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A robust automatic phase-adjustment method for financial forecasting

KNOWLEDGE-BASED SYSTEMS 2012年 27卷 245-261页

作者： Araujo, Ricardo de A. Univ Fed Pernambuco Informat Ctr Recife PE Brazil

In this work we present the robust automatic phase-adjustment (RAA) method to overcome the random walk dilemma for financial time series forecasting. It consists of a hybrid model composed of a qubit multilayer perceptron (QuMLP) with a quantum-inspired evolutionary algorithm (QIEA), which is able to evolve the complete QuMLP architecture and parameters, as well as searches for the best time lags to optimally describe financial phenomena. In the attempt to improve the QuMLP parameters supplied by QIEA, each individual of the QIEA population is further trained by the complex back-propagation (CBP) algorithm. Also, for each forecasting model generated, we use a phase fix procedure to adjust time phase distortions that appear in financial time series. Furthermore, an experimental analysis is conducted with the proposed method through six real world financial time series, and the obtained results are discussed and compared to results found with the best methods recently presented in the literature. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Qubit multilayer perceptron quantum-inspired evolutionary algorithm Hybrid models Financial forecasting Random walk dilemma

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A membrane algorithm with quantum-inspired subalgorithms and its application to image processing

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NATURAL COMPUTING 2012年第4期11卷 701-717页

作者： Zhang, Gexiang Gheorghe, Marian Li, Yuquan SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Univ Pitesti Dept Comp Sci Pitesti 110040 Romania

This paper presents a membrane algorithm, called MAQIS, by appropriately combining concepts and principles of membrane computing and quantum-inspired evolutionary approach. MAQIS has four distinct features from the membrane algorithms reported in the literature: initial solutions are only inside the skin membrane;different regions separated by membranes have different components of the algorithm;all the components inside membranes cooperate to produce offspring in a single evolutionary generation;communication rules are performed in a single evolutionary step. Extensive experiments conducted on knapsack problems show that MAQIS outperforms five counterpart approaches and our previous work. The effectiveness of MAQIS is also verified in the application of image processing.

关键词： Membrane computing Membrane algorithm quantum-inspired evolutionary algorithm Knapsack problem Image sparse decomposition

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Free Search with Adaptive Differential Evolution Exploitation and quantum-inspired Exploration

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2012年第3期35卷 1035-1051页

作者： Yin, Jihao Wang, Yifei Hu, Jiankun Univ New S Wales Australian Def Force Acad Sch Engn & Informat Technol Univ Coll Canberra ACT 2600 Australia Beihang Univ Sch Astronaut Beijing 100191 Peoples R China

Recently an interesting evolutionary mechanism, sensibility, inherited from a concept model of Free Search (FS) was introduced and used for solving network problems. Unfortunately, the original FS is not easy to implement because it requires key knowledge that is not clearly defined in the existing literature to determine the neighborhood space that profoundly affects the performance of the original FS. This paper thus designs a new implementation for the concept model of FS, and proposes a new algorithm, called Free Search with Adaptive Differential Evolution Exploitation and quantum-inspired Exploration (ADEQFS) to address this issue. In ADEQFS, we focus on designing a new mutation strategy by employing adaptive differential evolution techniques as well as concepts and principles from real-coded quantum-inspired evolutionary algorithm. In addition, we use the crossover operation from the traditional Differential Evolution scheme to alleviate the premature convergence for the proposed algorithm. Furthermore, we employ the greedy mechanism to preserve the best solutions found at each generation. The convergence analysis of the proposed algorithm is also presented in this paper. We give the proof of convergence by using the Markov chain model. Thirty-four optimization test functions with different mathematical characteristics are employed as benchmark set to test the performance of ADEQFS. The numerical results highlight the improved convergence rate and computation reliability. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： Free search Adaptive differential evolution quantum-inspired evolutionary algorithm Global optimization evolutionary computation

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

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

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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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An Improved quantum Evolution algorithm and Performance Analysis

An Improved Quantum Evolution Algorithm and Performance Anal...

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2012年计算机应用与系统建模国际会议

作者： Zhang Yi Yang Xiuxia Zhou Shaolei Department of Control Engineering Naval Aeronautical and Astronautical University

To tackle the shortcoming of deficient using of feedback information in quantum-inspired evolutionary computing and catastrophe such as quantum mutation, the improved quantum-inspired evolutionary algorithm (IQEA) is proposed. The crossover and mutation operators of genetic algorithm are introduced to the quantum evolution algorithm, at the same time, the better individuals information is applied to the generation of the population applying the probability evaluation model of the estimation of distribution algorithm. Theory analyze and test function simulation experiments show that the improved quantum genetic algorithm is characterized by rapid convergence , excellent robustness and so on.

关键词： Multiple quantum computation quantum-inspired evolutionary algorithm genetic optimization estimation of distribution

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Time-Frequency Atom Decomposition with quantum-inspired evolutionary algorithms

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2010年第2期29卷 209-233页

作者： Zhang, Ge-Xiang SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China

Time-frequency atom decomposition (TFAD) provides a flexible representation for non-stationary signals, but the extremely high computational effort greatly blocks its practical applications. quantum-inspired evolutionary algorithms (QEA) are efficient optimization methods with strong search capability and rapid convergence. This paper proposes the application of a modified variant of QEA to the TFAD problem. The problem on TFAD with evolutionary algorithms is formulated. By using gray coding, elite groups, and an appropriate termination criterion, the modified QEA is developed to search the suboptimal time-frequency atoms from a very large and redundant time-frequency dictionary. Also, this paper discusses the reduction of the computational time in terms of parameter setting, and presents an application example of radar emitter signals. Extensive experiments show the effectiveness and practicability of the presented algorithm.

关键词： Time-frequency atom decomposition quantum-inspired evolutionary algorithm Optimization Radar emitter signal

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Classical and quantum-inspired Tabu Search for Solving 0/1 Knapsack Problem

Classical and Quantum-Inspired Tabu Search for Solving 0/1 K...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Chou, Yao-Hsin Yang, Yi-Jyuan Chiu, Chia-Hui Natl Chi Nan Univ Puli 54561 Taiwan

ISBN: (纸本)9781457706530

In this paper, we propose a novel quantum-inspired evolutionary algorithm, called quantum-inspired Tabu search (QTS). QTS is based on the classical Tabu search and the characteristic of quantum computation, such as superposition. We will present how we implement QTS to solve 0/1 knapsack problem. Furthermore, the results of experiment are also compared with the original quantum-inspired evolutionary algorithm (QEA)' and the other heuristic algorithms' experimental results. The final outcomes shows that QTS performs much better than the others on 0/1 knapsack problem, without premature convergence and more efficiency.

关键词： quantum computing combinatorial optimization quantum-inspired evolutionary algorithm tabu search knapsack problem

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Solving flow shop scheduling problems by quantum differential evolutionary algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第5-8期49卷 643-662页

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

This paper proposed a novel quantum differential evolutionary algorithm (QDEA) based on the basic quantum-inspired evolutionary algorithm (QEA) for permutation flow shop scheduling problem (PFSP). In this QDEA, the quantum chromosomes are encoded and decoded by using the quantum rotating angle and a simple strategy named largest rotating angle value rule to determine job sequence based on job's quantum information is proposed for the representation of PFSP, firstly. Then, we merge the advantages of differential evolution strategy, variable neighborhood search and QEA by adopting the differential evolution to perform the updating of quantum gate and variable neighborhood search to raise the performance of the local search. We adopted QDEA to minimize the makespan, total flowtime and the maximum lateness of jobs and make the simulations. The results and comparisons with other algorithms based on famous benchmarks demonstrated the effectiveness of the proposed QDEA. Another contribution of this paper is to report new absolute values of total flowtime and maximum lateness for various benchmark problem sets.

关键词： Permutation flow shop scheduling quantum-inspired evolutionary algorithm Differential evolution Variable neighborhood search

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GFSOP Based Ternary quantum Logic Synthesis

GFSOP Based Ternary Quantum Logic Synthesis

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Conference on Optics and Photonics for Information Processing IV

作者： Khan, Mozammel H. A. East West Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh

ISBN: (纸本)9780819482938

quantum technology is one of the most promising technologies for future computing systems, since quantum algorithms solve problems much more efficiently than classical algorithms. All quantum algorithms are made up of quantum logic circuits. A quantum logic circuit is made up of quantum gates (reversible in nature) and is designed using reversible logic synthesis methods. For a given Hilbert space, ternary quantum system requires 0.63 times qutrits than the corresponding number of qubits. Thus, the ternary quantum system provides a much more compact and efficient information encoding. Beside other technologies, ternary quantum logic system can be realized using photon polarization. These advantages of ternary quantum system open avenue for developing ternary quantum algorithms. Galois field sum of products (GFSOP) based synthesis of ternary quantum logic circuit is the most practical approach, since any ternary logic function with many inputs can be represented as GFSOP expression and the GFSOP expression can be implemented as cascade of ternary quantum gates. Here we discuss minimization of ternary logic function as GFSOP expression using quantum-inspired evolutionary algorithm. We also discuss a method of realization of ternary GFSOP expression using ternary quantum gates. Experimental results are given to show the effectiveness of the ternary GFSOP minimization technique.

关键词： Galois field sum of products expression optical quantum system quantum-inspired evolutionary algorithm ternary quantum computation ternary quantum gates ternary quantum logic synthesis

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A method for fault diagnosis of analog circuits based on optimized fuzzy inference

A method for fault diagnosis of analog circuits based on opt...

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Pacific-Asia Conference on Circuits, Communications and Systems, PACCS

作者： Zhiyong Luo Min Xiang Haofei Xie Ping Wang Key Laboratory ofNetwork control & Intelligent Instrument Ministry ofEducation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781424479696

Because of the difficulty of disposing of fuzzy inference rules for fault diagnosis, a systematic approach for fault diagnosis of analog circuits based on optimized fuzzy inference is presented. The fuzzy logic system for based on fuzzy inference analog circuits fault diagnosis, quantum-inspired evolutionary algorithm (QEA) is used to optimize the membership function of the rules of the fault diagnosis fuzzy logic system, then self adapted genetic algorithm to select the optimum fuzzy rule aggregate, so the number of fuzzy rules is decreased to make it easy to dispose fault diagnosis of analog circuits. The simulation results of a analog power magnifier circuits show the fault diagnosis method of the analog circuits with optimized fuzzy inference is effective.

关键词： adapted genetic algorithm analog circuits fault diagnosis fuzzy inference quantum-inspired evolutionary algorithm Fault diagnosis analog circuits Fuzzy inference Fuzzy logic Fuzzy rules fuzzy logic system Membership functions

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