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quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits

NEUROCOMPUTING 2008年第1-3期72卷 581-591页

作者： Li, Panchi Li, Shiyong Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Heilongjiang Peoples R China Daqing Petr Inst Dept Comp Sci & Engn Daqing 163318 Peoples R China

A novel quantum-inspired evolutionary algorithm is proposed based on the Bloch coordinates of quantum bits (qubits) in this paper. The chromosome is comprised of qubits whose Bloch coordinates comprise gene chain. The quantum chromosomes are updated by quantum rotation gates, and are mutated by quantum non-gates. For the rotation direction of quantum rotation gates, a simple determining method is proposed. For the rotation and mutation of qubits, two new operators are constructed based on Bloch coordinates of qubits. In this algorithm, the Bloch coordinates of each qubit are regarded as three paratactic genes, each chromosome contains three gene chains, and each gene chain represents an optimization solution, which can accelerate the convergence process for the same number of chromosomes. By two application examples of function extremum and neural network weights optimization, the simulation results show that the approach is superior to common quantum evolutionary algorithm and simple genetic algorithm in both search capability and optimization efficiency. (c) 2007 Elsevier B.V. All rights reserved.

关键词： quantum-inspired evolutionary algorithm Bloch coordinates Three gene chains encoding quantum rotation gate Optimization 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 algorithm Based on P systems for Knapsack Problem

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FUNDAMENTA INFORMATICAE 2008年第1期87卷 93-116页

作者： Zhang, Ge-Xiang Gheorghe, Marian Wu, Chao-Zhong SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Wuhan Univ Technol Minist Educ Engn Res Ctr Transportat Safety Wuhan 430063 Hubei Peoples R China

This paper introduces an evolutionary algorithm which uses the concepts and principles of the quantum-inspired evolutionary approach and the hierarchical arrangement of the compartments of a P system. The P system framework is also used to formally specify this evolutionary algorithm. Extensive experiments are conducted on a well-known combinatorial optimization problem, the knapsack problem, to test the effectiveness of the approach. These experimental results show that this evolutionary algorithm performs better than quantum-inspired evolutionary algorithms, for certain arrangements of the compartments of the P system structure utilized.

关键词： quantum-inspired evolutionary algorithm P systems 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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Distribution Network Reconfiguration Using an Approximate Optimization Approach

Distribution Network Reconfiguration Using an Approximate Op...

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International Conference on Sensors, Measurement and Intelligent Materials (ICSMIM 2012)

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

ISBN: (纸本)9783037856529

Power network reconfiguration is an important process in the improvement of operating conditions of a power system and in planning studies, service restoration and distribution automation when remote-controlled switches are employed. This paper presents the use of a quantum-inspired evolutionary algorithm to solve the distribution network reconfiguration problem. The quantum-inspired evolutionary algorithm is the combination product of quantum computing and evolutionary computation and is suitable for a class of integer programming problems such as the distribution network reconfiguration problem. After the analysis and formulation of the distribution network reconfiguration problem, the effectiveness and feasibility of the introduced method is verified by a large number of experiments.

关键词： power system distribution network reconfiguration optimization quantum-inspired evolutionary algorithm

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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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quantum evolutionary Membrane algorithm and Its Application for Economic Dispatch in Power System with Wind Farms

Quantum Evolutionary Membrane Algorithm and Its Application ...

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The Second Asian Conference on Membrane Computing(ACMC2013)

作者： Wenjin Luo Li Zhang Manuel Alcazar-Ortega Alexander Domijan Electrical engineering department University at Buffalo NY USA School of Electrical and Information Engineering Xihua Universtiy

Wind is a clean, renewable and reliable energy source and analysis of the economics of wind farms in terms of the cost/MWh energy provided is a primer topic in electricity economics. This paper presents a novel evolutionary algorithm named as quantum evolutionary membrane algorithm, which combines the concept and theory of quantum-inspired evolutionary algorithm and membrane computation, to solve the economic dispatch problem in power system with on land and offshore wind farms. The case derived from real data is used for simulation tests. Another conventional evolutionary algorithm is also used to solve the same problem for comparison. The experimental results show that the proposed algorithm is quick and accurate to obtain the optimal solution which is the minimum cost for electricity supplied by wind farm system.

关键词： membrane computation economic dispatch on land wind farm offshore wind farm quantum-inspired evolutionary algorithm

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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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QEAM: an Approximate algorithm Using P Systems with Active Membranes

QEAM: an Approximate Algorithm Using P Systems with Active M...

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The Second Asian Conference on Membrane Computing(ACMC2013)

作者： Gexiang Zhang Jixiang Cheng Marian Gheorghe Florentin Ipate School of Electrical Engineering Southwest Jiaotong University Department of Computer Science The University of Sheffield Regent Court Portobello StreetSheffield S1 4DP UK Faculty of Mathematics and Computer Science University of Bucharest Academiei 14Bucharest Romania Faculty of Mathematics and Computer Science University of Pite stiTrgu din Vale 1 110040 Pite sti Romania

This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compartments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark satisfiability problems show that QEAM outperforms QEPS (quantum-inspired evolutionary algorithm based on P systems) and its counterpart quantum-inspired evolutionary algorithm.

关键词： Membrane computing active membranes approximate optimization approach quantum-inspired evolutionary algorithm satisfiability problem

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