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3rd International Conference on Cloud Computing and Security (ICCCS)

作者： Xiang, Sheng He, Yigang Chang, Liuchen Wu, Kehan Zhang, Chaolong Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China Anqing Normal Univ Sch Phys & Elect Engn Anqing 246011 Peoples R China Univ New Brunswick Dept Elect & Comp Engn Fredericton NB E3B 5A3 Canada

ISBN: (纸本)9783319685427;9783319685410

As a well-known combinatorial optimization problem, knapsack problems commonly arise in security areas. In this paper, an improved quantum-inspired evolutionary algorithm (PEQIEA) is proposed to solve knapsack problems. In PEQIEA, in each iteration, the state preference of the elite group is used to update the group. The elite group of each iteration consists of a certain number of individuals which are selected by their fitness values. A state preference is proposed to improve the efficiency of the algorithm. A new quantum-inspired gate is obtained by the elite group and their state preference. The Q-gate is then used to make the evolution of the group. The parameters in PEQIEA, which affect the accuracy and efficiency of the algorithm, are discussed empirically. The performance of PEQIEA is then evaluated through extensive experiments.

关键词： quantum-inspired evolutionary algorithm Elite group State preference Knapsack problem

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A MULTI-OBJECTIVE HW-SWCO-SYNTHESIS algorithm BASED ON quantum-inspired evolutionary algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2008年第2期7卷 129-148页

作者： Wei, Wenlong Li, Bin Zou, Yi Zhang, Wencong Zhuang, Zhenquan Univ Sci & Technol China Nat Inspired Comp & Applicat Lab Hefei 230026 Anhui Peoples R China

Hardware-Software (HW-SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW-SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW-SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.

关键词： HW-SW co-synthesis quantum-inspired evolutionary algorithm multi-PRI scheduling multi-objective optimization pareto optimal nondominated solutions

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Multi-Class Wavelet SVM Classifiers Using quantum-inspired evolutionary algorithm

Multi-Class Wavelet SVM Classifiers Using Quantum-Inspired E...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.18

作者： Zhiyong Luo Wenfeng Zhang Yougang Zhang Min Xiang Changhao Piao College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Automation Northwestern Polytechnical University Xi'an China

Based on quantum-inspired evolutionary algorithm (QEA), a novel approach of constructing multi-class least squares wavelet SVM (LS-WSVM) classifiers is presented, regularization parameters and kernel parameters of LS-WSVM can be optimized. quantum-inspired evolutionary optimazition can get appropriate parameters of LS-WSVM with global search, so the LS-WSVM model for the multi-class classifiers is built. And then, classification is studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the approach for the multi-class LS-WSVM classifiers is effective, that can obtain the optimal parameters of LS-WSVM with global searching QEA, and improved LS-WSVM provides excellent precision for classification.

关键词： Least squares wavelet support vector machine quantum-inspired evolutionary algorithm Parameter optimization Multi-class classifiers

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Handling Sequential Ordering Problem with quantum-inspired evolutionary algorithm

Handling Sequential Ordering Problem with Quantum-inspired E...

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智能计算及其应用国际会议

作者： YANG Qing1, ZHONG Shounan1 , NING Shengchao2 (1.chool of Mathematics and Statistics, Wuhan University, Wuhan 430072, China) (2.chool of Computer, Wuhan University, Wuhan 430079, China)

In this paper, QEA-SOP, a novel evolutionary algorithm inspired by quantum, is presented to handle the sequential ordering problem (SOP) which is well known as a classical NP-Hard combinatorial proble

关键词： quantum-inspired evolutionary algorithm sequential ordering problem spreading and restraining principles pattern matrix

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Combinational Circuits Test Generation using quantum-inspired evolutionary algorithm

Combinational Circuits Test Generation using Quantum-inspire...

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ICEMI'2005第七届国际电子测量与仪器学术会议

作者： Peng Xiyuan Zhao Zhongyu Peng Yu (Harbin Institute of Technology, Harbin, 150001 China )

Neural network model of combinational circuit maps the problem of test vector searching to the problem of calculating neural network minimum energy and formulates the question of test generation as a

关键词： Test generation quantum-inspired evolutionary algorithm combinational circuit.

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A quantum-inspired evolutionary computing algorithm for disk allocation method

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2003年第3期E86D卷 645-649页

作者： Kim, KH Hwang, JY Han, KH Kim, JH Park, KH Korea Adv Inst Sci & Technol Comp Engn Res Lab Dept Elect Engn & Comp Sci Div Elect EngnYuseong Gu Taejon 305701 South Korea Korea Adv Inst Sci & Technol Robot Intelligence Technol Lab Dept Elect Engn & Comp Sci Div Elect EngnYuseong Gu Taejon 305701 South Korea

Based on a quantum-inspired evolutionary algorithm (QEA), a new disk allocation method is proposed for distributing buckets of a binary cartesian product file among unrestricted number of disks to maximize concurrent disk I/O. It manages the probability distribution matrix to represent the qualities of the genes. Determining the excellent genes quickly makes the proposed method have faster convergence than DAGA. It gives better solutions and 3.2 - 11.3 times faster convergence than DAGA.

关键词： partial match queries optimal disk allocation quantum-inspired evolutionary algorithm

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quantum-inspired evolutionary Multicast algorithm

Quantum-Inspired Evolutionary Multicast Algorithm

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IEEE International Conference on Systems, Man and Cybernetics

作者： Li, Yangyang Zhao, Jingjing Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9781424427932

As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of "prematurity" makes it difficult to get a good multicast tree. A quantum-inspired evolutionary algorithm (QEA) to deal with multicast routing problem is presented in this paper, which saliently solves the "prematurity" problem in Genetic based multicast algorithm. Furthermore, in QEA, the individuals in a population are represented by multistate gene quantum bits and this representation has a better characteristic of generating diversity in population than any other representations. In the individual's updating, the quantum rotation gate strategy is applied to accelerate convergence. The algorithm has the property of simple realization and flexible control. The simulation results show that QEA has a better performance than CS and conventional GA.

关键词： genetic algorithm multicast quantum-inspired evolutionary algorithm

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A quantum-inspired evolutionary Clustering algorithm

A Quantum-inspired Evolutionary Clustering Algorithm

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International Conference on Fuzzy Theory and Its Applications (iFUZZY)

作者： Tsai, Chun-Wei Liao, Yu-Hsun Chiang, Ming-Chao Chia Nan Univ Pharm & Sci Dept Appl Informat & Multimedia Tainan 71710 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan

ISBN: (纸本)9781479903863

inspired by the concept and principles of quantum computing, the classical quantum-inspired evolutionary algorithm (QEA) provides a useful way to find out the approximate solution of many optimization problems. However, compared with other heuristic algorithms, the slow convergence speed of QEA has been an important issue when it is applied to solve the optimization problems. As such, an improved version, called fast quantum-inspired evolutionary algorithm (FQEA), is proposed in this paper. By adding a fast repair facility, the proposed algorithm can not only accelerate the convergence speed of the search process of QEA, it can also provide a better result than QEA. Experimental results show that the proposed algorithm FQEA can provide a better result than those obtained by QEA and k-means algorithm.

关键词： evolutionary algorithm quantum-inspired evolutionary algorithm clustering

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A novel hybrid quantum-inspired evolutionary algorithm for permutation flow-shop scheduling

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JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2009年第6期12卷 1165-1182页

作者： Zheng, Tianmin Yamashiro, Mitsuo Ashikaga Inst Technol Dept Ind & Informat Syst Engn Ashikaga Tochigi 3268558 Japan

This paper mainly studies the application of a novel Hybrid quantum-inspired evolutionary algorithm (HQEA) called DV HQEA (HQEA based on Differential evolution and Variable neighborhood search) in permutation flow-shop scheduling problem (PFSSP). In this new method, a simple representation method to determine job sequence based on Q-bit's probability amplitude information is proposed for PFSSP firstly. Then the quantum chromosomes are encoded and decoded by using the quantum rotating angle rather than the two probability amplitude strings which are widely adopted in the conventional QEAs. Also, we merge the advantages of Differential Evolution (DE) strategy, Variable Neighborhood Search (VNS) and QEA, and propose the new DV HQEA. By adopting the DE to perform the updating of quantum gate and VNS to perform the local search, we can obtain high performance. Simulation results and comparisons with other algorithms such as NEH heuristic and QEA based on famous benchmarks show the effectiveness of the proposed DV HQEA.

关键词： Flow-shop scheduling quantum-inspired evolutionary algorithm differential evolution variable neighborhood search

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quantum-inspired evolutionary algorithms: a survey and empirical study

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JOURNAL OF HEURISTICS 2011年第3期17卷 303-351页

作者： Zhang, Gexiang SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

关键词： quantum-inspired evolutionary algorithm evolutionary computation quantum computing Optimization

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