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Application of Improved Immune Algorithm in Logistics Distribution Center Problem Based on Ternary Optical Computer 2

Application of Improved Immune Algorithm in Logistics Distri...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Sun, Shaojiang Tong, Yihui Qi, Baofeng Wang, Xianchuan Wang, Zhehe Wang, Xianchao Engineering Fuyang Normal University School of Computer and Information Anhui Fuyang China Hainan Tropical Ocean University School of Computer Science and Technology Hainan Sanya China

ISBN: (纸本)9798350347456

To break the bottleneck of computing speed, bandwidth, and power consumption of electronic computers, in recent years, as an emerging computer, the ternary optical computer (TOC) has attracted much attention because of its own mega-parallelism, and as the theory and practice of optical computing technology continue to develop and enrich, the exploration of applications based on ternary optical computer is also advancing. Logistics distribution center (LDC) problem is one of the core issues in the study of logistics networks. A sensible logistics center distribution can improve the efficiency of goods transportation and the operation efficiency of logistics enterprises. Traditional immune algorithms are prone to premature convergence and insufficient local search capability. By introducing a linear selection strategy, the algorithm maintains population diversity. The introduction of Gaussian and Cauchy mutation (G&C mutation) allows the algorithm to jump out of the local optimum. The experimental results show that the algorithm has higher solution quality and better robustness and can solve the logistics center location problem under the given constraints efficiently, and compared to electronic computers, TOC is more efficient. © 2023 IEEE.

关键词： Optical data processing

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Pipelined Training and Transmission for Federated Learning 29

Pipelined Training and Transmission for Federated Learning

引用

29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Shen, Yihui Zhang, Hong Jia, Juncheng Deng, Tao Wang, Zhenhua Teng, Zhiwei Dong, Mianxiong Soochow University School of Computer Science and Technology Suzhou215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China China Merchants New Intelligence Technology Co. Ltd. Beijing China Muroran Institute of Technology Muroran050-8585 Japan

ISBN: (纸本)9798350330717

Federated learning is a distributed machine learning framework that enables distributed edge devices to jointly train machine learning models without exchanging private data. Because federated learning can leverage edge devices' data and computing resources, the research on federated learning has set off an upsurge. However, one of major challenges faced by federated learning is the limited computation and communication resources of edge devices, which leads to low efficiency of federated learning. In this paper, we propose a federated learning framework FedPipeline, which focuses on the local training and uploading process to improve the convergence efficiency. Different from the existing federated learning algorithms with disjoint sequential steps of training and uploading for devices, FedPipeline exploits the parallelism of training and uploading that devices upload part of the model updates in advance while they are conducting local training simultaneously. In this way, the computation and communication resources of devices can be better utilized. We further design strategies for the scheduling of early uploading and the selection of uploaded updates to maximize the benefit of FedPipeline. Experimental results show that FedPipeline can effectively accelerate the convergence speed of federated learning and preserve the accuracy of the final model. © 2023 IEEE.

关键词： Federated Learning Machine Learning Pipeline Training Transmission

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Solve the optimal path planning of material distribution in workshop with time window

Solve the optimal path planning of material distribution in ...

引用

2023 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Gao, Lihui Guo, Zhangcheng Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781510671881

In recent years, the rapid development of the Internet industry and the rapid rise of the e-commerce industry have promoted the continuous development of the logistics industry, and ordinary workshops can no longer meet people's needs, and are gradually moving towards smart workshops. Among them, distribution as an important link of smart workshop logistics, scientific and reasonable planning of the workshop path in advance can not only achieve the on-time delivery of smart workshop materials, but most importantly, reduce the distribution cost. Vehicle route planning is one of the hot issues in current development and research. A workshop material distribution planning model with the objective function of minimizing total cost was established, and the model was solved by an improved ant colony algorithm. By improving the algorithm's state transfer rules and pheromone volatile factors as well as pheromone increment, the improved ant colony convergence speed was fast. The ability to jump out of local optimal is better. The improved algorithm is applied to vehicle routing problem with time window (VRPTW), and the workshop model is simulated by Solomon [1] example, which proves that the total production cost is reduced. The total cost of production includes the number of routes and vehicles used, thus verifying the rationality of the algorithm. © 2023 SPIE. All rights reserved.

关键词： Vehicles

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Time-Series based phase-ordering selection for code-size reduction 36

Time-Series based phase-ordering selection for code-size red...

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36th international conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Peng, ZhenBang Zhang, Yi Xing, Mingjie Wu, Yanjun Zhao, Chen Institute of Software Chinese Academy of Sciences HIAS UCAS HangZhou China College of Intelligent Science and Technology HIAS UCAS HangZhou China Nanjing Institute of Software Technology NanJing China BeiJing China

ISBN: (纸本)1891706594

Recent advancements in networking, embedded systems, mobile computing, and artificial intelligence (AI) have brought renewed focus on code-size reduction. And within this domain, the sequence of optimization passes, known as phase-ordering, considered as a critical determinant. While traditional methods often fall short in addressing complex optimization landscapes, reinforcement learning (RL) algorithms have evolved as a formidable tools to tackle these challenges. According to existing research, previously applied optimization passes can significantly influence subsequent decisions—highlighting a gap in traditional RL algorithms’ ability to account for temporal dynamics inherent to the phase-ordering problem. In response to this, our paper proposes a strategy employing Long Short-Term Memory (LSTM) in conjunction with the Proximal Policy Optimization (PPO) RL algorithm, leveraging the temporal relationships between optimization passes to discern an enhanced phase-ordering scheme. The experimental results shows that our model reaches a 3000 iterations fewer in convergence and an additional 5.758% reduction in code size in entire benchmark than PPO alone. Furthermore, our approach also demonstrates robust generalization capabilities and superior performance across various benchmarks and RL algorithms. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Reinforcement learning

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Introduction to Quantum Multi-Agent Reinforcement Learning: Concepts and Applications

Introduction to Quantum Multi-Agent Reinforcement Learning: ...

引用

international conference on Information and Communication technology convergence (ICTC)

作者： Yeryeong Cho Seok Bin Son Soohyun Park Joongheon Kim Department of Electrical and Computer Engineering Korea University Seoul Republic of Korea Division of Computer Science Sookmyung Women's University Seoul Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Advancements in quantum computer hardware and software have paved the way for the application of quantum computing across various fields. With the development of computers with thousands of qubits, the power of quantum is increasing. Because of efficiency in high action dimensions, quantum computing has advantages in multi-agent fields where the amount of data is huge. At the same time, reinforcement learning (RL) has gained prominence for its potential in unknown environments. Consequently, the integration of quantum computing and multi-agent reinforcement learning (MARL), known as quantum multi-agent reinforcement learning (QMARL), is proposed. This paper introduces the overall concept and applications of quantum computing, MARL, and QMARL. It also shows the current technology trends depending on the application. In conclusion, the paper presents challenges and future research directions for QMARL and shows that QMARL can be utilized in various applications for excellent performance.

关键词： Quantum computing Qubit Reinforcement learning Market research Software Hardware Information and communication technology convergence

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An Ensemble-based Hybrid Approach to Strengthening Network Intrusion Detection Systems 8

An Ensemble-based Hybrid Approach to Strengthening Network I...

引用

8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Srilekha, Nalla Rajkumar, K. Varada Tummala, Usha Rani Andra, Rajesh Chittibabulu, Sape Mandalapu, Srinivasa Rao Raghu Engineering College Department of Cse Dakamarr India Hyderabad India Sir C R Reddy College of Engineering Department of Cse Eluru India Chalapathi Institute of Technology Department of Cse Guntur Mothadaka India Aditya University Department of Cse Surampalem India R V R & J C College of Engineering Department of Csbs Guntur India

ISBN: (纸本)9798350376425

The convergence of 6G technology and the Internet of Everything (IoE) creates a vast interconnected network of smart systems across various domains, including healthcare, agriculture, transportation, and residences. However, this increased connectivity also introduces new security threats, particularly in the form of botnet-based attacks. This research proposes a network-based intrusion detection method utilizing optimization and ensemble machine learning techniques to address these security challenges. A Gaussian filter is employed to remove background noise from the training data, ensuring accurate detection. Correlation-based feature selection and Random Forest Recursive feature elimination are utilized to identify and retain important low-dimensional features. The proposed method is implemented and validated using the Bot-IoT dataset in Python. Comparative analysis demonstrates its superior accuracy compared to existing approaches, highlighting its potential to enhance cloud computing intrusion detection. By effectively detecting and mitigating botnet-based attacks, this research contributes to the security and reliability of 6G and IoT networks, protecting critical infrastructure and ensuring the privacy and integrity of data. © 2024 IEEE.

关键词： Network intrusion

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Collaborative Collision Avoidance of Connected Vehicles Using ADMM with PI-Regulated Lagrangian Multipliers 19

Collaborative Collision Avoidance of Connected Vehicles Usin...

引用

19th IEEE international conference on Automation Science and Engineering, CASE 2023

作者： Tan, Kaige Feng, Lei Törngren, Martin Kth Royal Institute of Technology The Department of Machine Design Stockholm10044 Sweden

ISBN: (纸本)9798350320695

The decentralized approach is popular for the collaborative collision avoidance of connected vehicles in many scenarios. By modeling the task as a collaborative optimal control problem, Lagrangian methods are widely used to decouple the constraints and enable the decentralized solution. However, potential constraint-violating behavior will lead to oscillations during the Lagrangian update, resulting in more iterations and lower real-time efficiency. Existing methods generally neither address this shortcoming, nor explore the Lagrangian update mechanism. This study takes a control perspective, and solves this collaborative optimal control problem based on an extension of the Alternating Directions Method of Multipliers (ADMM) algorithm by performing the iteration update with a Proportional-Integral-(PI-) regulated controller. The link between the Lagrangian optimization and the PI controller improves the convergence performance during iterations. Simulation results in traffic intersection scenarios demonstrate the advantage of the proposed approach. © 2023 IEEE.

关键词： Collision avoidance

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Smart Traffic Control and Prediction Model Empowered with 5G technology, Artificial Intelligence and Machine Learning 1

Smart Traffic Control and Prediction Model Empowered with 5G...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Datey, Vaishnav Prabhu, Sandeep Pune India

ISBN: (纸本)9798350316469

This research paper addresses the escalating challenges of urban traffic congestion and diminished driving safety resulting from the burgeoning automobile population. The study formulates an innovative framework for an Enhanced Traffic Management Model (ETMM) leveraging the convergence of Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. Through in-depth interviews with a diverse panel of experts and comprehensive data analysis, the framework integrates real-time and historical data streams for accurate traffic prediction, congestion estimation, and dynamic signal optimization. The findings reveal that the proposed ETMM substantially improves traffic flow, minimizes travel time, and enhances urban mobility. Additionally, the research demonstrates the viability of AI-driven approaches for traffic management, underscored by successful model deployment and validation. The future scope lies in refining the framework with continuous data updates, incorporating user-centric insights, and expanding its applications to create safer, more efficient urban transportation systems. © 2023 IEEE.

关键词： 5G AI/ML Cloud computing Traffic Management Traffic Prediction Urban Mobility

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Hybrid Optimization Based Convolutional Neural Network for Intrusion Detection System

Hybrid Optimization Based Convolutional Neural Network for I...

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2023 international conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Sureka, N. Girirajan, B. Kumar, Komuravelly Sudheer Priya, B. S. Deepa Sowmya, M. Agni College of Technology Department of Biomedical Engineering Chennai India Sr University Department of Ece Warangal India Sr University School of Computer Science & Artificial Intelligence Warangal India Bannari Amman Institute of Technology Department of Cse Sathyamangalam Erode India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350313413

Intrusion Detection System (IDS) is one of the common deep learning (DL) techniques that are used to find and identify outliers to prevent adversarial attacks, fraud, and network intrusions. This paper proposed a hybrid Particle Swarm Optimization and Grey Wolf Optimization (HPSOGWO) based Convolutional Neural Network (CNN) model for intrusion detection systems. The proposed HPSOGWO is evaluated on the NSL-KDD dataset which contains 5 different classes and 41 features. The PSO is good for global exploration and GWO is for local exploitation. The hybrid PSO and GWO algorithm achieves a better balance between exploration and exploitation and enhances convergence speed. The CNN is utilized to enhance the system's capability to identify and classify intrusion accurately and effectively. The proposed HPSOGWO-based CNN model attains better results by utilizing evaluation metrics like accuracy, precision, recall, specificity, and f1-score values about 0.9918, 0.9852, 0.9908, 0.9879 and 0.9767 correspondingly which is comparatively higher than existing techniques like Chicken Swarm Optimization based Deep Long Short-Term Memory (ChCSO based Deep LSTM), Deep Neural Network (DNN) and LSTM. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Grey Wolf Optimization Internet of Things Intrusion Detection System Particle Swarm Optimization

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Design and Improvement of Firefly Algorithm Based on Ternary Optical Computer 2

Design and Improvement of Firefly Algorithm Based on Ternary...

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2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Tong, Yihui Sun, Shaojiang Qi, Baofeng Zhang, Jie Wang, Xuanchuan Wang, Zhehe Wang, Xianchao Fuyang Normal University School of Computer and Information Engineering Anhui Fuyang China Fuyang Normal University School of Mathematics and Statistics Anhui Fuyang China Hainan Tropical Ocean University School of Computer Science and Technology Hainan Sanya China

ISBN: (纸本)9798350347456

The firefly algorithm is used in the fields of image processing and task scheduling. The traditional design of this algorithm suffers from defects such as too slow convergence speeds and an easy tendency to fall into local optimal solutions. To this end, this paper designs a modified firefly algorithm based on the ternary optical computer, which has the characteristics of megabits, parallelism, and assignability by bit, and improves the classical firefly algorithm, mainly by using chaotic mapping for population initialization, introducing an adaptive dynamic step size, and making out-of-bounds adjustments to equilibrium. The experimental results show that the modified Firefly algorithm based on the ternary optical computer has enhanced merit-seeking ability and increased convergence speed. © 2023 IEEE.

关键词： Parallel processing systems

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