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10th IEEE International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Moriyama, Yoshifumi Iimura, Ichiro Nakayama, Shigeru Prefectural Univ Kumamoto Fac Adm Studies Higashi Ku 3-1-100 Tsukide Kumamoto 8628502 Japan Kagoshima Univ 1-21-40 Korimoto Kagoshima 8900065 Japan

ISBN: (纸本)9781538604694

The quantum-inspired evolutionary algorithm (QEA) and QEA with a pair-swap strategy (QEAPS), where each gene is represented by a quantum bit (qubit), and the qubit is updated by a unitary transformation in both algorithms. QEA and QEAPS can automatically shift from a global search to a local search and have shown superior search performance to the classical genetic algorithm. However, it is important to appropriately manage the convergence speed of qubits. In this study, we have proposed a measure that can confirm convergence state of qubits, and applied the measure to a method for keeping diversity. From the results of the computational experiment in the maximum cut problem, we have clarified that the proposed measure can confirm the state of the qubit, and the discovery rate of the optimal solution improves with to apply the method for maintenance of diversity.

关键词： quantum-inspired evolutionary algorithm QEA QEA with pair-swap strategy QEAPS maximum cut problem graph optimization

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An effective hybrid quantum-inspired evolutionary algorithm for parameter estimation of chaotic systems

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第2期37卷 1279-1285页

作者： Wang, Ling Li, Ling-po Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China

Parameter estimation of chaotic systems is an important issue and has attracted increasing interest from a variety of research fields. Recently, quantum-inspired evolutionary algorithms have been proposed and applied to some optimization problems. However, to the best Of Our knowledge, there is no published research work oil quantum-inspired evolutionary algorithm (QEA) for estimating parameters of chaotic systems In this paper, ail effective hybrid quantum-inspired evolutionary algorithm with differential evolution (HQEDE) is proposed and applied to estimate the parameters of the Lorenz system. Numerical simulation and comparisons with other methods demonstrate the effectiveness and robustness of the proposed algorithm In addition. the effects of the parameter settings on HQEDE are investigated (C) 2009 Elsevier Ltd All rights reserved

关键词： Parameter estimation quantum-inspired evolutionary algorithm Differential evolution Hybrid algorithm Chaotic system

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Application of quantum-inspired evolutionary algorithm to Reduce PAPR of an OFDM Signal Using Partial Transmit Sequences Technique

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IEEE TRANSACTIONS ON BROADCASTING 2010年第1期56卷 110-113页

作者： Chen, Jung-Chieh Natl Kaohsiung Normal Univ Dept Optoelect & Commun Engn Kaohsiung 802 Taiwan

This paper proposes a reduced-complexity partial transmit sequences (PTS) approach based on the quantum-inspired evolutionary algorithm (QEA) for the reduction of peak-to-average power ratio (PAPR) in an orthogonal frequency division multiplexing (OFDM) system. The conventional PTS technique improves the PAPR statistics for OFDM signals, but the considerable computational complexity for an exhaustive search over all combinations of allowed phase factors is a potential problem for practical implementation. To reduce the computational complexity while still obtaining the desirable PAPR reduction, we introduce the QEA, an effective algorithm that solves various combinatorial optimization problems, to search the optimal phase factors. The simulation results show that the proposed QEA achieves significant PAPR reduction with low computational complexity.

关键词： Orthogonal frequency division multiplexing partial transmit sequence peak-to-average power ratio quantum-inspired evolutionary algorithm

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Multiple-Case Outlier Detection in Multiple Linear Regression Model Using quantum-inspired evolutionary algorithm

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JOURNAL OF COMPUTERS 2010年第12期5卷 1779-1788页

作者： Akter, Salena Khan, Mozammel H. A. East West Univ Dept Comp Sci & Engn 43 Mohakhali C A Dhaka 1212 Bangladesh

In ordinary statistical methods, multiple outliers in multiple linear regression model are detected sequentially one after another, where smearing and masking effects give misleading results. If the potential multiple outliers can be detected simultaneously, smearing and masking effects can be avoided. Such multiple-case outlier detection is of combinatorial nature and sets of possible outliers need to be tested, where is the number of data points. This exhaustive search is practically impossible. In this paper, we have used quantum-inspired evolutionary algorithm (QEA) for multiple-case outlier detection in multiple linear regression model. A Bayesian information criterion based fitness function incorporating extra penalty for number of potential outliers has been used for identifying the most appropriate set of potential outliers. Experimental results with 10 widely referred datasets from statistical literature show that the QEA overcomes the effect of smearing and masking and effectively detects the most appropriate set of outliers.

关键词： Bayesian information criterion based fitness function multiple-case outlier detection multiple linear regression model quantum-inspired evolutionary algorithm

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Disruption Management for Predictable New Job Arrivals in Cloud Manufacturing

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JOURNAL OF INTELLIGENT SYSTEMS 2017年第4期26卷 683-695页

作者： Liu, Mi Yi, Shuping Wen, Peihan Song, Haicao Chongqing Univ Coll Mech Engn Shazheng St Chongqing 400044 Peoples R China Shihezi Univ Coll Mech & Elect Engn Xinjiang 832003 Peoples R China

Manufacturing resources are shared and centrally managed on the cloud platform in cloud manufacturing, which is a new model of modern manufacturing. The production data are collected, which can be used to predict the manufacturing events. Based on those, disruption problems of scheduling should be researched from a new point of view. In this paper, new job arrivals were considered as the disruption event. The time of the occurrence of disruption was predictable in contrast to uncertainty. Alternative subcontractors chosen from the cloud platform were available for outsourcing with different processing prices and transporting distances. The objective of the original scheduling, the deviation between the new schedule and the old one, and the outsourcing cost were all considered. To express the problem, mathematical models and a three-field notation model were constructed. To solve the problem, a hybrid quantum-inspired chaotic group leader optimization algorithm was proposed, in which a hybrid encoding way was applied. To verify the algorithm, experiments were carried out. The results showed that the proposed algorithm performs well.

关键词： Cloud manufacturing disruption management quantum-inspired evolutionary algorithm group leader optimization algorithm outsourcing

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Adaptive niche quantum-inspired immune clonal algorithm

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NATURAL COMPUTING 2016年第2期15卷 297-305页

作者： Liu, Jianyong Wang, Huaixiao Sun, Yangyang Li, Ling PLA Univ Sci & Technol Coll Field Engn Nanjing 210007 Jiangsu Peoples R China PLA Univ Sci & Technol Coll Def Engn Nanjing 210007 Jiangsu Peoples R China

The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effectively and make the function converge to as many as possible extreme value points. The quantum coding can better explore the solution space, the niche mechanism ensures the algorithm to converge to multi-extremum and the adaptive mechanism is introduced according to the characteristics of each procedure of the algorithm to improve the effect of the algorithm. Example analysis shows that the ANQICA is better in exploration and convergence. Therefore, the ANQICA can be used to solve the problem of multi-modal function optimization effectively.

关键词： Optimization algorithm quantum-inspired evolutionary algorithm Niche Adaptive Immune clonal algorithm

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Stochastic Optimal Dispatch of Power System Considering the Correlation of Multiple Wind Farm Outputs

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第6期44卷 616-627页

作者： Yang, Hongming Zhang, Yongxi Wang, Shuang Zhao, Junhua Lai, Mingyong Dong, Zhao Yang Huang, Jingjie Changsha Univ Sci & Technol Sch Elect & Informat Engn Hunan Prov Key Lab Smart Grids Operat & Control Hunan Prov Engn Res Ctr Elect Transportat & Smart Changsha 410114 Hunan Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Shishi Power Ltd Liabil Co Quanzhou Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Guangdong Peoples R China

As an important way of addressing energy and environmental challenges, the market share of wind power generation has increased dramatically in the past decade and has introduced significant challenges to power system operation. In this article, the tail correlation between multiple wind farms is studied. The joint probability distribution of multiple wind farms is estimated by employing the Gumbel copula function. Based on the estimated joint probability distribution, a stochastic optimal dispatch model is proposed to take into account the chance constraints of energy utilization from multiple wind farms in the power system. The sample average approximation method is employed to handle the chance constraints in the proposed model, so as to transform stochastic optimal dispatch into a deterministic non-linear optimization problem. The quantum-inspired evolutionary algorithm is used to solve the proposed model. The proposed model and algorithm are tested with comprehensive case studies to demonstrate their effectiveness.

关键词： stochastic optimal dispatch correlation between multiple wind farm outputs Gumbel copula function chance constraints sample average approximation 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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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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Solving No-Wait Flow Shop Scheduling Problems by a Hybrid quantum-inspired evolutionary algorithm

Solving No-Wait Flow Shop Scheduling Problems by a Hybrid Qu...

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9th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Zheng, Tianmin Yamashiro, Mitsuo SoftAgency Co Ltd Oyama Tochigi 3230824 Japan

ISBN: (纸本)9783642167720

This paper is the first to consider the hybrid quantum-inspired evolutionary algorithm (HQEA) on the no-wait permutation flow shop scheduling problem (PFSP) for minimizing the makespan. In this HQEA, the quantum chromosomes are encoded by using the quantum rotating angle and a simple but efficient converting mechanism for determining job sequence is proposed for the representation of solution firstly. Then, we adopt differential operation to perform the updating of quantum gate and the local search to perform exploitation in the promising permutation-based solutions. We make the simulations on famous benchmarks and the comparisons with other state-of-the-art approaches demonstrate the effectiveness of the proposed HQEA for no-wait flow shop scheduling problem.

关键词： quantum-inspired evolutionary algorithm no-wait permutation flow shop differential operation local search

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