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检索条件"主题词=Quantum-inspired evolutionary algorithm"

共 89 条记录，以下是21-30 订阅

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Real-observation quantum-inspired evolutionary algorithm for a class of numerical optimization problems

Real-observation quantum-inspired evolutionary algorithm for...

引用

7th International Conference on Computational Science (ICCS 2007)

作者： Zhang, Gexiang Rong, Haina Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

ISBN: (纸本)9783540725893

This paper proposes a real-observation quantum-inspired evolutionary algorithm (RQEA) to solve a class of globally numerical optimization problems with continuous variables. By introducing a real observation and an evolutionary strategy, suitable for real optimization problems, based on the concept of Q-bit phase, RQEA uses a Q-gate to drive the individuals toward better solutions and eventually toward a single state corresponding to a real number varying between 0 and 1. Experimental results show that RQEA is able to find optimal or close-to-optimal solutions, and is more powerful than conventional real-coded genetic algorithm in terms of fitness, convergence and robustness.

关键词： evolutionary computation quantum-inspired evolutionary algorithm real observation numerical optimization

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A Real-Coded Multi-Objective quantum-inspired evolutionary algorithm and its Application 29

A Real-Coded Multi-Objective Quantum-Inspired Evolutionary A...

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第29届中国控制与决策会议

作者： Li Yong Wu Xiaohong Zhang Yuxian Shenyang University of Technology

ISBN: (纸本)9781509046584

A real-coded multi-objective quantum evolutionary algorithm is proposed with the extension of a real-coded single-objective quantum evolutionary algorithm,and it is applied to the optimization design problem of low voltage large current vehicle-based *** update mechanism and intelligent search mechanism are improved for multi-objective problem *** crowd distance and population mutation are also taken into *** proposed algorithm is applied to test functions with two objectives and three *** shows better performance both in convergence and distribution compared with *** to design requirements,motor efficiency and material costs are selected as objective functions,and then the multi-objective model of low voltage large current vehicle-based generator is *** the proposed algorithm to the model,better designing schemes are obtained compared with original *** validity of the algorithm is proved.

关键词： Multi-objective optimization quantum-inspired evolutionary algorithm Motor optimization design

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A Comprehensive Learning quantum-inspired evolutionary algorithm

A Comprehensive Learning Quantum-Inspired Evolutionary Algor...

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International Conference on Information and Business Intelligence (IBI 2011)

作者： Qin, Yanhui Zhang, Gexiang Li, Yuquan Zhang, Huishen SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

ISBN: (纸本)9783642290862

This paper proposes a Comprehensive Learning quantum-inspired evolutionary algorithm (CLQEA) by introducing the philosophy of comprehensive learning into quantum-inspired evolutionary algorithms. In CLQEA, each individual in a population learns not only from its own best historical solution searched, but also from the best solutions that other individuals found. This idea is very helpful to enhance population diversity through applying a group of elite individuals to perform Q-gates to produce offspring. Extensive experiments carried out on knapsack problems with various items show that CLQEA outperforms several QIEAs recently reported in the literature.

关键词： Comprehensive Learning quantum-inspired evolutionary algorithm knapsack problem Combinatorial Optimization

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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...

引用

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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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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A Novel quantum-inspired evolutionary algorithm Based on Variable Angle-Distance Rotation

A Novel Quantum-Inspired Evolutionary Algorithm Based on Var...

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2010 IEEE World Congress on Computational Intelligence

作者： Liu, Wenjie Chen, Hanwu Yan, Qiaoqiao Liu, Zhihao Xu, Juan Zheng, Yu Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China Nanjing Univ Informat Sci & Tech Sch Comp & Software Nanjing 210044 Peoples R China

ISBN: (纸本)9781424481262

By reviewing the original INIQGA algorithm, an improved algorithm (IINIQGA) is put forward by revising the lookup table. In addition, By introducing the variable angle-distance rotation method into the update Q(t) procedure, a novel quantum-inspired evolutionary algorithm, QEA-VAR, was proposed. Compared with previous algorithms, our update Q(t) procedure is more simple and feasible. Finally, the corresponding experiments on the 0-1 knapsack problem were carried out, and the results show that our improvement is efficient, and comparing with IINIQGA, QEA, and CGA, QEA-VAR has a faster convergence and better profits than other algorithms.

关键词： quantum-inspired evolutionary algorithm variable angle-distance rotation 0/1 knapsack problem

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An quantum-inspired evolutionary algorithm applied to design optimizations of electromagnetic devices

An quantum-inspired evolutionary algorithm applied to design...

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15th International Symposium on Applied Electromagnetics and Mechanics (ISEM)

作者： Zhang, Wei Xu, Hailiang Bai, Yanan Yang, Shiyou Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

To eliminate the deficiency of traditional evolutionary algorithms in numerical implementations with respect to the three key operators such as selection, crossover and mutation operators, a new evolutionary algorithm based on probabilistic models, the quantum-inspired evolutionary algorithm (QEA), is studied for design optimizations of electromagnetic devices. In the proposed QEA, an adaptive update formulation is proposed for the rotation angle to balance the exploration and exploiting searches while the two level information sharing is simplified to one level one to facilitate the implementation of the proposed algorithm. Also, a new information sharing mechanism is introduced. The proposed algorithm is evaluated on an engineering inverse problem with promising results.

关键词： quantum-inspired evolutionary algorithm inverse problem evolutionary algorithm based on probabilistic models

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The immune quantum-inspired evolutionary algorithm

The immune quantum-inspired evolutionary algorithm

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IEEE International Conference on Systems, Man and Cybernetics

作者： Li, Y Zhang, YN Zhao, RC Jiao, LC Northwestern Polytech Univ Sch Comp Xian 710072 Peoples R China

ISBN: (纸本)0780385667

By leading immune concepts and methods into quantum-inspired evolutionary algorithm (QEA), a novel algorithm, the immune quantum-inspired evolutionary algorithm (IQEA), is proposed. On condition of preserving QEA's advantages, IQEA utilizes some characteristics and knowledge in the pending problems for restraining the repeat and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQEA is superior to the conventional EA (CEA), the immune EA (IEA) and QEA.

关键词： evolutionary algorithm quantum-inspired evolutionary algorithm immune operator knapsack problem

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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

作者： Luo, Zhiyong Zhang, Wenfeng Zhang, Yougang Xiang, Min Piao, Changhao Chongqing Univ Posts & Telecommun Coll Automat Chongqing 400065 Peoples R China Northwestern Polytech Univ Coll Automat Xian 710072 Peoples R China

ISBN: (纸本)9781424421138

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 mufti-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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Chaotic quantum-inspired evolutionary algorithm: enhancing feature selection in BCI

Chaotic Quantum-inspired Evolutionary Algorithm: enhancing f...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ramos, Alimed Celecia Vellasco, Marley Pontificia Univ Catolica Rio de Janeiro Elect Engn Dept Rio de Janeiro Brazil

ISBN: (纸本)9781728169293

quantum-inspired evolutionary algorithms (QiEAs) have demonstrated to be very effective in several applications. In particular, employing this algorithm for feature selection as a wrapper technique in Brain-Computer Interfaces applications was recently proposed with great results. Moreover, the training time of the model was decreased while maintaining a high classification accuracy, both essential conditions for a successful BCI. The drawback of this model was the sensitiveness to changes in the direction and magnitude of the rotation angle, which can produce adverse effects in both performance and convergence time. Chaotic systems and evolutionary algorithms, when combined, can enhance the convergence rate and speed of the evolutionary process, incrementing the capacity of reaching the global optima. In this paper we explore the effects of adding ergodicity to a QiEA by the employment of chaotic maps in two operators: chaotic uniform crossover and chaotic quantum update gate. To validate the proposed approach, six commonly used chaotic maps arc tested with data of Motor Imagery (MI) Electroencephalography (EEG) of right and left hand movement. The results of these experiments are compared with the ones of a QiEA and a classical Genetic algorithm (GA). In the proposed model, Wavelet Packet Decomposition is employed as the time-frequency analysis to characterize the signal, whereas a Multilayer Perceptron Neural Network is used as a classifier. The results demonstrated that Chaotic QiEAs can significantly improve the convergence time of the model with only a small loss in the final accuracy.

关键词： Feature selection Chaotic maps quantum-inspired evolutionary algorithm BCI applications Motor Imagery classification

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