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Teaching-Learning-Based Differential Evolution Algorithm for Optimization Problems

Teaching-Learning-Based Differential Evolution Algorithm for...

引用

international conference on Internet computing in Science and Engineering (ICICSE)

作者： Changming Zhu Yan Yan Haierhan Jun Ni Research and Development Center China Academy of Launch Vehicle Technology Beijing China Shanghai Sanda University Shanghai China

Differential Evolution (DE) is one of the current best evolutionary algorithms. It becomes the popular research topic in many fields such as evolutionary computing and intelligent optimization. At present, DE has successfully been applied to diverse domains of science and engineering, such as signal processing, neural network optimization, pattern recognition, machine intelligence, chemical engineering and medical science. However, almost all the evolutionary algorithms, including DE, still suffer from the problems of premature convergence, slow convergence rate and difficult parameter setting. To overcome these drawbacks, we propose a novel Teaching-Learning-Based Differential Evolution Algorithm(TLDE), in which the pheromone and the sensitivity model in free search algorithm to replace the traditional roulette wheel selection model, and introduces OBL to present an improved artificial bee colony algorithm. Experimental results confirm the superiority of Teaching-Learning-Based Differential Evolution Algorithm over several state-of-the-art evolutionary optimizers.

关键词： Optimization convergence Sociology Statistics Signal processing algorithms Algorithm design and analysis Benchmark testing

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Proportionate Conjugate Gradient Algorithm for Sparse Systems

Proportionate Conjugate Gradient Algorithm for Sparse System...

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international conference on computing and Networking technology (ICCNT)

作者： Shila Dhande Rajib Lochan Das Department of Electronics and Communication DAIICT Gandhinagar India Department of Electronics and Computer KJSCE Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

For colored input, the Recursive Least Square (RLS) technique is employed to achieve a higher computational complexity but a faster convergence rate. Recently, For lesser computational complexity, the conjugate gradient (CG) algorithm has replaced the RLS algorithm as it yields a similar convergence rate with. In this paper, a sparse CG algorithm is proposed by introducing a proportional gain matrix in its update equation to obtain further improvement in the convergence rate for identifying sparse systems with colored inputs. The proposed algorithm provides a better convergence rate as the active taps (AT) get higher step size and thus converge fast. Incorporation of the gain function is made tactfully to obtain improvement in its overall performance. Simulation findings demonstrate that the suggested technique considerably increases the steady-state error and convergence rate.

关键词： Adaptive systems Gaussian noise Adaptive filters Filtering algorithms Mathematical models Steady-state Sparse matrices Computational complexity Gain convergence

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An Improved EM-Based Semi-supervised Learning Method

An Improved EM-Based Semi-supervised Learning Method

引用

international Joint conference on Bioinformatics, Systems Biology and Intelligent computing, IJCBS

作者： Xinghua Fan Zhiyi Guo Houfeng Ma College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

The iterative process in the standard EM-based semi-supervised learning includes two steps: firstly, use the classifier constructed in previous iteration to classify all unlabeled samples; then, train a new classifier based on the reconstructed training set, which is composed of labeled samples and all unlabeled samples. There is a problem in the process of reconstructing the training set: unlabeled samples should be divided into two parts by the classifier, reliable and misclassified, the misclassified part is considered as training samples. This affects the finally classification performance and convergence efficiency. This paper presents an improved method, which reconstructs the labeled samples set and then considers it as the training set in each iterative process. The process is implemented by adding the reliable part in unlabeled samples to labeled samples and forming a new labeled samples set. Experimental results in text classification show that the proposed method improves the classification performance and convergence efficiency.

关键词： Semisupervised learning Iterative algorithms convergence Iterative methods Text categorization Electromagnetic compatibility Machine learning Bioinformatics Systems biology Intelligent systems

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Optimal Task Scheduling in 6G Networks: A Variational Quantum computing Approach 10th

Optimal Task Scheduling in 6G Networks: A Variational Quant...

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10th EAI international conference on Industrial Networks and Intelligent Systems, INISCOM 2024

作者： Khalid, Uman Rehman, Junaid ur Farooq, Ahmad Zaman, Fakhar Shin, Hyundong Department of Electronics and Information Convergence Engineering Kyung Hee University Yongin Korea Republic of University of Luxembourg Esch-sur-Alzette Luxembourg Department of Electrical Engineering and Automation Aalto University Espoo Finland Clayton Australia

ISBN: (纸本)9783031673566

Optimal task scheduling in 6G networks plays a crucial role in enabling a wide range of applications such as augmented reality, virtual reality, autonomous vehicles, and the Internet of Things (IoT). With the network landscape becoming a more complex and diverse ecosystem, it is critical to advance conventional scheduling algorithms in order to guarantee the necessary efficiency and performance. In this regard, quantum computing can significantly speed up search for optimal schedules, increase the likelihood of finding optimal solutions, and facilitate the creation of correlations between tasks in a scheduling problem by virtue of parallelism, superposition, and entanglement. In this paper, we explore the variational quantum computing approach to tackle the complex task scheduling problem in cloud radio access network (C-RAN) architecture for 6G networks. By leveraging the quantum approximate optimization algorithm (QAOA) and utilizing IBM Qiskit as a simulation testbed, we aim to optimize task scheduling for enhancing wireless network performance. Herein, the classical quadratic constrained integer optimization (QCIO) problem instance is transformed to an Ising Hamiltonian formulation to implement task scheduling optimization on a quantum computer. We also evaluate the effectiveness and stability of QAOA by analyzing the expected cost and the probability of obtaining an optimal schedule as a function of QAOA circuit layers. Our findings highlight the applicability of variational quantum computing in addressing intricate optimization problems as well as setting the stage for the development of more advanced quantum optimization algorithms for 6G networks. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Scheduling algorithms

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A Kind of Composite Genetic Algorithm Based on Extreme Pre-Judgement

A Kind of Composite Genetic Algorithm Based on Extreme Pre-J...

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international conference on Computer Supported Cooperative Work in Design

作者： Fachao Li Limin Liu Chenxia Jin College of Economics and Management Hebei University of Science and Technology Shijiazhuang China College of Science Hebei University of Science and Technology Shijiazhuang China

ISBN: (纸本)1424409624

In view of the slowness and the locality of convergence for Simple Genetic Algorithm (SGA for short), Composite Genetic Algorithm, as an improved genetic algorithm, is proposed based on extreme pre-judgement. The implementation is also given. The convergence and computing efficiency are analyzed from different aspects by the methods of Markov chain and simulation. All the results indicate that the new type of algorithm possess better convergence with the strategy of reserving the optimal individuals and could avoid efficiently the premature phenomenon. So it will be applied to the optimization problems with large-scale and high-accuracy.

关键词： Genetic algorithms Genetic mutations Large-scale systems Algorithm design and analysis Computational modeling Analytical models convergence Optimization methods Collaborative work Educational institutions

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Intelligent Dictionary Elements Extraction using Optical Character Recognition and Deep Learning based Object Detection

Intelligent Dictionary Elements Extraction using Optical Cha...

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international conference for convergence of technology (I2CT)

作者： Nigod Dayal Dhurke Sanjay Rabidas Sanket Vijay Narkhede Human Centred Design Computing Group Centre for Development of Advanced Computing (C-DAC) Pune India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

The Sanskrit Encyclopaedic Dictionary conceived and compiled by Deccan College Post Graduate Institute, Pune, India is available in paper format. For greater reach, there is a need for digitization of the dictionaries and conversion of these volumes into a comprehensive structured database. These volumes have Articles with a typical complex structure. In this paper, a novel approach for Automatic Lexical Element Extraction from Sanskrit Encyclopaedic Dictionary Pages (ALEE-SED) that consists of Sanskrit Dictionary Page Segmentation into articles and their meaning blocks using Optical Character Recognition (OCR) and Detectron2 Model further followed by an intelligent article wise dictionary (Lexical) element extraction is described.

关键词： Deep learning Dictionaries Databases Optical character recognition Object detection convergence

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Multi-class Vehicle Detection Using Multi-scale Hard Negative Mining 1

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6th international conference on Internet of Vehicles and the 9th international Symposium on Cloud and Service computing, IOV/SC2 2019

作者： Kang, Minsung Lim, Young-Chul Research Division of Future Automotive Technology Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of

ISBN: (数字)9783030386511

ISBN: (纸本)9783030386504

The performance capabilities of object detection processes have been greatly improved due to the development of deep learning methods. As the performance of object detection methods improves, studies of problems that remained unsolved are now becoming more common. In CCTV technology, such as tracking technology, it has become easier to resolve the matching issue as the performance of object detection methods has improved. A network such as YOLOv3, a single stage multi scale based object detection method, robustly detects objects of various sizes while maintaining real-time performance. Object detection methods for multi scale structures are associated with the problem of an imbalance between a positive box and a negative box on each feature scale. In the CCTV environment, the object detection performance can be degraded due to this ‘unbalance’ problem because the number of objects corresponding to the positive box is relatively small. The learning time is also important because re-training is required for new environments that are constantly being added. In order to solve this problem, we propose a method that solves the unbalance problem through multi scale hard negative mining and that improves the object detection performance while also reducing the learning time. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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An Approach to Integration of Gait and Fingerprint Features for Advanced Biometric Recognition technology

An Approach to Integration of Gait and Fingerprint Features ...

引用

international conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Amit Kumar Trivedi Kartik Kumar Raghav Aggarwal Anmol Garg Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala Punjab India

In this study, the convergence of gait recognition and fingerprint identification was explored using heat map integration. Gait recognition, a biometric method that relies on distinct walking patterns, finds applications in security and healthcare. The study focused on the accuracy assessment of various body segments when gait and fingerprint technologies were combined. The results indicated that the highest accuracy, at 97.12%, was achieved by integrating the lower body's middle portion with fingerprints.

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SMP Based Solver for Large Binary Systems

SMP Based Solver for Large Binary Systems

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IEEE international conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Nikhil Jain Brajesh Pande Phalguni Gupta Indian Institute of Technology Kanpur Kanpur India

Solving a large sparse system of linear equations (SSLE)over the binary field is an important step in the general number field seive (GNFS) algorithm that is often used to factorize large integers used in the RSA algorithm. Block Lanczos method proposed by Montgomery is an efficient and reliable method for solving such systems. A number of implementations of Block Lanczos method on distributed systems have been proposed in past. This paper discusses the parallel Montgomery's Block Lanczos method for solving binary system of equations on shared multiprocessors(SMP). A simple experiment shows that the speed of convergence of this algorithm is dependent on a wise choice of initial guess for kick-starting the algorithm. A somewhat dense choice of the initial guess is suggested rather than a random choice for faster convergence. Devoid of any communication overheads of a loosely coupled system, the improved method gives good performance on SMP systems which can provide an able alternative to the more popular distributed systems. Details of implementation of this algorithm on SMP and experimental results are also provided in the paper.

关键词： Equations Linear systems convergence Iterative algorithms Distributed computing Protocols Cryptography Security Polynomials Nonlinear filters

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An Improved Firefly Algorithm Enhanced by Negatively Correlated Search Mechanism

An Improved Firefly Algorithm Enhanced by Negatively Correla...

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IEEE international conference on Progress in Informatics and computing (PIC)

作者： Shi Wang Xiao Yang Zonghui Cai Lin Zou Shangce Gao College of Computer Science and Technology Taizhou University Jiangsu China Faculty of Engineering University of Toyama Toyama Japan

ISBN: (纸本)9781538676738;9781538676721

Firefly algorithm (FA) is inspired by natural phenomena and it is an effective optimizer for solving complex problems. However alike other swarm intelligent algorithms, FA also suffers from the premature convergence problem. To further improve the search effectiveness and alleviate this issue, the hybridization of different algorithms has shown to be a promising research direction. In this paper, we for the first time propose a hybrid algorithm, called NCFA by combing the firefly algorithm with the negatively correlated search. The characteristics of firefly algorithm make population diversity decline rapidly, which is more likely to lead to premature convergence. The core of the negatively correlated (NC) search is considered to be a special diversity control strategy. Experimental results based on CEC2017 benchmark functions demonstrate the superiority of such hybridization, and the diversity analysis of population also verify its rationality.

关键词： Sociology Statistics Diversity reception convergence Optimization Particle swarm optimization Probability distribution

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