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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Saravanan, Jegadit S. Nithish, U.S.R. Giri Prasath, R. Balaji, B. Anantha Narayanan, V. Sudhakar, Tata Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India National Institute of Ocean Technology Chennai India

ISBN: (纸本)9798350370249

Accurate sensor data is crucial for the successful operation of Autonomous Underwater Vehicles (AUVs) in various underwater missions. However, achieving precision in sensor data pose significant challenges due to dynamic underwater environments and inherent sensor limitations. This paper investigates the utilization of the Kalman filter, a mathematical model, to enhance sensor data accuracy in AUV navigation. But the filter performance is highly dependent on accurate noise covariance matrices (process noise covariance Q and measurement noise covariance R). In dynamic environments, these noise characteristics can change rapidly and unpredictably, making it difficult to maintain optimal filter performance. Furthermore, it explores the integration of genetic algorithms (GAs) with the Kalman filter to optimize its performance in dynamic conditions. The proposed hybrid approach, termed adaptive genetic algorithm based Kalman Filter (AGAKF), dynamically adjusts filter parameters using GAs, ensuring optimal performance in real-time. The study evaluates the efficacy of the AGAKF method through extensive simulations and comparisons with traditional Kalman filtering coupled with genetic algorithm in MATLAB. Results demonstrate that the AGAKF approach achieves superior noise reduction and preserves signal characteristics more effectively, offering promising potential for enhancing AUV navigation accuracy in diverse underwater environments. © 2024 IEEE.

关键词： adaptive genetic algorithm Autonomous Underwater Vehicle Evolutionary algorithms Kalman Filter Sensor Noise Filtering

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Online Parameter Identification for Fractional Order Model of Lithium Ion Battery via adaptive genetic algorithm 12

Online Parameter Identification for Fractional Order Model o...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Guo, Bin Sun, Huanli Zhao, Ziliang Liu, Yixin Shandong Univ Sci & Technol Coll Transportat Qingdao 266590 Peoples R China China FAW Grp Corp Changchun 130011 Peoples R China

ISBN: (纸本)9798350321050

In order to overcome the shortcomings of the equivalent circuit model and the electrochemical model, a fractional impedance model is established based on the electrochemical impedance spectrum data, and the polarization effect is described in a simple and meaningful way using fractional elements. In this paper, we propose an online parameter identification method for fractional order model (FOM) of lithium ion battery, where an adaptive genetic algorithm is designed to estimation unknown parameters. To this end, an FOM is constructed by using the Grunwald-Letnikov (GL) definition. Then, an unscented kalman filter (UKF) method is adopted to estimate the internal model states. Based on the obtained states, an adaptive genetic algorithm (AGA) is designed to online identify the unknown parameters. Finally, comprehensive experimental verification results are provided to show the effectiveness of the proposed methods.

关键词： Fractional order model adaptive genetic algorithm online parameter identification

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Optimization Performance Analysis for adaptive genetic algorithm with Nonlinear Probabilities

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第6期13卷 361-366页

作者： Sun, Wenjuan Su, Qiaoping Yuan, Hongli Chen, Yan Chaohu Univ Coll Informat Engn Hefei Peoples R China Anhui Xinhua Univ Sch Intelligent Mfg Hefei Peoples R China Anhui Xinhua Univ Sch Big Data & Artificial Intelligence Hefei Peoples R China

genetic algorithm (GA) has been proven to be easy in falling into local optimal value due to its fixed crossover probability and mutation probability, while adaptive genetic algorithm (AGA) has strong global search capability because the two probabilities adjust adaptively. There are two categories of AGA according to the different adjustment methods for crossover and mutation probabilities: probabilistic linear adjustment AGA and probabilistic non-linear adjustment AGA. AGA with linear adjustment of probability values cannot solve the problems of local optimal value and premature convergence. The nonlinear adaptive probability adjustment strategy can avoid premature convergence, poor stability and slow convergence speed. The typical AGA with nonlinear adjustment of probabilities are compared and analyzed through benchmark functions. The optimization performance of typical AGA algorithms is compared and analyzed by 10 benchmark functions. Compared with traditional GA and other AGA algorithms, AGA with crossover and mutation probabilities adjusted nonlinearly at both ends of the average fitness value has higher computational stability and is easy to find the global optimal solution, which provides ideas for the application of adaptive genetic algorithm.

关键词： adaptive genetic algorithm genetic algorithm nonlinear adjustment probability

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adaptive genetic algorithm enabled tailoring of piezoelectric metamaterials for optimal vibration attenuation

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SMART MATERIALS AND STRUCTURES 2022年第7期31卷 075026-075026页

作者： Jian, Yupei Tang, Lihua Hu, Guobiao Wang, Yuesheng Aw, Kean C. Univ Auckland Dept Mech Engn Auckland 1010 New Zealand Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore Tianjin Univ Sch Mech Engn Tianjin 300350 Peoples R China

Piezoelectric metamaterials with shunt resonant circuits have been extensively investigated for their tunability in bandgaps. However, the vibration attenuation ability induced by the electromechanical coupling is generally weaker than that of mechanical metamaterials, limiting their applications in engineering practice. This research presents a non-uniform piezoelectric metamaterial beam with shunt circuit parameters optimized by an adaptive genetic algorithm (AGA) for tailoring the vibration attenuation zone. First, the non-uniform piezoelectric metamaterial beam is modeled for transmittance analysis and verified by the finite element method. By simultaneously tuning the resonance frequencies and the resistance of the shunt circuits, it is conceptually demonstrated that the attenuation zone can be broadened, and the undesired localized vibration modes can be mitigated. Subsequently, two optimization strategies are proposed respectively for two typical vibration scenarios. The inductances and the load resistance in the shunt circuits constitute the set of design variables and are optimized by the AGA. Dedicated case studies are carried out, and the results show that the objective-oriented circuitry parameters can greatly enrich the design freedom, and tailor the transmittance profile according to a given vibration spectra. As compared to the conventional uniform and the graded piezoelectric metamaterial beams, the proposed design provides superior vibration attenuation performance and demonstrates a promising approach for tailoring piezoelectric metamaterials systems.

关键词： piezoelectric metamaterial optimization adaptive genetic algorithm tailored transmittance

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Development of an adaptive genetic algorithm to Optimize the Problem Of Unequal Facility Location

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FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2022年第2期47卷 111-125页

作者： Budi, Hendrik Setia Elveny, Marischa Zhuravlev, Pavel Jalil, Abduladheem Turki Al-Janabi, Samaher Alkaim, Ayad F. Saleh, Marwan Mahmood Shichiyakh, Rustem Adamovich Sutarto Univ Airlangga Fac Dent Med Dept Oral Biol Surabaya 60132 Indonesia Univ Sumatera Utara DS & CI Res Grp Dr Mansur Rd 9 Kampus USU Medan 20155 Indonesia Plekhanov Russian Univ Econ Stremyanny Lane 36 Moscow 117997 Russia Al Mustaqbal Univ Coll Med Labs Tech Dept Babylon 51001 Hilla Iraq Univ Babylon Coll Sci Women Babylon Iraq Univ Anbar Ramadi Iraq Kuban State Agr Univ Dept Management Krasnodar 350044 Krasnodar Regio Russia Univ Pendidikan Mandalika Fac Sci Engn & Appl Math Educ Mataram Indonesia SDG1 Zero Hunger Res Cluster Landmark Univ Nigeri SDG6 Clean Energy Res Cluster Landmark Univ Omu Aran Nigeria

The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.

关键词： Unequal Facility location Interactions adaptive genetic algorithm Mutation Operator Intelligence healthy lifestyle

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Handling dynamic capacitated vehicle routing problems based on adaptive genetic algorithm with elastic strategy

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Li, Jianxia Liu, Ruochen Wang, Ruinan Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

With the development of e -commerce platforms, the logistics industry is also booming. The transportation process is at the core of the logistics industry, which influences the timeliness and rationality of logistics distribution. In the real world, there are many dynamic demands in logistics distribution, so logistics distribution should deal with dynamic demands quickly to realize more effective route planning. This paper proposes an adaptive genetic algorithm (AGA) with elastic strategy (AGA-ES) to solve the dynamic capacitated vehicle routing problems (DCVRPs). Firstly, AGA designs an adaptive local search operator, which can adaptively adjust the quantity of nodes that need local search, according to the fitness value of different individuals. At the same time, AGA develops adaptive crossover and mutation operators, which can automatically adjust the crossover and mutation probability based on the different fitness values. The experimental results verify the better performance of AGA on the traditional capacitated vehicle routing problems (CVRPs). After that, AGA combines elastic strategy (AGA-ES) to solve DCVRPs. This paper mainly concerns three types of actual demand information in DCVRPs: distribution nodes increase, distribution nodes decrease, and distribution road conditions change, according to the actual background. AGA-ES could interact with demand information and judge the types of different demands, and then adaptively plan new and reasonable routes after receiving one or more of the above demand information. The verification of benchmark data sets shows that AGA-ES can effectively deal with DCVRPs. Furthermore, this paper collects the longitude and latitude coordinates of 100 SF express stations in Xi'an, constructs the distribution information of these express stations, and verifies that AGA-ES can solve the DCVRPs under the actual background.

关键词： adaptive genetic algorithm adaptive local search Dynamic demand Dynamic capacitated vehicle routing problems Elastic strategy

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Research on WiFi Indoor Positioning Error Correction Method Based on adaptive genetic algorithm 5th

Research on WiFi Indoor Positioning Error Correction Method ...

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5th EAI International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Zheng, Fa-yue Beijing Polytech Mech & Elect Engn Sch Beijing 100176 Peoples R China

ISBN: (纸本)9783030945510;9783030945503

In view of the shortcomings of traditional mean filtering and Kalman filtering in the process of receiving indoor WiFi positioning signal, a new method of correction of indoor positioning error based on adaptive genetic algorithm is proposed. In order to improve the accuracy of dynamic adaptive genetic signal processing, the algorithm uses adaptive genetic algorithm to modify the singular value of the average distance per hop and the error of measurement distance, and then the modified adaptive genetic value is processed again by weighted filter. The simulation results show that the algorithm has high accuracy, and compared with mean filter and Kalman filter, the algorithm has a great improvement in the influence of average ranging error and communication radius on the error.

关键词： adaptive genetic algorithm Wifi Indoor positioning Error correction

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Automatic mode-locked fiber laser based on adaptive genetic algorithm

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OPTICAL FIBER TECHNOLOGY 2024年 83卷

作者： Han, Dongdong Guo, Ruotong Li, Guojun Chen, Yani Zhang, Boyuan Ren, Kaili Zheng, Yipeng Zhu, Lipeng Li, Tiantian Hui, Zhanqiang Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China China Xian Satellite Control Ctr Xian 710043 Shaanxi Peoples R China

A modified adaptive genetic algorithm (GA) is proposed and implemented in a mode-locked erbium-doped fiber laser (EDFL) based on nonlinear polarization rotation. The algorithm combines GAs with adaptive crossover and mutation rates. As the number of iterations increases during the solving operation, the crossover and mutation rates are dynamically adjusted with the change in the fitness function value. A stretched pulse was automatically generated when the modified adaptive GA was continuously optimized to converge the intracavity polarization state to the target state. The average number of generations required to adjust from other states to stretched pulse locking is two, which is better than that afforded by the general GA. The evolution curves of the mode-locked EDFL with different crossover and mutation rates were investigated, and the results indicate the effectiveness of the proposed modified adaptive GA. This study proposes a new scheme for rapid polarization searching in automatic mode-locked fiber lasers.

关键词： adaptive genetic algorithm Stretched pulse Passively mode-locked fiber laser

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Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第14期80卷 21165-21202页

作者： Denis, R. Madhubala, P. Periyar Univ Dept Comp Sci Salem TN India Don Bosco Coll Dept Comp Sci Dharmapuri TN India

The exponential rise in the development of cloud computing environments in the healthcare field, the protection and confidentiality of the medical records become a primary concern for healthcare services applications. Today, health data stored in the cloud is highly confidential information concealed to avoid unauthorized access to protect the patient's information. As cloud-based medical data transmission becomes more common, it receives growing attention from researchers and academics. Despite the potential for misuse, medical data transmitted through unreliable networks can be manipulated or compromised. The current cryptosystems alone are not sufficient to deal with these issues, and hence this paper introduces a new hybridization of data encryption model to shelter the diagnosis data in medical images. The proposed model is developed by combining either 2D Discrete Wavelet Transform 1 Level (2D-DWT-1 L) or 2D Discrete Wavelet Transform 2 Level (2D-DWT-2 L) steganography with the proposed hybrid encryption scheme. The hybrid encryption scheme is built by strategically applying Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms to secure diagnosis data to be embedded with the RGB channels of medical cover image. One of the key novelties is the use of an adaptive genetic algorithm for Optimal Pixel Adjustment Process (AGA-OPAP) that enriches data hiding ability as well as imperceptibility features. To evaluate the efficiency of the proposed model, numerical tests are performed. The results show that the proposed algorithm is capable of safely transmitting medical data. Comparison of results is carried out concerning the datasets with the state-of-the-art algorithm. In terms of various statistical measures, the results showed the superiority of the proposed algorithm, such as peak signal to noise ratio (PSNR), correlation, structural content (SC), structure similarity (SSIM), entropy, histogram, NPCR, UACI and embedding capacity. The prop

关键词： Encryption Decryption Data hiding Cloud environment Health information exchange adaptive genetic algorithm Steganalysis

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Imaging of intracerebral hemorrhage with adaptive genetic algorithm in brain electrical impedance tomography

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第7期34卷 074002-074002页

作者： Shi, Yanyan Liu, Zhenkun Fu, Feng Wang, Meng Lou, Yajun Zheng, Shuo Henan Normal Univ Coll Elect & Elect Engn Xinxiang 453007 Peoples R China Fourth Mil Med Univ Sch Biomed Engn Xian 710032 Peoples R China

Intracerebral hemorrhage refers to bleeding caused by the spontaneous rupture of blood vessels. Accurate diagnosis of hemorrhage is vital in the treatment of a patient. As a new medical imaging technique, electrical impedance tomography (EIT) is able to offer images of conductivity distribution variation caused by pathological change. However, image reconstruction of EIT suffers from the problem of serious ill-posedness. In particular, in brain imaging, irregular and multi-layered head structure together with the low conductivity of the skull further aggravate the problem. In order to address this problem, a new image-reconstruction method is proposed for imaging of hemorrhage in this work. With current solutions solving by a Tikhonov regularization method for the original conductivity distribution, the proposed method enhances the reconstruction quality by introducing an adaptive genetic algorithm. To test the performance of the proposed method, simulation work is conducted. A three-layer head model is established and an inclusion, which simulates hemorrhage, is placed at six different locations in the brain layer. Images reconstructed by the Tikhonov method, Newton-Raphson method and the traditional genetic algorithm are used for comparisons. Quantitative evaluation is also performed. The anti-noise performance of the proposed method is estimated by considering noise with differing signal-to-noise ratios. In addition to simulation, phantom experiments are carried out to further verify the performance of the proposed method. The results show that the proposed method performs well in the reconstruction of simulated intracerebral hemorrhage. With the proposed method, the inclusion can be more accurately reconstructed and the background is much clearer than the other three traditional methods.

关键词： electrical impedance tomography (EIT) intracerebral hemorrhage adaptive genetic algorithm reconstruction

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