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Joint Caching and User Association Optimization for Adaptive Bitrate Video Streaming in UAV-Assisted Cellular Networks

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IEEE ACCESS 2022年 10卷 106275-106285页

作者： Xie, Junfeng Wang, Zhaoba Chen, Youxing North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Edge caching and adaptive bitrate video streaming are two promising techniques to ensure a good video viewing experience. Edge caching can bring contents closer to users to alleviate redundant content transmissions, reduce user-perceived delay and improve transmission capability. Adaptive bitrate video streaming is able to adaptively adjust video quality based on time-varying network conditions and different users' preference. Due to the strong coupled relationship between caching and user association, in this article, we focus on the issue of joint caching and user association optimization for adaptive bitrate video streaming in UAV-assisted cellular networks. First, we formulate the optimization problem as a non-linear integer programming (NLIP) to minimize the content delivery delay. To solve this challenging NP-hard problem, a heuristic algorithm based on quantum-inspired evolutionary algorithm (QEA) is proposed to obtain the best caching and user association solutions iteratively. Finally, simulations are conducted to demonstrate that compared with three benchmark algorithms without joint optimization of caching and user association, the proposed algorithm can greatly improve users' video viewing experience and achieve better system performance in terms of reducing the total content delivery delay.

关键词： Streaming media Bit rate Adaptive systems Delays Optimization Heuristic algorithms Cellular networks Unmanned aerial vehicles evolutionary algorithms Edge caching adaptive bitrate video streaming user association unmanned aerial vehicle quantum-inspired evolutionary algorithm

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Test Scheduling for Network-on-Chip Using XY-Direction Connected Subgraph Partition and Multiple Test Clocks

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2016年第1期32卷 31-42页

作者： Hu, Cong Li, Zhi Xu, Chuanpei Jia, Mengyi Xidian Univ Sch Mechanoelect Engn Xian 710071 Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Guilin Univ Aerosp Technol Guilin 541004 Peoples R China

It is attractive to reuse the on-chip functional interconnects as test access mechanism (TAM) in network-on-chip (NoC) system testing. However, in the methodology of NoCreuse as TAM, the influence factors in NoC testing significantly increased. To further reduce test time and show significant gains over other work, we propose XY-direction connected subgraph partition (XYCSP) approach to eliminate the path conflicts before testing, and concurrently determine the position of test access points. We then present a multiple test clock strategy to bridge the gap between the NoC channel bandwidth and the core test wrapper bandwidth. With the help of adaptive probability gate quantum-inspired evolutionary algorithm (APGQEA) strategy, which blends adaptive strategy and multi-nary oriented techniques, the proposed NoC test scheduling algorithm permits quick exploration and exploitation of the solution space. Moreover, power constraints are also taken into account. Experimental results for the ITC'02 benchmarks show that the proposed scheme can achieve shorter test time compared to prior works.

关键词： Network-on-chip Test scheduling XY-direction connected subgraph partition Multiple test clocks quantum-inspired evolutionary algorithm

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MOQEA/D: Multi-Objective QEA With Decomposition Mechanism and Excellent Global Search and Its Application

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第9期25卷 12517-12527页

作者： Deng, Wu Cai, Xing Wu, Daqing Song, Yingjie Chen, Huiling Ran, Xiaojuan Zhou, Xiangbing Zhao, Huimin Sichuan Tourism Univ Sch Informat & Engn Chengdu 610100 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci Nanjing 210094 Peoples R China Shanghai Ocean Univ Coll Econ & Management Shanghai 201306 Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Wenzhou Univ Comp Sci Dept Wenzhou 325035 Peoples R China Chiang Mai Univ Int Coll Digital Innovat Chiang Mai 50200 Thailand Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

In this paper, a large-scale multi-objective gate assignment model is constructed by considering the flight international and domestic attributes, task type, airline affiliation, and aircraft type. Then a multi-objective quantum-inspired evolutionary algorithm based on decomposition mechanism, namely MOQEA/D is developed to solve the constructed model effectively. Specifically, a new decomposition mechanism is designed to decompose the multi-objective GAP into several single-objective sub-GAPs. Each quantum bit string solves a single-objective sub-GAP independently. And a new optimal crossover strategy is proposed to limit the randomness of observation operations and maximize the preservation of excellent genes to further improve the optimization performance. Finally, the multi-objective knapsack problem and the multi-objective GAP are selected to verify the effectiveness of the MOQEA/D. The experiment results demonstrate that the MOQEA/D can effectively solve large-scale multi-objective knapsack problem and obtain ideal gate assignment results. It takes on very significance and application value in solving complex optimization problems.

关键词： Gate assignment quantum-inspired evolutionary algorithm decomposition mechanism optimal crossover multi-objective optimization

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quantum-inspired Genetic algorithm for Resource-Constrained Project-Scheduling

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IEEE ACCESS 2021年 9卷 38488-38502页

作者： Saad, Hatem M. H. Chakrabortty, Ripon K. Elsayed, Saber Ryan, Michael J. Univ New South Wales Canberra Sch Engn & Informat Technol Capabil Syst Ctr Canberra ACT 2612 Australia Al Azhar Univ Fac Sci Dept Phys Cairo 11651 Egypt Univ New South Wales Canberra Sch Engn & Informat Technol Canberra ACT 2612 Australia

The Resource-Constrained Project-Scheduling Problem (RCPSP) is an NP-hard problem which can be found in many research domains. The optimal solution of the RCPSP problems requires a balance between exploration/exploitation and diversification/intensification. With this in mind, quantum-inspired evolutionary algorithms' ability to improve the population and quality of solutions, this work investigates the performance of a quantum-inspired genetic algorithm (QIGA), which has been adapted to work with RCPSPs. The proposed QIGA possesses the same structure as classical genetic algorithms, but the initial and updated populations are implemented using quantum gates and quantum superposition, bearing in mind the adaptation of such operators to fit with discrete problems. This work aims to solve RCPSPs using the QIGA to investigate the influence of the various quantum parameters in the proposed algorithm, such as the quantum population size, different options for the quantum gates and re-combination to use in the QIGA. The well-known PSPLIB benchmark instances of J30, J60 and J120 activities are used to test the effectiveness and performance of the proposed QIGA. It is apparent from the results that quantum mutation, quantum crossover and representation of quantum superposition using quantum gates enhances population diversity. The QIGA is also found to outperform many other evolutionary algorithms.

关键词： quantum computing Logic gates Genetic algorithms Heuristic algorithms Search problems Optimization Statistics quantum-inspired evolutionary algorithm resource-constrained project-scheduling problems NP-hard problems genetic algorithm quantum gates quantum superposition

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Automatic Design of P Systems for Five Basic Arithmetic Operations within One Framework

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Chinese Journal of Electronics 2014年第2期23卷 302-304页

作者： CHEN Yixi ZHANG Gexiang WANG Tao HUANG Xiaoli School of Electrical Engineering Southwest Jiaotong University

To solve the programmability of a membrane system, this paper presents an automatic design method of a cell-like P system framework for performing five basic arithmetic operations. This method proved that different P systems can be designed with same framework by programming. A technique is introduced to remove redundant rules in the process of the design of a P *** effectiveness and feasibility of this method is verified by experimental results.

关键词： Membrane computing P system framework Automatic design Basic arithmetic operation quantum-inspired evolutionary algorithm

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A twofold update quantum-inspired genetic algorithm for efficient combinatorial optimal decisions in engineering system design and operations

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JOURNAL OF ENGINEERING DESIGN 2023年第4期34卷 271-293页

作者： Zou, Pan Jiao, Jianxin (Roger) Zhou, Feng Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA Univ Michigan Dept Ind & Mfg Syst Engn Dearborn MI USA

It is often computationally intensive to solve combinatorialoptimisation problems due to the inherent large solution space. These problems are commonly observed in the fields of engineering system design and operations. Traditional techniques are limited in handling the growing complexity and size of these problems efficiently. This paper presents a twofold update quantum-inspired genetic algorithm to solve combinatorial optimisation problems. It is generalised as an improved version of quantum-inspired evolutionary algorithm. The paper proposes a new problem formulation and the solution procedure for quantum-inspired evolutionary algorithms. An improved quantum-inspired genetic algorithm is proposed with a twofold update mechanism along with various operators. The proposed method is applied to solving a real-life engineering system optimisation problem of modular design. The results are compared using a classical genetic algorithm versus a quantum-inspired evolutionary algorithm, indicating that the proposed method outperforms the traditional methods and is more robust and more efficient.

关键词： Combinatorial optimisation engineering system optimisation quantum-inspired computing quantum-inspired evolutionary algorithm quantum-inspired genetic algorithm

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Free Search with Adaptive Differential Evolution Exploitation and quantum-inspired Exploration

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2012年第3期35卷 1035-1051页

作者： Yin, Jihao Wang, Yifei Hu, Jiankun Univ New S Wales Australian Def Force Acad Sch Engn & Informat Technol Univ Coll Canberra ACT 2600 Australia Beihang Univ Sch Astronaut Beijing 100191 Peoples R China

Recently an interesting evolutionary mechanism, sensibility, inherited from a concept model of Free Search (FS) was introduced and used for solving network problems. Unfortunately, the original FS is not easy to implement because it requires key knowledge that is not clearly defined in the existing literature to determine the neighborhood space that profoundly affects the performance of the original FS. This paper thus designs a new implementation for the concept model of FS, and proposes a new algorithm, called Free Search with Adaptive Differential Evolution Exploitation and quantum-inspired Exploration (ADEQFS) to address this issue. In ADEQFS, we focus on designing a new mutation strategy by employing adaptive differential evolution techniques as well as concepts and principles from real-coded quantum-inspired evolutionary algorithm. In addition, we use the crossover operation from the traditional Differential Evolution scheme to alleviate the premature convergence for the proposed algorithm. Furthermore, we employ the greedy mechanism to preserve the best solutions found at each generation. The convergence analysis of the proposed algorithm is also presented in this paper. We give the proof of convergence by using the Markov chain model. Thirty-four optimization test functions with different mathematical characteristics are employed as benchmark set to test the performance of ADEQFS. The numerical results highlight the improved convergence rate and computation reliability. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： Free search Adaptive differential evolution quantum-inspired evolutionary algorithm Global optimization evolutionary computation

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A novel hybrid column generation-metaheuristic approach for the vehicle routing problem with general soft time window

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INFORMATION SCIENCES 2015年 316卷 598-615页

作者： Beheshti, Ali Kourank Hejazi, Seyed Reza Isfahan Univ Technol Dept Ind & Syst Engn Esfahan 841568311 Iran

The vehicle routing problem with general soft time window involves designing a set of routes for a fleet of vehicles based at a central depot that is required to service a number of geographically dispersed customers while minimizing the total travel distance and delivery time costs. Delivery time cost function is a general piecewise linear function. In this study, we propose a mathematical model of this problem. Then an efficient hybrid column generation-metaheuristic approach is developed. In the proposed algorithm, the hybridization of column generation (CG) and the metaheuristic is performed in both integrative and collaborative modes. In the integrative phase, a quantum-inspired evolutionary algorithm is used to solve the sub-problems of column generation. In the collaborative phase, the column generation and electromagnetism algorithms are parallelized, and the information from these two algorithms is exchanged to find better solutions. Finally, the performance of the proposed approach is evaluated using a set of modified classic benchmark instances adopted from the literature. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Column generation heuristic Vehicle routing problem with general soft time window quantum-inspired evolutionary algorithm Electromagnetism algorithm

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Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models

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NEURAL NETWORKS 2009年第5-6期22卷 623-632页

作者： Schliebs, Stefan Defoin-Platel, Michael Worner, Sue Kasabov, Nikola Auckland Univ Technol KEDRI Auckland New Zealand Lincoln Univ Ctr Bioprotect Canterbury New Zealand

This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are Studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naive Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an Optimal Solution (or a close to it), in a better accuracy, and in a more informative set of features selected. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Evolving spiking neural network quantum-inspired evolutionary algorithm Multiple probabilistic model Estimation of distribution algorithm

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Evacuation path optimization based on quantum ant colony algorithm

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ADVANCED ENGINEERING INFORMATICS 2016年第3期30卷 259-267页

作者： Liu, Min Zhang, Feng Ma, Yunlong Pota, Hemanshu Roy Shen, Weiming Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia Tongji Univ Key Lab Embedded Syst & Serv Comp Shanghai Peoples R China

Evacuation planning contains more than a few decisions which have to be made in a very short period of time and in the most appropriate way. Evacuation path optimization has vital importance in reducing the human and social harm and saving the aid time. Significant research efforts have been made in the literature to deal with evacuation optimization on the basis of deterministic optimization model, nevertheless the stochastic aspects or uncertainty of real-world evacuation have not been taken into account comprehensively. inspired by the promising performance of heuristic algorithms to solve combinatorial problems, this paper proposes an improved quantum ant colony algorithm (QACA) for exhaustive optimization of the evacuation path that people can evacuate from hazardous areas to safe areas. In comparison with ACO (ant colony optimization) based method, QACA has the capability of finding a good solution faster using fewer individuals and possesses strong robustness, as a result of the quantum representation and updating of pheromone. Experiment results show that the proposed approach executes more effectively during evacuation. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Evacuation optimization Swarm intelligence quantum-inspired evolutionary algorithm Ant colony algorithm

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