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Focusing through disturbed multimode optical fiber based on self-adaptive genetic algorithm

OPTIK 2022年 261卷

作者： Yu, Hua Yao, Zheyi Sui, Xiubao Gu, Guohua Chen, Qian Nanjing Univ Sci & Technol Sch Elect Engn & Optoelect Technol Nanjing 210094 Peoples R China

In terms of the imaging resolution and miniaturization, multimode fiber outperforms single-mode fiber bundles. However, fiber disturbance has always been a challenge to multimode fiber imaging. The great majority of existing algorithms are incapable of dealing with abrupt and strong perturbations from outside. In this paper, a self-adaptive genetic algorithm is proposed. When the environment changes rapidly, additional new elements can be introduced, preventing the optimization from being trapped in a local optimum. The effectiveness of the algorithm is verified by simulation. The experimental results indicate that it enables the speckle pattern to refocus better when the fiber is perturbed. The algorithm can also be used to transmit images. Our research will facilitate the advancement of detection technology using multimode optical fiber in the industrial and medical domains.

关键词： Multimode fiber Scattering Wavefront shaping self-adaptive genetic algorithm Focusing Multimode fiber Scattering Wavefront shaping self-adaptive genetic algorithm Focusing

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A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity

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NEURAL COMPUTING & APPLICATIONS 2019年第5期31卷 1435-1443页

作者： Sun, Na Lu, Yong Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

genetic algorithm (GA) is an important and effective method to solve the optimization problem, which has been widely used in most practical applications. However, the premature convergence of GA has unexpected effect on the algorithm's performance, the main reason is that the evolution of outstanding individuals multiply rapidly will lead to premature loss of population's diversity. To solve the above problem, a method to qualify the population diversity and similarity between adjacent generations is proposed. Then, according to the evaluation of population diversity and the fitness of individual, the adaptive adjustment of crossover and mutation probability is realized. The results of several benchmark functions show that the proposed algorithm can search the optimal solution of almost all benchmark functions and effectively maintain the diversity of the population. Compared with the existing algorithms, it has greatly improved the convergence speed and the global optimal solution.

关键词： genetic algorithm Mutation mode Population diversity self-adaptive genetic algorithm

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A Clever Approach to Develop an Efficient Deep Neural Network Based IDS for Cloud Environments Using a self-adaptive genetic algorithm 2

A Clever Approach to Develop an Efficient Deep Neural Networ...

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2nd International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Chiba, Zouhair Abghour, Noreddine Moussaid, Khalid El Omri, Amina Rida, Mohamed Hassan II Univ Casablanca Math & Comp Dept LIMDAS Labs Fac Sci Ain Chock Casablanca Morocco

ISBN: (纸本)9781538683170

Nowadays, Cloud Computing is one of the fastest growing and most used computing paradigms in the IT field. It is a computational platform that integrates massive computing, storage and network resources into a unified pool of resources, and offers them online over Internet to customers in an on-demand and pay-per-use fashion with least involvement of the cloud service provider. This new archetype characterized by big data and distributed technology uses such technology as multi-tenancy and virtualization, which brings along vulnerabilities, sharing risks and lead to different matters related to security and privacy in cloud computing (CC). Therefore, it is essential to create an efficient intrusion detection system to detect intruders and suspicious activities in and around the CC environment by monitoring network traffic, while maintaining performance and service quality. In this work, we propose a clever approach using a self-adaptive genetic algorithm (SAGA) to build automatically a Deep Neural Network (DNN) based Anomaly Network Intrusion Detection System (ANIDS). SAGA is a variant of standard genetic algorithm (GA), which is developed based on GA improved through an adaptive Mutation algorithm (AMA). Our method consists of using SAGA with the purpose of looking for the optimal or near optimal combination of most relevant values of the parameters included in building of DNN based IDS or impacting its performance, like feature selection, data normalization, architecture of DNN, activation function, learning rate and Momentum term, which ensure high detection rate, high accuracy and low false alarm rate. CloudSim 4.0 simulator platform and Kyoto 2006+ dataset version 2015 were employed for simulation and validation of the proposed system. The experimental results obtained demonstrate that in comparison to several traditional and recent approaches, our proposed IDS achieves higher detection rate and lower false positive rate.

关键词： cloud computing anomaly detection network intrusion detection system deep neural network Optimization self-adaptive genetic algorithm adaptive mutation algorithm Kyoto 2006+dataset

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A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2015年 374卷 502-507页

作者： Lu, Hai-liang Wen, Xi-shan Lan, Lei An, Yun-zhu Li, Xiao-ping Wuhan Univ Sch Elect Engn Wuhan 430072 Peoples R China

A self-adaptive genetic algorithm for estimating Jiles-Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet's hysteresis loops, and the results are in good agreement with published data. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Magnetic hysteresis loop Jiles-Atherton model self-adaptive genetic algorithm Parameter estimation

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self-adaptive genetic algorithm Based MU-MIMO Scheduling Scheme

Self-Adaptive Genetic Algorithm Based MU-MIMO Scheduling Sch...

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15th IEEE International Conference on Communication Technology (ICCT)

作者： Yang, Chengcheng Han, Jiang Li, Yi Xu, Xiaodong Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781479900749

Multi-user Multiple Input Multiple Output (MU-MIMO) system is known to enhance the system capacity with low network delay. One of the biggest challenges with MU-MIMO is on the scheduling scheme which simultaneously selects multiple users to maximize the sum rate. genetic algorithm (GA) works perfectly as an optimal or suboptimal solution with quite low complexity to handle such problems. In this paper, we promote a MU-MIMO scheduling scheme which is based on a modified GA, namely self-adaptive GA (SaGA). SaGA can adjust the performance of population and generation dynamically during run. Furthermore, a self-adaptive elitism strategy also makes contributions to a better behave and faster convergence beyond the traditional GA strategy. Simulation results demonstrate that the performance of SaGA based scheduling is quite approximate to the exhaustive searching while with a much lower complexity.

关键词： MU-MIMO Scheduling scheme self-adaptive genetic algorithm Sum rat Complexity

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Multi-objective Fleet Assignment Problem Based on self-adaptive genetic algorithm

Multi-objective Fleet Assignment Problem Based on Self-adapt...

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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Yang, Xiao Jiang, Bo Civil Aviat Flight Univ China Guanghan Peoples R China

ISBN: (纸本)9783037856932

Since the beginning of the twenty-first century, energy conservation has become the theme of the development of the world. China government set the emissions-reduction targets in various industries on the 12th Five-Year Plan. And the airlines were committed to reduce their carbon emissions. From an operational perspective, the airline model assignment problem is a key factor of the total carbon emissions on the entire route network. But the traditional aircraft assignment models approach did not account for this purpose to reduce carbon emissions. By constructing the multi-objective optimization models consider carbon emissions assignment model using a genetic algorithm, numerical example shows that the model is able to meet all aspects demand which include meeting route network capacity demand, minimizing operating costs and reducing total aircraft fleet carbon emissions.

关键词： self-adaptive genetic algorithm Carbon Emissions Fleet Assignment Multi-objective Optimization

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self-adaptive genetic algorithm Based MU-MIMO Scheduling Scheme

Self-Adaptive Genetic Algorithm Based MU-MIMO Scheduling Sch...

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2013 15th IEEE International Conference on Communication Technology

作者： Chengcheng Yang Jiang Han Yi Li Xiaodong Xu Beijing University of Posts and Telecommunications

ISBN: (纸本)9781479900763

Multi-user Multiple Input Multiple Output(MU-MIMO) system is known to enhance the system capacity with low network *** of the biggest challenges with MU-MIMO is on the scheduling scheme which simultaneously selects multiple users to maximize the sum *** algorithm(GA) works perfectly as an optimal or suboptimal solution with quite low complexity to handle such *** this paper,we promote a MU-MIMO scheduling scheme which is based on a modified GA,namely self-adaptive GA(SaGA).SaGA can adjust the performance of population and generation dynamically during ***,a self-adaptive elitism strategy also makes contributions to a better behave and faster convergence beyond the traditional GA *** results demonstrate that the performance of SaGA based scheduling is quite approximate to the exhaustive searching while with a much lower complexity.

关键词： MU-MIMO Scheduling scheme self-adaptive genetic algorithm Sum rat Complexity

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Free-form Trap Design for Vibratory Feeders using a genetic algorithm and Dynamic Simulation 17

Free-form Trap Design for Vibratory Feeders using a Genetic ...

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17th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Haraldson, Daniel Franzeen Sorensen, Lars Caroe Mathiesen, Simon Univ Southern Denmark Maersk McKinney Moller Inst SDU Robot DK-5230 Odense M Denmark

ISBN: (纸本)9789897584428

The task of feeding parts into a manufacturing system is still extensively handled using classical vibratory bowl feeders. However, the task of designing these feeders is complex and largely handled by experience and trial-and-error. This paper proposes a self-adaptive genetic algorithm based learning strategy that uses dynamic simulation to validate feeder designs. Compared to previous approaches of ensuring parts are oriented to a desired orientation by both deciding on a set of suitable mechanisms and then optimizing them to the specific part, this strategy learns a free-form design needing little prior domain knowledge from the designer. This novel approach to feeder design is validated on two different parts and it creates designs of hills and valley that reorients the parts to a single orientation. The found designs are validated both in simulation and with real-world experiments and achieve high success rates for reorienting the parts.

关键词： self-adaptive genetic algorithm Dynamic Simulation Part Feeding Vibratory Feeders Optimization

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Optimal Operation of Integrated Energy System Based on Exergy Analysis and adaptive genetic algorithm

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IEEE ACCESS 2020年 8卷 158752-158764页

作者： Chen, Haoyong Chen, Simin Li, Ming Chen, Jinbin South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China Guangdong Planning & Designing Inst Telecommun Co Guangzhou 510630 Peoples R China

Due to the rising tensions surrounding the energy industry in response to the depletion of non-renewable energy sources and pollution of the environment, it is especially important to improve the energy utilization rate of integrated energy systems (IESs). Exergy is an important index to measure energy quality. Therefore, this paper proposes an IES operation optimization method based on exergy analysis and an adaptive genetic algorithm. First, based on the energy network theory, a unified expression of the exergy transfer process is given, and the energy level factor is introduced to evaluate the energy quality. Second, the steady-state expression of different forms of exergy in the energy transfer line is given. Third, the exergy economic model of the IES is established by combining exergy analysis, and the solution process of the adaptive genetic algorithm is given. Finally, the operation optimization simulation and analysis of an IES in Bali are carried out to verify the effectiveness of the exergy analysis method and feasibility of the simulation method.

关键词： Resistance heating Adaptation models Load modeling genetic algorithms Energy exchange Optimization Economics Exergy analysis energy networks exergy economic loss energy utilization rate self-adaptive genetic algorithm integrated energy system

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Camera Calibration by Hybrid Hopfield Network and self-adaptive genetic algorithm

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MEASUREMENT SCIENCE REVIEW 2012年第6期12卷 302-308页

作者： Xiang, Wen-jiang Zhou, Zhi-xiong Ge, Dong-yuan Zhang, Qing-ying Yao, Qing-he Shaoyang Univ Dept Mech & Energy Engn Daxiang Dist 422004 Shaoyang Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Hunan Peoples R China S China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China Royal Inst Technol KTH Dept Prod Engn Sch Ind Engn & Management S-10044 Stockholm Sweden Kyushu Univ Fac Engn Dept Mech Engn Nishi Ku Fukuoka 8190395 Japan

A new approach based on hybrid Hopfield neural network and self-adaptive genetic algorithm for camera calibration is proposed. First, a Hopfield network based on dynamics is structured according to the normal equation obtained from experiment data. The network has 11 neurons, its weights are elements of the symmetrical matrix of the normal equation and keep invariable, whose input vector is corresponding to the right term of normal equation, and its output signals are corresponding to the fitting coefficients of the camera's projection matrix. At the same time an innovative genetic algorithm is presented to get the global optimization solution, where the cross-over probability and mutation probability are tuned self-adaptively according to the evolution speed factor in longitudinal direction and the aggregation degree factor in lateral direction, respectively. When the system comes to global equilibrium state, the camera's projection matrix is estimated from the output vector of the Hopfield network, so the camera calibration is completed. Finally, the precision analysis is carried out, which demonstrates that, as opposed to the existing methods, such as Faugeras's, the proposed approach has high precision, and provides a new scheme for machine vision system and precision manufacture.

关键词： Camera calibration projective matrix Hopfield neural network self-adaptive genetic algorithm longitudinal direction and lateral direction

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