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Hamming-distance-based adaptive quantum-inspired evolutionary algorithm for network coding resources optimization

The Journal of China Universities of Posts and Telecommunications 2015年第3期22卷 92-99页

作者： Qu Zhijian Liu Xiaohong Zhang Xianwei Xie Yinbao Li Caihong School of Computer Science and Technology Shandong University of Technology

An adaptive quantum-inspired evolutionary algorithm based on Hamming distance （HD-QEA） was presented to optimize the network coding resources in multicast networks. In the HD-QEA, the diversity among individuals was taken into consideration, and a suitable rotation angle step （RAS） was assigned to each individual according to the Hamming distance. Performance comparisons were conducted among the HD-QEA, a basic quantum-inspired evolutionary algorithm （QEA） and an individual＇s fitness based adaptive QEA. A solid demonstration was provided that the proposed HD-QEA is better than the other two algorithms in terms of the convergence speed and the global optimization capability when they are employed to optimize the network coding resources in multicast networks.

关键词： network coding quantum-inspired evolutionary algorithm Hamming distance multicast network

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A quantum-inspired evolutionary algorithm Based Clustering Method for Wireless Sensor Networks 7

A Quantum-Inspired Evolutionary Algorithm Based Clustering M...

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International Conference on Ubiquitous and Future Networks

作者： Tsai, Chun-Wei Kang, Chun-Ting Chiang, Ming-Chao Natl Ilan Univ Dept Comp Sci & Informat Engn Yilan 26047 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan

ISBN: (纸本)9781479989935

How to prolong the lifetime of a wireless sensor network (WSN) has been a critical research issue because most of the sensors in this kind of network environment have a characteristic that the lifespan of batteries is short;thus, the energy consumption of each sensor has to be as low as possible. The number of sensors in a WSN can easily grow up to tens of thousands. In that case, the lifetime optimization will become a difficult problem. This paper presents a quantum-inspired evolutionary algorithm to improve the performance of the low energy adaptive clustering hierarchy (LEACH). Experimental results show that the proposed algorithm can provide a result that is about 12% better than LEACH, in terms of the energy consumed.

关键词： Wireless sensor network quantum-inspired evolutionary algorithm LEACH

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quantum-inspired evolutionary algorithms for NOMA user pairing

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ICT EXPRESS 2022年第1期8卷 11-17页

作者： Narottama, Bhaskara Hendraningrat, Denny Kusuma Shin, Soo Young Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi South Korea Natl Standardizat Agcy Indonesia Deputy Stand Dev Jakarta Indonesia

This work proposes the utilization of the quantum-inspired evolutionary algorithm (QEA) for user pairing in non-orthogonal multiple access (NOMA). By exploiting quantum concepts such as superposition, it obtained a user pairing solution that approximates the highest achievable sum rate. Moreover, elitist QEA (E-QEA) is proposed to further enhance the performance by eliminating the risk of losing the best solution of the current iteration in the next iteration. Simulation result demonstrates that E-QEA and QEA yield higher average achievable sum rates compared to random user pairing. (C) 2022 Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences.

关键词： quantum-inspired evolutionary algorithm Non-orthogonal multiple access 6G Wireless communications

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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 Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financing

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COMPUTATIONAL ECONOMICS 2024年第2期63卷 919-950页

作者： Chen, Chi Ming Tso, Geoffrey Kwok Fai He, Kaijian City Univ Hong Kong Dept Management Sci 7-261 7-F Lau Ming Wai Acad Bldg AC3 Tat Chee A Hong Kong Peoples R China Hunan Normal Univ Coll Tourism 36 Lushan Rd Changsha 410081 Peoples R China

Mobile e-commerce has grown rapidly in the last decade because of the development of mobile network services, computing capabilities and big data's applications. Financial institutions have been undergoing fundamental transformation in credit risk areas, specifically to traditional credit policy, that is now inadequate for accurately evaluating an individual's credit risk profile in a timely manner. A big-scale dataset representing deep mobile usage of 450,722 anonymous mobile users with a 28-month loan history and mobile behavior of both iOS and Android is designed, can add value for credit scoring in terms of better accuracy and lower feature acquisition cost by introducing a cost-based quantum-inspired evolutionary algorithm (QIEA) feature selection method. The QIEA adopts quantum-based individual representation and quantum rotation gate operator to improve feature exploration capability of conventional genetic algorithm (GA). The expected feature yield fitness function introduced in QIEA able to identify cost-effective feature subsets. Experimental results show that quantum-based method achieves good predictive performances even with only 70-80% number of features selected by GAs, and hence achieve lower feature acquisition costs with budget constraints. Additionally, computational time can be reduced by 30-60% compared with GAs depending on different feature set sizes.

关键词： quantum-inspired evolutionary algorithm Cost constraint feature selection Credit scoring Big data Mobile behavior Optimization

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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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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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quantum-inspired Distributed Memetic algorithm

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Complex System Modeling and Simulation 2022年第4期2卷 334-353页

作者： Guanghui Zhang Wenjing Ma Keyi Xing Lining Xing Kesheng Wang the School of Information Science and Technology and the Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China the School of Information Science and Technology Hebei Agricultural UniversityBaoding 071001China. the State Key Laboratory for Manufacturing System Engineering and the Systems Engineering Institute Xi’an Jiaotong UniversityXi’an 710049China the School of Electronic Xidian UniversityXi’an 710071China the Department of Production and Quality Engineering Norwegian University of Science and TechnologyTrondheim 7491Norway

This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global *** exploration evolves three independent populations by heterogenous *** exploitation evolves an external elite archive in parallel with exploration to balance global and local *** transfer is based on a point-ring communication topology to share successful experiences among distinct search *** restart adopts an adaptive perturbation strategy to control search diversity *** computation is a newly emerging technique,which has powerful computing power and parallelized ***,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum *** QDMA integrates the superiorities of distributed,memetic,and quantum *** experiments are carried out to evaluate the superior performance of *** results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum *** superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.

关键词： distributed evolutionary algorithm memetic algorithm quantum-inspired evolutionary algorithm quantum distributed memetic algorithm

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A novel quantum-inspired evolutionary view selection algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2018年第10期43卷 1-20页

作者： Kumar, Santosh Kumar, T. V. Vijay ABES Engn Coll Dept Comp Sci & Engn Ghaziabad India Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

A data warehouse (DW) is designed primarily to meet the informational needs of an organization's decision support system. Most queries posed on such systems are analytical in nature. These queries are long and complex, and are posed in an exploratory and ad-hoc manner. The response time of these queries is high when processed directly against a continuously growing DW. In order to reduce this time, materialized views are used as an alternative. It is infeasible to materialize all views due to storage space constraints. Further, optimal view selection is an NP-Complete problem. Alternately, a subset of views, from amongst all possible views, needs to be selected that improves the response time for analytical queries. In this paper, a quantum-inspired evolutionary view selection algorithm (QIEVSA) that selects Top-K views from a multidimensional lattice has been proposed. Experimental comparison of QIEVSA with other evolutionary view selection algorithms shows that QIEVSA is able to select Top-K views that are comparatively better in reducing the response times for analytical queries. This in turn aids in efficient decision making.

关键词： Data warehouse on-line analytical processing materialized view selection quantum-inspired evolutionary algorithm

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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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