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Parameter Identification of Permanent Magnet Synchronous Motor Based on LSOSMO algorithm

SENSORS 2025年第9期25卷 2648-2648页

作者： Zhang, Songcan Zhou, Zhuangzhuang Pu, Yi Li, Yan Xu, Yingxi Henan Univ Sci & Technol Coll Informat Engn Luoyang 471000 Peoples R China CAMA Luoyang Measurements &Controls Co Ltd Luoyang 471000 Peoples R China

The exact identification of the parameters of Permanent Magnet Synchronous Motors (PMSMs) is extremely significant to reach servo system's excellent performance control. So as to solve the problems of slow PMSM parameter identification using the spider monkey algorithm, and easily falling into local optimal and having unstable identification results;the LSOSMO algorithm is put forward in this article, which combines logistic-sine chaotic mapping strategy, dynamic probability adaptive t-distribution method, and an opposition-based learning strategy to determine PMSMs' electric parameters (stator resistance Rs, dq-axis inductance Ld, Lq, and flux linkage psi f). First, the logistic sinusoidal chaotic mapping strategy was used to enhance the uniformity of the initial population of the spider monkey optimization (SMO) algorithm. Then, in the local leader stage and the local leader decision stage of the SMO, the dynamic probability adaptive T-distribution method and opposition-based learning strategy are used to replace the greedy selection strategy, increase the position disturbance, and balance the global search and local search ability of the algorithm, so as to improve the performance and convergence speed of the algorithm. The simulation results prove that, compared to the other five algorithms' identification results, the four parameters that are identified by the LSOSMO algorithm exhibit higher stability and accuracy, with errors that are relative to the true values remaining below 1.1%. The effectiveness and reliability of the identification algorithm is further verified by this.

关键词： PMSMs parameter identification spider monkey optimization algorithm chaotic mapping adaptive t-distribution method opposition-based learning strategy

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Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

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Computers, Materials & Continua 2022年第11期73卷 4179-4192页

作者： R.Uma Mageswari Sara A.Althubiti Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Vardhaman College of Engineering(Autonomous)HyderabadTelangana501218India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended *** the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target *** existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia *** energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network *** this motivation,the current study develops an Enhanced spider monkey optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for *** proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum *** proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance *** simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

关键词： Wireless multimedia sensor networks clustering spider monkey optimization algorithm energy efficiency metaheuristics quantum computing

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ESKEA: Enhanced Symmetric Key Encryption algorithm Based Secure Data Storage in Cloud Networks with Data Deduplication

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期117卷 3309-3325页

作者： Ebinazer, Silambarasan Elkana Savarimuthu, Nickolas Bhanu, S. Mary Saira Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamil Nadu India

Data deduplication approach is utilized in cloud storage to decrease the bandwidth of communication and storage space by eliminating the data copies from the cloud service provider (CSP). However, one of the main problems of cloud storage is data deduplication with secure data storage. To overcome this issue, the researchers presented symmetric data storage methods based on an encryption algorithm. Nonetheless, the Enhanced Symmetric Key Encryption algorithm (ESKEA) based on secure data storage with data deduplication is proposed in this research to further improve data confidentiality. In this approach, the block-level deduplication of data is performed using the Convergent Encryption (CE) algorithm to check the CSP duplicate copies of data. Then, ESKEA algorithm is presented for secure storage of data. In ESKEA, spider monkey optimization algorithm (SMOA) optimally selects the secret key. The results of this work showed that the execution of the proposed ESKEA is more effective than that of the SKEA in terms of upload time and download time.

关键词： Cloud storage Data deduplication Symmetric encryption algorithm Convergent encryption spider monkey optimization algorithm

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Enhanced AlexNet for Detecting the Myocardial Infarction: An Efficient Approach

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第0期

作者： Bulbule, Shamal Soma, Shridevi Gokaraju Rangaraju Inst Engn & Technol Dept Comp Sci & Engn Hyderabad 500090 Telangana India Poojya Doddappa Appa Coll Engn Dept Comp Sci & Engn Kalaburagi 585102 Karnataka India

Heart muscle damage is a result of myocardial infarction (MI), which is caused by inadequate blood supply. Around the world, MI is the leading cause of death for middle-aged and older people. To reduce the risk of MI, early detection is important. This detection is obtained by using a deep learning algorithm. In the literature, few methods are reviewed which does not provide optimal results for detection. Hence, in this paper, the Enhanced AlexNet is developed for an effective diagnosis (ED) to identify MI signals (EAlexNet). To train AlexNet and obtain the best results, a hybrid spider monkey optimization (SMO) and salp swarm optimization (SSO) algorithm is used. Four phases are taken into consideration in the paper to find the MI signals. The input dataset is used to construct the echo frames, and the formed frames are then trained using the EAlexNet. Then, using an adaptive algorithm called a support vector machine (SVM) with kernel function, the process of feature extraction is carried out. Finally, the proposed approach is used to complete the MI classification process. The normal (non-MI) and abnormal (MI) cases are identified from the proposed model. The HMC-QU dataset is taken into account for analysis purposes, and the effectiveness of the suggested strategy is assessed. The suggested approach is contrasted with the current approaches, including ResNet, MobileNet, and VGGNet, respectively. The suggested method is put into practise using the MATLAB platform, and the accuracy, sensitivity, precision, and specificity performance analysis is examined.

关键词： Enhanced AlexNet spider monkey optimization algorithm ResNet MobileNet MI and non-MI

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Hybrid optimization enabled trust-based secure routing with deep learning-based attack detection in VANET

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AD HOC NETWORKS 2022年 136卷

作者： Kaur, Gurjot Kakkar, Deepti Dr B R Ambedkar Natl Inst Technol Dept Elect & Commun Engn Jalandhar India

Vehicular ad-hoc network (VANET) model is easy to arrange and inexpensive answer for clever traffic control and traffic failure protective measures. Generally, the nodes present in VANET utilize broadcast protocols for effectively transmitting safety information, although nodes do not perform based on routing protocols. The misbehavior can produce through targeted attack in which aggressive vehicle can purposely send malevolent packets to damage. Moreover, due to the reason of dynamic behavior of nodes in VANET plus routing difficulties, unexpected misconduct also occurs owing to the software or hardware failures in vehicle. The challenges faced by existing techniques are provided here. VANET becomes a most significant concern to enhance the expediency, mobility, and security application in ITS. Precautions are further tough owing to its rapid mobility, wireless communication and large coverage of nodes. Also, the computational time required for executing independent operations are conspicuously maximum when, the number of vehicles is maximized. In order to overcome these challenges, in this paper, hybrid optimization-based Deep Maxout Network (DMN) is developed for attack classification in VANET. The Cluster Head (CH) selection and routing process is performed using designed hybrid optimization algorithm. The feature selection process is more significant in order to perform effective classifi-cation process. In addition, the attack classification is carried out using DMN and it is taught by introduced optimization algorithm. The developed optimization-based DMN model obtained better classification perfor-mance with precision and recall of 0.9395, and 0.9462 as well as routing performance with energy and trust of 0.2454J, and 0.4402.

关键词： Attack detection Congruence coefficient Deep maxout network Aquila optimizer spider monkey optimization algorithm

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spider monkey optimization: a survey

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2018年第4期9卷 929-941页

作者： Agrawal, Vani Rastogi, Ratika Tiwari, D. C. Jiwaji Univ Sch Math & Comp Sci Gwalior India Jiwaji Univ Sch Math & Allied Sci Gwalior India Jiwaji Univ Sch Phys Gwalior India

algorithms inspired by the intelligent social behavior of simple agents have become popular among the researchers in the recent years. These algorithms are able to find the solution of those real-world optimization problems, which otherwise cannot be solved easily by deterministic techniques. spider monkey optimization (SMO) is one such algorithm which is inspired by the intelligent behavior of spider monkeys. SMO and its variants have been successful and effective in dealing with complex real world optimization problems due to its high efficacy. This paper presents an intense review of SMO, its variants, applications and relative performance with other algorithms .

关键词： Swarm intelligence spider monkey optimization algorithm Population-based algorithm Fission-fusion social structure

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Resolution of the manufacturing cell formation problem using the spider monkey Metaheuristic 13

Resolution of the manufacturing cell formation problem using...

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13th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Soto, Ricardo Crawford, Broderick Castillo, Samara Olivares, Rodrigo Pontificia Univ Catolica Valparaiso Escuela Ingn Informat Valparaiso Chile Univ Valparaiso Escuela Ingn Civil Informat Valparaiso Chile

ISBN: (纸本)9789899843486

The Manufacturing Cell Design Problem (MCDP) consists on the grouping of machines, from a production plant, into independent manufacturing cells. The idea is that each cell processes families of parts, thus reducing movements of the parts among cells. The main reason to perform this procedure is to reduce cost and time in the production of parts. To solve this problem, we use the spider monkey optimization metaheuristic (SMO), an algorithm based on nature that has a fission-fusion social structure. This has resolved 90 instances of the problem of manufacturing cells, taken from the experiments of Boctor. The results obtained have shown that the SMO algorithm is able to successfully reach all the optimal values.

关键词： spider monkey optimization algorithm Metaheuristic optimization Manufacturing Cell Design Problem

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Simulation of QoS Parameters in Cognitive Radio System Using SMO algorithm

Simulation of QoS Parameters in Cognitive Radio System Using...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Rana, Vibhuti Mundra, P. S. Chandigarh Engn Coll Elect & Commun Engn Landran Mohali India

ISBN: (纸本)9781509052974

Cognitive radio (CR) is the current growing technology in wireless communication field and has increase the ability to use the frequency spectrum more properly. The main objective of cognitive radios is to sense the surrounding and use primary user's vacant spaces and allot them to the secondary users without interference each other. This paper presents the optimal solution and optimizes the Quality of services (QoS) parameters to minimum i.e. smaller as compare other solutions which are induced by different optimization techniques Simulated Annealing (SA) and Genetic algorithm (GA). The proposed algorithm called spider monkey optimization algorithm (SMO). SMO is a swarm intelligence technique which works on foraging behaviour of spider monkeys. SMO is tentative and lapse based synergetic iterative process. spider monkeys have been categorized as fusion-fission social structure (FHSS) based animals and they divides into smaller subgroups and search food. The proposed algorithm has been used to optimize the performance of QoS parameters in terms of minimum power consumption, minimum bit error rate (BER), maximum throughput, minimum interference and maximum spectral efficiency. The simulation results shows that the fitness scores obtained by the proposed algorithm i.e. SMO are better (smaller) than SA and GA algorithm in the optimization of QoS parameters of cognitive radio system. The proposed algorithm is better (smaller) than the existing algorithm.

关键词： Cognitive Radio Fusion-Fission Social Structure Genetic algorithm Quality of Services spider monkey optimization algorithm Simulated Annealing

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Fractional Calculus Based PID Controller Tuned by SMO for LFC Application

Fractional Calculus Based PID Controller Tuned by SMO for LF...

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IEEE India International Conference on Power Electronics

作者： Priyambada Satapathy Manoj Kumar Debnath Nimai Charan Patel Sabita Tripathy Department of Electrical Engineering Siksha ‘O’ Anusandhan University Odisha India Department of Electrical Engineering Siksha ‘O’ Anusandhan University Bhubaneswar Odisha India Department of Electrical Engineering Government College of Engineering Keonjhar Odisha India

ISBN: (纸本)9781538649978;9781538649961

In this investigation, to regulate the frequency a hybrid model having two area (thermal-hydro-wind & thermal-hydro-diesel) along with fractional order proportional integral derivative (FOPID) controller under MATLAB environment has been modelled. Further, the gain parameters of the offered controller are tuned with the help of recently introduced spider monkey optimization technique (SMO) by iterative method with ITAE (integral time absolute error) as the cost/objective function. At first, the modelled system is verified with the presence of AC tie-line. Further sensitivity comparative analysis has been done by amplifying the loading of the system by 15% with SMO tuned classical PID controller and offered FOPID controller. Finally, investigation of vigorous responses reveals that SMO tuned FOPID controller yield minimum oscillations on the basis of overshoot and undershoot as well as least settling time as compared to PID controller under above-mentioned circumstances.

关键词： Fractional order PID controller spider monkey optimization algorithm Load frequency control Fractional calculus Multi-generation system

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