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IEEE Congress on evolutionary Computation (CEC)

作者： Wei, Xin Fujimura, Shigeru Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushyu Fukuoka 8080135 Japan

ISBN: (纸本)9781467315098

This paper proposes a parallel quantum evolutionary algorithm (PQEA) using Client-Server model for multi-objective optimization problems. Firstly, the PQEA uniformly decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems. All the sub-problems are classified into several groups according to their similarities. Each "Client" processes the evolution for a group of neighbor sub-problems in parallel. There is a quantum individual used to address the sub-problems of a group in a "Client". Since the quantum individual is a probabilistic representation, it can share evolutionary information of the neighbor sub-problems in one group, while the sub-problems are orderly solved using a same q-bit individual. The "Server" maintains non-dominated solutions that are generated by every "Client". The current best solution for each sub-problem can be found in the "Server", when the quantum individual updated its states for evolution. Experimental results have demonstrated that PQEA obviously outperforms the most famous multi-objective optimization algorithms MOEA/D on the bi-objectives. For the more objectives, the PQEA obtains the similar results with MOEA/D, even with the same evaluation times. Furthermore, in this paper, the scalability and sensitivity of PQEA have also been experimentally investigated.

关键词： quantum evolutionary algorithm Parallel Multi-Objective Optimization Pareto Front

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Novel quantum-Inspired Co-evolutionary algorithm

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2016年第2期10卷 353-364页

作者： Shao, Ming Zhou, Liang Shanghai Univ Engn Sci Sch Management Shanghai 201600 Peoples R China Shanghai Lixin Univ Commerce Sch Math & Informat Sci Shanghai 201620 Peoples R China

Co-evolutionary mechanism is now used into evolutionary algorithms and provides these algorithms the power to promote the convergence. In order to promote the performance of the traditional quantum-inspired evolutionary algorithm (QEA), we proposed a novel quantum-inspired co-evolutionary algorithm (NQCEA), in this paper. The quantum state population is firstly divided into multiple sub-populations, which complete the evolution processes independently. In each evolution cycle, every sub-population will produce an elitist individual, which is then selected to construct an elite library. Subsequently, these individuals in this elite library can help the poor sub-population to find the global optimal solution or near-optimal solution. In addition, a diversity indicator is defined for every sub-population and is used to measure the diversity of the corresponding sub-population. As for the sub-population with poor diversity, the mutation strategies are implemented in order to expand its diversity and improve its global search ability. Finally, the NQCEA is compared with the traditional QEA to test their performance. Experiments are performed on the global numerical optimization functions and the simulation results indicate that this new algorithm has the characteristics of good global search capability and more stable performance than the traditional QEA.

关键词： quantum evolutionary algorithm Co-evolution Global optimization Elitist individual

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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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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Xiaoming You,Xiankun Sun,Sheng Liu,Jiaying Huang College of Electronic and Electrical Engineering,Shanghai University of Engineering Science Shanghai,China

A novel Self-organizing quantum evolutionary algorithm for Multi-objective optimization(MSQEA) is *** technique for improving the performance of MSQEA has been *** using self-organizing co-evolution strategy each subpopulation can obtain more optimal *** of the quantum dynamic mechanism all the subpopulations may move concurrently in a force-field until all of them reach their equilibrium ***-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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Modelling the spice parameters of SOI MOSFET using a combinational algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4683-S4692页

作者： Sarvaghad-Moghaddam, Moein Orouji, Ali A. Ramezani, Zeinab Elhoseny, Mohamed Farouk, Ahmed Kumar, N. Arun Semnan Univ Dept Elect & Comp Engn Semnan Iran Mansoura Univ Fac Comp & Informat Mansoura Egypt SASTRA Univ Sch EEE Thanjavur India

Progress in the technology of submicron semiconductor device, which makes a short-channel and quantum effects, having equations of nonlinear modelling, leads to complicated and time-consuming calculations. In order to control these complexities and obtain the device characteristics according to device parameters, a faster method is needed. In this paper, a combinational algorithm is proposed for modelling a nano silicon-on-insulator metal-oxide-semiconductor field effect transistor (SOI MOSFET) characteristic. The proposed method shows the same device characteristics with lower input parameters. In this method, a combination of genetic algorithm (GA) and artificial neural network are used. Then quantum evolutionary algorithm (QEA) is employed instead of genetic algorithm (GA) for comparing and modifying algorithm. Results show that the algorithm's accuracy is 95% and 98% for test data of GA and QGA, respectively. Moreover, the reduction percentage of input parameters are 11% and 52% for GA and QEA, respectively. The simulation results represent that the implemented quantum genetic algorithm for prediction of device characteristics is more effective and accurate than GA.

关键词： SOI MOSFET Parameter Extraction Genetic algorithm quantum evolutionary algorithm Artificial neural network

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A bi-objective fleet size and mix green inventory routing problem, model and solution method

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SOFT COMPUTING 2019年第4期23卷 1375-1391页

作者： Alinaghian, Mehdi Zamani, Mohsen Isfahan Univ Technol Dept Ind & Syst Engn 17 Daneshgah AveDaneshgah St Esfahan *** Iran

Inventory routing problem (IRP) is one of the most important logistics problems;in this type of problems, decision maker usually has the option to use several types of vehicles to form a fleet with appropriate size and composition in order to minimize both inventory and transportation costs. Meanwhile, the increasing fuel consumption and its economic and environmental impacts mean that this issue must also be incorporated into the routing problems. This paper proposes a new bi-objective model for green inventory routing problem with the heterogeneous fleet. The objectives of the proposed model are (I) to reduce the emissions and (II) to minimize the fleet size, vehicle type, routing, and inventory costs. Given the NP-hard nature of the assessed problem, a bi-objective meta-heuristic algorithm based on the quantum evolutionary algorithm is proposed to achieve these objectives. To evaluate the performance of the proposed algorithm, its results are compared with the results of the exact method and Non-dominated Sorting Genetic algorithm II (NSGAII). The results of these comparisons indicate the better performance of the proposed algorithm.

关键词： Multi-objective optimization Pollutant emissions Fuel consumption The fleet size and mix IRP quantum evolutionary algorithm NSGAII

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A novel quantum ant colony optimization algorithm and its application to fault diagnosis

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2008年第3-4期30卷 313-329页

作者： Wang, Ling Niu, Qun Fei, Minrei Shanghai Univ Sch Mechatron & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

Fault detection in chemical production processes is difficult because of the amount of data that needs to be analysed and the absence of fault data. This paper uses support vector machines (SVMs) to diagnose faults, as it fits the small sample problem. To eliminate system disturbances and noise from the high levels of data, it then combines SVMs with a novel quantum ant colony optimization (QACO) algorithm to select the fault features. The proposed method is then tested using the benchmark Tennessee Eastman Process and shown to be effective. In particular, it is demonstrated that QACO and SVMs can find the essential fault variables exactly, and will greatly improve the fault diagnosis performance of SVMs for a complex chemical process.

关键词： ant colony optimization fault diagnosis quantum evolutionary algorithm

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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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A novel quantum inspired hybrid metaheuristic for dispatch of power system including solar photovoltaic generation

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2021年第6期16卷 558-583页

作者： Bodha, Kapil Deo Yadav, Vinod Kumar Mukherjee, Vivekananda Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad Bihar India Delhi Technol Univ Dept Elect Engn Delhi India

This manuscript proposes a novel metaheuristic, which is an amalgamation of the quantum concept and the gravitational search particle swarm optimization technique. The inclusion of quantum concepts in the algorithm enhances its capability, as in quantum space there is no restriction on the movement of particle and the solution can be obtained with smaller population and faster convergence. An adaptive contraction expansion factor is also introduced which ensures better exploration of the algorithm. The technique is applied to solve the combined economic emission dispatch of a hybrid solar thermal unit operating in New Delhi, India for full and reduced solar radiation. To verify the effectiveness of the proposed technique, it is also applied to solve the combined economic emission dispatch of a 10 unit, 2000 MW system. It is observed that the proposed approach provides a fuel cost saving of up to 1300$ when compared with other referred techniques.

关键词： Binary encoded problems combinatorial optimization combined economic emission dispatch mixed integer optimization problem quantum evolutionary algorithm rotation quantum gate

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QEA-QCNN: optimization of quantum convolutional neural network architecture based on quantum evolution

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MEMETIC COMPUTING 2024年第3期16卷 233-254页

作者： Li, Yangyang Hao, Xiaobin Liu, Guanlong Shang, Ronghua Jiao, Licheng Xidian Univ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Joint Int Res Lab Intelligent Percept & ComputatM Xian 710071 Peoples R China

quantum neural network (QNN) is a research orientation that combines quantum computing and machine learning. It has the potential to solve the bottleneck problem of shortage of computing resource in deep learning, and is expected to become the first practical application scheme that demonstrate application level quantum advantages on current Noise Intermediate scale quantum (NISQ) devices. However, limited by the current scale of NISQ devices, QNNs have fewer quantum bits and quantum circuits cannot be too deep. Currently, there is no clear design strategy for the architecture of QNN. Designing QNN architectures arbitrarily not only has high circuit complexity but also often poor network performance. Similar to classical convolutional neural network, in this paper, a quantum evolution-based optimization algorithm is proposed for design of quantum convolutional neural network (QCNN) architecture. The design of QNN architecture is viewed as a combinatorial optimization problem, and the quantum evolution algorithm is used to adaptively design the QCNN architecture with its global search ability in a large discrete search space. Comprehensive experimental results indicate that the proposed method can effectively reduce the complexity of QCNN circuits, reduce the difficulty of deploying quantum circuits, and further improve the expressibility of QCNN.

关键词： quantum convolutional neural network quantum architecture search quantum evolutionary algorithm Parameterized quantum circuits

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Mixed continuous/binary quantum-inspired learning system with non-negative least square optimisation for automated design of regularised ensemble extreme learning machines

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2016年第3期28卷 581-606页

作者： Mozaffari, Ahmad Azad, Nasser L. Emami, Mahdi Fathi, Alireza Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Babol Univ Technol Dept Elect Engn Babol Sar Iran Babol Univ Technol Dept Mech Engn Babol Sar Iran

In this paper, a hybrid quantum-inspired evolutionary algorithm (QIEA) is proposed to automatically design regularised ensemble extreme learning machines (EELMs). quantum evolutionary computing is a relatively recent spot-lighted concept which takes advantage from both the evolutionary and quantum computing laws. In general, QIEAs have been proven to be really powerful for optimising complex engineering tasks. The fascinating trait of observation operator in QIEA enables us to transform the quantum bits to both the binary and continuous spaces. Here, the authors present a mix continuous/binary version of QIEA, to find out whether it is suited for designing regularised EELMs. Indeed, the design process of EELM is conducted at two different levels, i.e. hyper and low levels. At the low level, some novel criteria are presented in the form of penalty functions to enable the optimiser searching for parsimonious, compact and accurate regularised extreme learning machines, as individual components of the ensemble. At the hyper-level, the non-negative least square error optimisation technique is utilised to deterministically find the most eligible components for designing the ensemble. Through extensive numerical experiments, the authors demonstrate that the proposed method is really efficient for the automated design of EELM identifiers.

关键词： quantum evolutionary algorithm ensemble extreme learning machine evolutionary ensemble design regularisation penalty function regression problems

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