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8th International Symposium on Computational Intelligence and Design (ISCID)

作者： Tang, Qi Zhou, Xiaoye Tang, Jianxun Shenyang Univ Technol Sch Management Shenyang Peoples R China Civil Aviat Univ China Coll Air Traff Management Tianjin Peoples R China

ISBN: (纸本)9781467395878

This paper studies the typical batching and scheduling problem of parallel flowshop, which is characterized by multi-product, multi-stage, parallel production, production coordination between stages, limit of equipment production capacity and inventory capacity, parallel equipment selection and limitation of batch quantity. This paper presents modified quantum evolutionary algorithm(MQEA) with continuous codes representing the batch size on the every stage to enhance the ability to handle constraints. The computational results show that the MQAE may find optimal or suboptimal solutions in a short run time for all the instances.

关键词： chemical industry quantum evolutionary algorithm batch scheduling

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quantum evolutionary algorithm Based Network Intrusion Detection

Quantum Evolutionary Algorithm Based Network Intrusion Detec...

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3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT)

作者： Yin, Xiangdong Hunan Univ Sci & Engn Dept Comp Sci Xiangtan Hunan Peoples R China

ISBN: (纸本)9781424455379

This quantum evolutionary algorithm is a newly developed method of probability optimization based on principles of quantum computing. Regarding complex optimization problems, quantum evolutionary algorithm has a strong search capability and the most optimal performance. Network intrusion detection problem can be converted into the one of data optimal classification, thus this paper introduces the quantum evolutionary algorithm to achieve the whole optimization process. The comparison simulation experiments have been carried out and the experimental results show the feasibility and effectiveness of methods proposed by this paper.

关键词： intrusion detection network evolutionary algorithm quantum evolutionary algorithm network intrusion detection

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A hybrid chaotic quantum evolutionary algorithm for resource combinatorial optimization in manufacturing grid system

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2011年第5-8期52卷 821-831页

作者： Zhang, Haijun Hu, Yefa Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China

Resource composition in manufacturing grid (MGrid) system is one of the recent critical issues of MGrid researches. Especially, resource combinatorial optimization (RCO) becomes more challenging when multiple optimal criteria are considered in MGrid system. Based on the quantum evolution theory, we propose a hybrid chaotic quantum evolutionary algorithm (CQEA) for RCO problems. We also propose a novel resource encoding method for CQEA, which is dynamic and flexible. The experimental results show that the proposed CQEA is effective, efficient, and scalable for the RCO problem in MGrid system.

关键词： Manufacturing grid quantum evolutionary algorithm Chaotic search Combinatorial optimization Resource encoding

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Product family configuration optimisation considering after-sale service: an adaptive quantum evolutionary algorithm approach

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JOURNAL OF ENGINEERING DESIGN 2022年第10期33卷 728-759页

作者： Luo, Xinggang Du, Yuqing Zhang, Zhongliang Kwong, C. K. Hangzhou Dianzi Univ Sch Management Hangzhou Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

By providing after-sale services, manufacturing companies can gain more chances to interact with customers, improve brand preference and loyalty, occupy new bases in the value chain, and create more cross-selling opportunities. Considering after-sale service, we propose a new method for product family configuration optimisation in this study. Mathematical models for product family configuration optimisation are established with the objective of maximising the overall profit of the family of products and after-sale services. An adaptive quantum evolutionary algorithm (AQEA) is developed to solve the established optimisation models. An example of an e-book reader product is used to illustrate the proposed method in a case study. An industry case study shows that: (1) the proposed method outperforms the traditional method;(2) the proposed AQEA has better performance when compared with the other three meta-heuristic algorithms;and (3) the proposed method is more useful for industrial cases where the proportion of service profit is relatively large.

关键词： Product family quantum evolutionary algorithm optimisation after-sale service

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Optimal VAR Planning Considering Intermittent Wind Power Using Markov Model and quantum evolutionary algorithm

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IEEE TRANSACTIONS ON POWER DELIVERY 2010年第4期25卷 2987-2996页

作者： Hong, Ying-Yi Pen, Kuan-Lin Chung Yuan Christian Univ Dept Elect Engn Taipei Taiwan

Techniques for distributed generations (DGs) have attracted increasing attention due to their effects on environmental sustainability and the reduction in traditional megawatt (MW) generation expansion. Wind farms are one of the DGs and have intermittent characteristics. This paper presents a method for static VAR planning considering existing wind generator voltages and transformer taps as controllers to regulate the voltage profile in a distribution system with wind farms. Wind power generations and bus loads are modeled with the Markov model. The probabilities and durations of the operation states are obtained. Through a quantum evolutionary algorithm, the cost of static VAR compensators and MW loss in the system are minimized and the operational constraints are fulfilled. The applicability of the proposed method is verified through simulations using a standalone 25-bus (Penghu) system and a 17-bus system.

关键词： Intermittence Markov model quantum evolutionary algorithm VAR planning voltage fluctuation

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A Methodology Based on quantum evolutionary algorithm for Topology Optimization of Electromagnetic Devices

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IEEE TRANSACTIONS ON MAGNETICS 2019年第6期55卷 1-4页

作者： Li, Yilun Yang, Shiyou Ren, Zhuoxiang Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China Sorbonne Univ UR2 L2E F-75005 Paris France

Stochastic algorithms are powerful global optimizers in topology optimizations (TOs). However, the low convergence speed and high computational cost, due to the large dimensionality of a TO problem, of such an approach are still two open issues to be addressed. To address these two issues, a TO methodology based on a modified quantum evolutionary algorithm is proposed. In the proposed methodology, an adaptive updating mechanism for the rotation gate is introduced to balance the exploration and exploitation searches;and a redistribution mechanism of design variables is developed to reduce the computational cost of the algorithm. The numerical results as reported validate the effectiveness and feasibility of the proposed methodology and demonstrate its advantages over a traditional ON/OFF method and its ancestor.

关键词： quantum evolutionary algorithm redistribute mechanism topology optimization (TO)

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C4ISR Service Deployment Based on an Improved quantum evolutionary algorithm

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第2期18卷 2405-2419页

作者： Jiao, Zhiqiang Zhang, Jieyong Yao, Peiyang Wan, Lujun Wang, Xun Air Force Engn Univ Grad Coll Xian 710077 Peoples R China Air Force Engn Univ Informat & Nav Coll Xian 710077 Peoples R China Air Force Engn Univ ACT Nav Xian 710077 Peoples R China Univ Natl Def Sci & Technol Expt Training Base Sch Informat & Commun Xian 710106 Peoples R China

To overcome the limited communication bandwidth in the deployment of command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) services, a service deployment platform was extended from the cloud to a terminal platform based on the concept of fog computing. Additionally, computing and memory resources on the terminal platform were used to alleviate the communication pressure due to the flow of combat information. A corresponding C4ISR service deployment model was constructed, and the quantum evolutionary algorithm, the shortest path algorithm, and the pairwise exchange method were combined to create a two-level search algorithm. This method can plan the operational information flow path and service deployment simultaneously, thereby achieving the optimal deployment of services even under the constraints of limited communication bandwidth.

关键词： Bandwidth Cloud computing Computational modeling Task analysis Software Processor scheduling Service-oriented architecture Bandwidth constraints C4ISR service deployment pairwise exchange method quantum evolutionary algorithm

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Multiupdate mode quantum evolutionary algorithm and its applications to combination and permutation problems

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第2期7卷 166-173页

作者： Wei, Xin Fujimura, Shigeru Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

Based on the concept and principles of quantum computing, this paper proposes a new evolutionary algorithm called multiupdate mode quantum evolutionary algorithm (MMQEA). MMQEA, like the other classic evolutionary algorithms, is also characterized by the representation of the individual evaluation function and the population dynamics;however, instead of binary, numeric, or symbolic representation, MMQEA uses two interactional q-bit strings as an individual. Update modes are introduced as a variational operation that evolves the individuals to make a better solution. The proposed individual structure and update modes are inspired by quantum entanglement. Update modes perform as reproducing the states of a pair of q-bit strings of individual simultaneously. For guiding the individual evolution to maintain the population diversity and avoid prematurity, each q-bit string of individual provides its evolutionary history information to another. To demonstrate its effectiveness and applicability, the proposed algorithm was tested on two famous combinatorial optimization problems, namely, the knapsack problem and flow shop problem. The results show that MMQEA performs very well compared to quantum evolutionary algorithm (QEA) and the conventional genetic algorithm. (C) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： quantum evolutionary algorithm multiupdate mode q-bit knapsack problem flowshop problem

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A quantum evolutionary algorithm for the second-best congestion pricing problem in urban traffic networks

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TRANSPORTATION PLANNING AND TECHNOLOGY 2015年第8期38卷 851-865页

作者： Shahbandi, Mehrdad Gholami Nasiri, Mohammad Mahdi Babazadeh, Abbas Univ Tehran Sch Civil Engn Coll Engn Tehran 11155 Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This paper investigates the congestion pricing problem in urban traffic networks. A first-best strategy, a second-best strategy for toll leveling in closed cordons and a second-best strategy for determining both toll levels and toll points are considered. The problem is known to be a mixed integer programming model and formulated as a bi-level optimization problem, with an objective of maximizing the social welfare. A method is presented to solve the problem, based on a novel metaheuristic algorithm, namely quantum evolutionary algorithm (QEA). To verify the proposed method, the widely used genetic algorithm (GA) is also applied to solve the problem. The problem is solved for a medium-size urban traffic network and the results of the QEA are compared against the conventional GA. Computational results show that the QEA outperforms the GA in solution quality.

关键词： congestion pricing network cordon optimization quantum evolutionary algorithm genetic algorithm

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A New quantum evolutionary algorithm with Sifting Strategy for Binary Decision Diagram Ordering Problem

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2010年第4期4卷 47-61页

作者： Layeb, Abdesslem Saidouni, Djamel-Eddine Univ Mentouri Constantine Dept Comp Sci Constantine Algeria

In this work, the authors focus on the quantum evolutionary quantum hybridization and its contribution in solving the binary decision diagram ordering problem. Therefore, a problem formulation in terms of quantum representation and evolutionary dynamic borrowing quantum operators are defined. The sifting search strategy is used in order to increase the efficiency of the exploration process, while experiments on a wide range of data sets show the effectiveness of the proposed framework and its ability to achieve good quality solutions. The proposed approach is distinguished by a reduced population size and a reasonable number of iterations to find the best order, thanks to the principles of quantum computing and to the sifting strategy.

关键词： Binary Decision Diagram quantum Computing quantum evolutionary algorithm Sifting algorithm Variable Ordering Problem

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