can be employed for real-world applications. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Sine cosine algorithm Global optimization Swarm intelligence Kernel extreme learning machine

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fruit fly algorithm Based on Extremal optimization

Fruit Fly Algorithm Based on Extremal Optimization

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International Conference on Computational Intelligence and Security

作者： Shuiping Zhang Yang Chen Yangdan Geng School of Information Engineering Jiangxi University of Science and Technology Ganzhou Jiangxi China

ISBN: (纸本)9781509048410

Aiming at the problem that solutions obtained by simply using FOA (fruit fly optimization algorithm) would fall into local optimum, with slow convergence and low accuracy, we therefore propose an algorithm EOFOA that combines EO (Extremal optimization algorithm) with FOA. EOFOA brings the basic idea of EO, an algorithm of extremal dynamics, into FOA, so as to prevent local optimum and increase convergence accuracy by taking advantages of changing the initial distribution strategy, improving the diversity of the fruit fly population through randomly replacing the individuals that are not adaptive to the population, and making use of its strong volatility that enables the algorithm to search continuously. Simulation results show that: EOFOA is more accurate than FOA and the convergence is also quickened, which is efficient in avoiding premature convergence.

关键词： Local optimum fruit fly optimization algorithm Extremal optimization algorithm Premature convergence

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A Dynamic Step-wise Tiny Encryption algorithm with fruit fly optimization for Quality of Service improvement in healthcare

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Healthcare Analytics 2023年 3卷 100177-100177页

作者： Sutradhar, Shrabani Karforma, Sunil Bose, Rajesh Roy, Sandip Department of Computational Science Brainware University West Bengal India Department of Computer Science The University of Burdwan West Bengal India

Healthcare highly depends on accessing, processing, analyzing, sharing, and storing electronic data. Cloud computing has evolved in various healthcare fields, such as bioinformatics, health management systems, and telemedicine. The Internet of Medical Things (IoMT), a subset of IoT, is an essential element of a healthcare management system. The expeditious development in IoMT, intelligent medical devices, healthcare applications, and high-speed networks has enabled medical systems to efficiently diagnose, treat, manage diseases, and remotely monitor and improve the Quality of Service (QoS). To achieve this high data, high speed network, longer battery life and reliable connectivity are basic requirements. 5G conformed these needs with its numerous and distinct potential. However, the shortcomings of these medical devices in the IoMT are privacy concerns and direct physical threats. We propose a new algorithm to enhance the QoS of the IoMT devices/systems. Initially, the dataset is collected and the 5G network is operationalized. We propose a Dynamic Step-wise Tiny Encryption algorithm with fruit fly optimization algorithm to encrypt data. Encrypted data is transmitted using the Energy Efficient Routing Protocol. When the user requests to access the data, the data is decrypted. We show that the proposed system has higher QoS attributes and is more effective. Our experimental results show that our proposed solution considerably improves QoS and network performance in terms of lifetime, communication overhead, storage footprint, and power consumption compared to the competing methods. © 2023 The Author(s)

关键词： Dynamic Step-wise Tiny Encryption algorithm Energy Efficient Routing Protocol fruit fly optimization algorithm Internet of Medical Things Quality of Service

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Improved fruit fly algorithm to Solve No-Idle Permutation Flow Shop Scheduling Problem

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PROCESSES 2025年第2期13卷 476-476页

作者： Zeng, Fangchi Cui, Junjia Hunan Univ Sch Mech & Transportat Engn Changsha 410082 Peoples R China

The no-idle permutation flow shop scheduling problem (NIPFSP), as a current hot topic, is widely present in practical production scenarios in industries such as aviation and electronics. However, existing methods may face challenges such as excessive computational time or insufficient solution quality when solving large-scale NIFSSP instances. In this paper, a discrete fruit fly optimization algorithm (DFFO) is proposed for solving the NIPFSP. DFFO consists of three phases, i.e., the smell search phase based on the variable neighborhood, the visual search phase based on the probabilistic model, and the local search phase. In the smell search phase, multiple perturbation operators are constructed to further expand the search range of the solution;in the visual search phase, a probabilistic model is constructed to generate a series of positional sequences using some elite groups, and the concept of shared sequences is adopted to generate new individuals based on the positional sequences and shared sequences. In the local search stage, the optimal individuals are refined with the help of an iterative greedy algorithm, so that the fruit flies are directed to more promising regions. Finally, the test results show that DFFO's performance is at least 28.1% better than other algorithms, which verifies that DFFO is an efficient method to solve NIPFSP.

关键词： no-idle permutation flow shop scheduling probabilistic model local search fruit fly optimization algorithm

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AP Deployment optimization for WLAN: a fruit fly optimization Approach

AP Deployment Optimization for WLAN: a Fruit Fly Optimizatio...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Liu, Peng Meng, Xin Wu, Jianghong Yao, Mengqian Tang, Zhenzhou Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou Peoples R China

ISBN: (纸本)9781728107325

In order to address the existing problems of unreasonable access point (AP) placement, AP redundancy and excess transmit power, a heuristic topology optimization for deploying WLAN within non-uniform deployment area is proposed in this paper. The proposed scheme consists of two steps. Firstly, the location and the transmit power of each AP are jointly optimized by leveraging fruit fly optimization algorithm (FOA). Then, the "lazy" APs are further removed by traversal check to reduce the AP redundancy. Sufficient simulations have been done to evaluate the performance of the proposed FOA-based deployment scheme by comparing with the uniformed deployment scheme. Simulation results indicate that the total transmit power can be significantly reduced by taking advantage of the proposed scheme while guaranteeing the complete coverage of the desired service area. In addition, the coverage of the WLAN can also be considerably improved with given number of AP and total transmit power of APs.

关键词： Wireless local area networks AP deployment optimization fruit fly optimization algorithm Non-uniform deployment area

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Multi-objective fruit fly optimization Based on Cloud Model 13

Multi-objective Fruit Fly Optimization Based on Cloud Model

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13th World Congress on Intelligent Control and Automation (WCICA)

作者： Wu, Lianghong Wang, Hui-Ying Zuo, Cili Wei, Hua-Liang Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9781538673454

A normal cloud model based fruit fly optimization algorithm is extended for multi-objective optimization. The normal cloud model is used to produce new food positions for the fruit flies in the osphresis-based search, and a parameter self-adaptive strategy is proposed to adjust the search space dynamically. The concept of Pareto domination is incorporated into the vision-based search and a cloud model based multi-objective fruit fly optimization algorithm (CMMOFOA) is then developed. To have a good diversity of the member in the elitist archive, a normalized nearest neighbor distance based diversity maintenance strategy is adopted. The OF test set in CEC2009 is used to test the performance of CMMOFOA. Numerical simulation results demonstrate that CMMOFOA can converge to the Pareto fronts of the test problems with good accuracy and distribution, and has better or competitive performance compared with six popular state-of-the-art algorithms.

关键词： fruit fly optimization algorithm Multi-objective optimization Normal cloud model

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Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartA期86卷 105147页

作者： Hao, Shuhui Huang, Changcheng Heidari, Ali Asghar Xu, Zhangze Chen, Huiling Alabdulkreem, Eatedal Elmannai, Hela Wang, Xianchuan Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Wenzhou Med Univ Informat Technol Ctr Wenzhou 325035 Peoples R China

Since the outbreak of COVID-19, it has seriously endangered the health of human beings. Computer automatic segmentation of COVID-19 X-ray images is an important means to assist doctors in rapid and accurate diagnosis. Therefore, this paper proposes a modified FOA (EEFOA) with two optimization strategies added to the original FOA, including elite natural evolution (ENE) and elite random mutation (ERM). To be specific, ENE and ERM can effectively speed up the convergence and deal with the problem of local optima, respectively. The outstanding performance of EEFOA was confirmed by experimental results comparing EEFOA with the original FOA, other FOA variants, and advanced algorithms at CEC2014. After that, EEFOA is implemented for multi-threshold image segmentation (MIS) of COVID-19 X-ray images, where a 2D histogram consisting of the original greyscale image and the non-local means image is used to represent the image information, and Re & PRIME;nyi's entropy is used as the objective function to find the maximum value. The evaluation results of the MIS segmentation experiments show that, whether high or low threshold, EEFOA can achieve higher quality segmentation results and greater robustness than other advanced segmentation methods.

关键词： Meta -heuristic algorithm fruit fly optimization algorithm Multi -threshold image segmentation COVID-19 X-ray images

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Boosted hunting-based fruit fly optimization and advances in real-world problems

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 159卷 113502-113502页

作者： Fan, Yi Wang, Pengjun Heidari, Ali Asghar Wang, Mingjing Zhao, Xuehua Chen, Huiling Li, Chengye Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Med Univ Dept Pulm & Crit Care Med Affiliated Hosp 1 Wenzhou 325000 Peoples R China

fruit fly optimization algorithm (FOA) is a well-established meta-heuristic method with a clear core concept, a straightforward computation, and a code framework that is easy to build. In the case of large scale and multifaceted practical problems, the optimization effect of FOA may be unsatisfactory, and it is prone to stagnation. In this paper, to enrich the exploration and exploitation capability of the classic FOA, an effective whale-inspired hunting strategy is introduced to replace the random search plan of the original FOA, which we named it as WFOA. The proposed WFOA is compared with 9 state-of-the-art FOA's variants on a comprehensive set of 23 benchmark set and 30 IEEE CEC 2014 functions and advanced algorithms on a set of 21 test functions to validate its effectiveness. In addition, the effectiveness of WFOA is also verified on 20 IEEE CEC 2011 benchmark problems for tackling real-world problems. The statistical data shows that developed components effectively expand the exploration and exploitation capacity of the original FOA. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Whale optimization algorithm Swarm intelligence Global optimization

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A bioinformatic variant fruit fly optimizer for tackling optimization problems

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KNOWLEDGE-BASED SYSTEMS 2021年 213卷 106704-106704页

作者： Fan, Yi Wang, Pengjun Mafarja, Majdi Wang, Mingjing Zhao, Xuehua Chen, Huiling Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Birzeit Univ Dept Comp Sci Birzeit Palestine Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China

The fruit fly optimization algorithm (FOA) is a swarm-based algorithm inspired by fruit flies' food search behaviors in nature. The conventional FOA is attracting widespread interests due to its briefness, and simplicity in structure. However, FOA still has some disadvantages presented in the exploration and exploitation abilities when it is used to solve different optimization problems. To optimize these drawbacks, the performance FOA can be improved by employing different operators that can help it explore more promising areas in the search space and in finding better solutions in the local area of the candidate optimal solutions. In this paper, an improved FOA approach (called BSSFOA) that employs (1) bat sonar strategy to strengthen the exploration, (2) hybrid distribution that combined Gaussian distribution with student distribution to enhance the exploitation is proposed. In BSSFOA, the FOA uses the bat sonar strategy to search for the global optima, while the hybrid distribution mechanism is used to search the local area of the global optima in the hope of finding better solutions. To assess the performance of the proposed approach, a comprehensive set of 30 benchmark functions was used with the continuous version of the BSSFOA. Moreover, a discrete version of BSSFOA was proposed as a searching mechanism in the feature selection process, where 17 well-known datasets were used to assess the ability of the BSSFOA to search the best performing features among these datasets. The obtained results reveal the superiority of the BSSFOA in solving both continuous and discrete optimization problems. Therefore, it can be concluded that the employed mechanisms have constructive impacts in mitigating the core problems of FOA. (C) 2020 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Swarm intelligence Global optimization Feature selection

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