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第25届中国控制与决策会议

作者： Ping Yan Ming-hai Jiao Li-qiang Zhao School of Economics and Management Shenyang Aerospace University Computing Center Northeastern University

ISBN: (纸本)9781467355339

The flowshop scheduling problem with fuzzy processing times is concerned in this paper. A triangular fuzzy number is used to represent the uncertainty processing times of jobs. The due windows have been assigned to all jobs. If a job is completed within its due window, then it incurs no scheduling cost. Otherwise, an earliness or tardiness cost is incurred. The objective is to find a job schedule such that the weighted sum of earliness and tardiness penalties of jobs is minimized. Schedules are generated by a proposed hybrid algorithm in the context of quantum evolutionary algorithm and particle swarm optimization approach. Three novel coding schemes are designed for transforming an individual into a sequence of jobs. Furthermore, a velocity disturbance strategy is also introduced into the proposed algorithm to improve the diversity of the swarm. The simulation results show that the proposed algorithm is able to obtain higher quality solutions stably and efficiently in the fuzzy flowshop scheduling problem.

关键词： Flowshop scheduling Fuzzy processing time Particle swarm optimization quantum evolutionary algorithm

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Solving a Single Machine Scheduling Problem with Uncertain Demand Using QPSO algorithms

Solving a Single Machine Scheduling Problem with Uncertain D...

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第25届中国控制与决策会议

作者： Ping Yan Ming-hai Jiao Xu Yao School of Economics and Management Shenyang Aerospace University Computing Center Northeastern University Institute Scientific & Technical Information of Liaoning Province

ISBN: (纸本)9781467355322

By considering the imprecise or fuzzy nature of the data in real-world problems, a single machine scheduling problem with uncertainty demand is investigated. A triangular fuzzy number is used to represent the uncertainty demand, and a half-trapezoid one is employed to represent fuzzy duedate. On the basis of the agreement index of fuzzy duedate and fuzzy completion time, this problem is formulated with the objective to maximize the total weighting agreement indexes for all the customer orders. We presented a hybrid algorithm QPSO of particle swarm optimization （PSO） and quantum evolutionary algorithm （QEA） to solve this problem. In the proposed QPSO, some novel coding schemes are designed for transforming a particle into a feasible process sequence of customer orders. Moreover, a mutation mechanism is also introduced into the QPSO and improves the diversity of the swarm greatly. The feasibility and effectiveness of the proposed QPSO is demonstrated by some simulation experiments.

关键词： Single machine scheduling Fuzzy demand Particle swarm optimization quantum evolutionary algorithm

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quantum Binary Shuffled Frog Leaping algorithm

Quantum Binary Shuffled Frog Leaping Algorithm

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3rd International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC)

作者： Wang, Lianguo Gong, Yaxing Gansu Agr Univ Coll Informat Sci & Technol Lanzhou Peoples R China Gansu Agr Univ Coll Engn Lanzhou Peoples R China

ISBN: (纸本)9780769551227

A quantum binary shuffled frog leaping algorithm (QBSFLA) is proposed through integrating the theories of the quantum evolutionary algorithm and the shuffled frog leaping algorithm (SFLA). Firstly, the superposition state characteristic of quantum makes the separate individual expresses more states, and the probability expression characteristic makes individuals' states are expressed with certain probability for increasing the diversity of the population potentially. Then, SFLA is used to regulate the phrase of the quantum bit to achieve a balance of the local and global search and improve the run speed. The experimental results of three 0-1 knapsack problems solved by QBSFLA with the greedy algorithm show that QBSFLA has several advantages such as fast convergence, powerful global search ability and good stability.

关键词： swarm intelligence shuffled frog leaping algorithm quantum evolutionary algorithm knapsack problem greedy algorithm binary system

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Improved quantum evolutionary algorithm for combinatorial optimization problem

Improved quantum evolutionary algorithm for combinatorial op...

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6th International Conference on Machine Learning and Cybernetics

作者： Zhang, Rui Gao, Hui Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China Harbin Inst Technol Sch Commun Sci & Engn Harbin 150090 Peoples R China

ISBN: (纸本)9781424409723

The method of calculating rotation angle of quantum rotation gate plays an important role to the performance of quantum evolutionary algorithm (QEA). This paper proposes an improved quantum evolutionary algorithm (IQEA), whose core is that a new approach of adaptive calculating rotation angle of quantum rotation gate is designed on the basis of the probability amplitude ratio of the corresponding states. Rapid convergence and good global search capability characterize the performance of IQEA. Based on a typical combinatorial optimization problem - 0/1 knapsack problems, the influence of the relative parameter to the performance of IQEA is demonstrated, and then comparing experiments have been done. The results show that IQEA is superior to the previous quantum evolutionary algorithm.

关键词： quantum evolutionary algorithm improved quantum evolutionary algorithm quantum rotation gate combinatorial optimization knapsack problem

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Real-coded quantum evolutionary algorithm for complex functions with high-dimension

Real-coded quantum evolutionary algorithm for complex functi...

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IEEE International Conference on Mechatronics and Automation

作者： Zhang, Rui Gao, Hui Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China Harbin Inst Technol Sch Commun Sci & Engn Harbin 150090 Peoples R China

ISBN: (纸本)9781424408276

For optimzing complex functions with high-dimension, a real-coded quantum evolutionary algorithm (RCQEA) is proposed on the basis of the concept and principles of quantum computing such as qubits and superposition of states. Firstly, in this algorithm, real-coded triploid chromosomes, whose alleles are composed of real variable and a pair of probability amplitudes of the correspinding states of one qubit, are constructed to keep the diversity of solution. Secondly, complementary double mutation operator (CDMO), which is designed according to a pair of probability amplitudes of the correspinding states of one qubit satisfying the normalization condition, as well as quantum rotation gate (QRG) are used to update chromosomes, which can treat the balance between exploration and exploitation. Thirdly, discrete crossover (DC) is employed to expand search space. Finally, "Hill-climbing" selection (HCS) is adopted to accelerate the convergence speed. Simulation results on 4 benchmark complex functions with high-dimension show that RCQEA is not only effective, efficient, but also very adaptive to the dimensions, and has the characteristics of rapider convergence, more powerful global search capability and better stability.

关键词： quantum computation quantum evolutionary algorithm real-coded quantum evolutionary algorithm function optimization

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quantum evolutionary algorithm based on immune theory for multi-modal function optimization

Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemic...

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Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemical Universities 2007年第3期20卷 45-49页

作者： You, Xiao-Ming Liu, Sheng Shuai, Dian-Xun College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai 201620 China Department of Computer Science and Technology East China University of Science and Technology Shanghai 200237 China

A novel quantum evolutionary algorithm based immune mechanism (MIQEA) for solving function optimization containing multiple global optima was proposed. By niche methods the original population was divided into subpopulations automatically, and then local search was carried by the immune mechanism in which antibody can be clone selected, immune cell can accomplish cross-mutation, memory cells can be produced and similar antibodies can be suppressed for all subpopulations, each subpopulation can obtain optimal solutions. The algorithm can maintain all optimal solutions. The quantum evolutionary algorithm with intrinsic parallelism is integrated with adaptive immune dynamic model, it not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed and has been able to get the global optimal and sub-optimal solutions rapidly. The convergence of the MIQEA was proved;its superiority is shown by some simulation experiments.

关键词： Cross-mutation Immune operator Multi-modal function optimization quantum evolutionary algorithm

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IMPROVED quantum evolutionary algorithm FOR COMBINATORIAL OPTIMIZATION PROBLEM

IMPROVED QUANTUM EVOLUTIONARY ALGORITHM FOR COMBINATORIAL OP...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： RUI ZHANG HUI GAO School of Automation Harbin University of Science and Technology Harbin 150080 China School of Communication Science and Engineering Harbin Institute of Technology Harbin 150090 Chin

The method of calculating rotation angle of quantum rotation gate plays an important role to the performance of quantum evolutionary algorithm (QEA).This paper proposes an improved quantum evolutionary algorithm (IQEA), whose core is that a new approach of adaptive calculating rotation angle of quantum rotation gate is designed on the basis of the probability amplitude ratio of the corresponding *** convergence and good global search capability characterize the performance of *** on a typical combinatorial optimization problem-0/1 knapsack problems, the influence of the relative parameter to the performance of IQEA is demonstrated, and then comparing experiments have been *** results show that IQEA is superior to the previous quantum evolutionary algorithm.

关键词： quantum evolutionary algorithm Improved quantum evolutionary algorithm quantum rotation gate Combinatorial optimization Knapsack problem

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A New quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2010年第3期25卷 1486-1495页

作者： Jeong, Yun-Won Park, Jong-Bae Jang, Se-Hwan Lee, Kwang Y. Konkuk Univ Dept Elect Engn Seoul 143701 South Korea Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

This paper proposes a new binary particle swarm optimization (BPSO) approach inspired by quantum computing, namely quantum-inspired BPSO (QBPSO). Although BPSO-based approaches have been successfully applied to the combinatorial optimization problems in various fields, the BPSO algorithm has some drawbacks such as premature convergence when handling heavily constrained problems. The proposed QBPSO combines the conventional BPSO with the concept and principles of quantum computing such as a quantum bit and superposition of states. The QBPSO adopts a Q-bit individual for the probabilistic representation, which replaces the velocity update procedure in the particle swarm optimization. To improve the search capability of the quantum computing, this paper also proposes a new rotation gate, that is, a coordinate rotation gate for updating Q-bit individuals combined with a dynamic rotation angle for determining the magnitude of rotation angle. The proposed QBPSO is applied to unit commitment (UC) problems for power systems which are composed of up to 100-units with 24-h demand horizon.

关键词： Binary particle swarm optimization combinatorial optimization constraint treatment technique quantum computing quantum evolutionary algorithm unit commitment

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quantum Dynamic Mechanism-based Multi-objective evolutionary algorithm and Performance Analysis

Quantum Dynamic Mechanism-based Multi-objective Evolutionary...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： You, Xiaoming Sun, Xiankun Liu, Sheng Huang, Jiaying Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781424447541

A novel Self-organizing quantum evolutionary algorithm for Multi-objective optimization(MSQEA) is proposed The technique for improving the performance of MSQEA has been described By using self-organizing co-evolution strategy each subpopulation can obtain more optimal solutions Because of the quantum dynamic mechanism all the subpopulations may move concurrently in a force-field until all of them reach their equilibrium states Self-organizing algorithm has advantages in terms of the adaptability, reliability and the learning ability over traditional organizing algorithm, so the solution quality is improved 0/1 Multi-objective knapsack problem simulation results demonstrate the superiority of MSQEA in this paper

关键词： quantum evolutionary algorithm Multi-objective optimization co-evolutionary strategy quantum dynamic mechanism

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An evolutionary Approach for Probabilistic Temporal Fuzzy Controllers

An Evolutionary Approach for Probabilistic Temporal Fuzzy Co...

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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Bazrafshan, Fazel Mojallali, Hamed Islamic Azad Univ Qazvin Branch Dept Elect & Comp Engn Qazvin Iran Univ Guilan Fac Engn Dept Elect Engn Rasht Iran

ISBN: (纸本)9781424455690

Temporal fuzzy systems are powerful systems that use temporal information in their reasoning process. Probabilistic fuzzy systems are the systems that combine the advantages of probabilistic and fuzzy uncertainty with each other. In this paper, we propose a novel approach, which combines the advantages of probabilistic fuzzy systems with temporal fuzzy systems. The proposed algorithm uses an evolutionary algorithm to find the parameters of the system. In order to examine the proposed method, a fuzzy controller for an overhead crane system is designed. Simulation results show that the proposed method has better performance than other methods.

关键词： Probabilistic fuzzy system Temporal fuzzy system quantum evolutionary algorithm

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