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5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)

作者： Mahseur, Mohammed Meraihi, Yassine Boukra, Abdelmadjid Ramdane-Cherif, Amar Univ Sci & Technol Houari Boumediene Fac Elect & Informat Algiers 16025 Algeria Univ MHamed Bougara Boumerdes Appl Automat Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin en Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France

ISBN: (纸本)9781538606865

The quality of service multicast routing problem (QoSMRP) is a very interesting research issue for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed for solving this problem. This paper proposes two hybrid approaches based on the hybridization of Firefly algorithm (FA) with quantum evolutionary algorithm (QEA) to solve the QoS multicast routing problem. The main idea of the first approach, named FAQEA1, is the integration of the evolutionary equation of FA in the evolutionary operator of QEA;while the main idea of the second approach, named FAQEA2, is the replacement of the evolutionary equation of QEA by the evolutionary equation of FA. The aim of these approaches is to avoid the premature convergence, ensure the diversity of the solutions and enhance the efficiency of QEA. The experimental results reveal the superiority and effectiveness of our proposed algorithms compared with other existing algorithms.

关键词： quantum evolutionary algorithm Firefly algorithm Quality of Service (QoS) Multicast routing Multicast tree

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Parameters Optimization of ANFIS using quantum-inspired evolutionary algorithm 29

Parameters Optimization of ANFIS using Quantum-inspired Evol...

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第29届中国控制与决策会议

作者： Qian Xiaoyi Zhang Yuxian Mohammed Altayeb Awad Li Yong School of Electrical Engineering Shenyang University of Technology

ISBN: (纸本)9781509046584

For describing nonlinear system accurately and improving adaptive neuro-fuzzy inference system model,a quantum-inspired evolutionary algorithm is presented for optimizing parameters of adaptive neuro-fuzzy inference *** this paper,an allele real-coded quantum evolutionary algorithm is introduced to optimize the premise and consequent parameters of adaptive neuro-fuzzy inference *** real-coding method based on allele is adopted,and variable-scale updating strategy of parameter is *** a result,the parameter falling into local minimum is *** model accuracy of adaptive neuro-fuzzy inference system is *** simulation examples show the effectiveness of proposed *** proposed improved algorithm is compared with the standard adaptive neuro-fuzzy inference ***,the proposed adaptive neuro-fuzzy inference system model is applied to predict the quality index in textile slashing process.

关键词： parameter optimization adaptive neuro-fuzzy inference system allele real-coded quantum evolutionary algorithm variable-scale updating strategy

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quantum evolutionary algorithm for Parallel Flowshop Scheduling Problem in Chemical Industry 8

Quantum Evolutionary Algorithm for Parallel Flowshop Schedul...

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8th International Symposium on Computational Intelligence and Design (ISCID)

作者： Tang, Qi Zhou, Xiaoye Tang, Jianxun Shenyang Univ Technol Sch Management Shenyang Peoples R China Civil Aviat Univ China Coll Air Traff Management Tianjin Peoples R China

ISBN: (纸本)9781467395878

This paper studies the typical batching and scheduling problem of parallel flowshop, which is characterized by multi-product, multi-stage, parallel production, production coordination between stages, limit of equipment production capacity and inventory capacity, parallel equipment selection and limitation of batch quantity. This paper presents modified quantum evolutionary algorithm(MQEA) with continuous codes representing the batch size on the every stage to enhance the ability to handle constraints. The computational results show that the MQAE may find optimal or suboptimal solutions in a short run time for all the instances.

关键词： chemical industry quantum evolutionary algorithm batch scheduling

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Freight Information Matching Based on quantum evolutionary algorithm

Freight Information Matching Based on Quantum Evolutionary A...

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The 2016 International Conference on Engineering Management (Iconf-EM 2016)

作者： Xiangwei Mu Yan Chen Haixia Zhao Transportation Management College Dalian Maritime University

ISBN: (纸本)9781510835412

In order to improve the efficiency of freight vehicle and cargo matching, a mathematical model was established, which describes target and the related constraints, and quantum evolutionary algorithm was proposed to solve this matching problem. In the experiment, improved quantum evolutionary algorithm was compared with the standard genetic algorithm, and the algorithm parameters were optimized. Experiment results show that quantum evolutionary algorithm has better convergence speed, accuracy and stability, which can efficiently get an optimal freight supply and demand matching plan.

关键词： Freight Information Matching quantum evolutionary algorithm Genetic algorithm

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A quantum evolutionary algorithm for the second-best congestion pricing problem in urban traffic networks

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TRANSPORTATION PLANNING AND TECHNOLOGY 2015年第8期38卷 851-865页

作者： Shahbandi, Mehrdad Gholami Nasiri, Mohammad Mahdi Babazadeh, Abbas Univ Tehran Sch Civil Engn Coll Engn Tehran 11155 Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This paper investigates the congestion pricing problem in urban traffic networks. A first-best strategy, a second-best strategy for toll leveling in closed cordons and a second-best strategy for determining both toll levels and toll points are considered. The problem is known to be a mixed integer programming model and formulated as a bi-level optimization problem, with an objective of maximizing the social welfare. A method is presented to solve the problem, based on a novel metaheuristic algorithm, namely quantum evolutionary algorithm (QEA). To verify the proposed method, the widely used genetic algorithm (GA) is also applied to solve the problem. The problem is solved for a medium-size urban traffic network and the results of the QEA are compared against the conventional GA. Computational results show that the QEA outperforms the GA in solution quality.

关键词： congestion pricing network cordon optimization quantum evolutionary algorithm genetic algorithm

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An adaptive quantum evolutionary algorithm and its Application to Path Planning

An adaptive Quantum Evolutionary Algorithm and its Applicati...

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

作者： Ming, Li Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479986965

The path planning problem is of essential significance for the theoretical research and practical applications of mobile robot navigation. However, it is found to be non-deterministic polynomial time hard (NP-hard) problem. Aiming at solving the problem of the large computational complexity, an adaptive quantum evolutionary algorithm with improved population initialization, adaptive quantum gate operation, crossover and mutation is presented to better the computing performance. The experimental simulation results have demonstrated that the proposed algorithm has high speed of convergence and good global search capability and thus proved that our algorithm is effective and feasible for the trajectory planning of mobile robot in obstacles environments.

关键词： path planning quantum evolutionary algorithm mobile robot quantum computation

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A L5-based Synchronous Cellular quantum evolutionary algorithm 7

A <i>L</i>5-based Synchronous Cellular Quantum Evolutionary ...

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7th International Conference on Information Technology in Medicine and Education (ITME)

作者： Qi, Feng Xu, Lili Shandong Normal Univ Jinan Peoples R China

ISBN: (纸本)9781467383028

In cellular quantum evolutionary algorithm(CQEA), the selection method of the neighbors for each individual affects its performance. Base on L5 neighbors and Asynchronous strategy, a L5-based synchronous cellular quantum evolutionary algorithm(LSCQEA) is proposed. In LSCQEA, each individual in a lattice and its four neighbors all undergo a quantum evolutionary algorithm, then population evolves by the overlapped neighbors of each individual. The performance of the proposed algorithm is tested on Knapsack Problem 14 benchmark functions, and simulation results show that LSCQEA has the similar performance but simpler complexity compared with other algorithms.

关键词： quantum evolutionary algorithm L5-based neighbors cellular structure

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quantum evolutionary algorithm for Chemical Parallel Flow Shop Scheduling Problem

Quantum Evolutionary Algorithm for Chemical Parallel Flow Sh...

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2015 AASRI International Conference on Industrial Electronics and Applications(IEA 2015)

作者： Qi Tang Peng Liu Jianxun Tang Xiang Li Management School Shenyang University of Technology College of Air Traffic Management Civil Aviation University of China

This paper studies the typical problem of integration of chemical batching and scheduling, which is characterized by multi-product, multi-stage, parallel production, production coordination between stages, limit of equipment production capacity and inventory capacity, parallel equipment selection and limitation of batch quantity. The optimization objective of this problem is to minimize the make-span by arranging batch number and size, allocating machines, making decision about the processing sequence and scheduling time of batch on a machine on the premise that the equipment production capacity, maximum inventory and material supply coordination are met. This paper presents modified quantum evolutionary algorithm(MQEA). The computational results show that the MQAE may find optimal or suboptimal solutions in a short run time for all the instances.

关键词： chemical industry quantum evolutionary algorithm batch scheduling

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Expediting population diversification in evolutionary computation with quantum algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2021年第1期17卷 63-73页

作者： Kim, Jun Suk Ahn, Chang Wook Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju South Korea Gwangju Inst Sci & Technol Artificial Intelligence Grad Sch Gwangju South Korea

quantum computing's uniqueness in commencing parallel computation renders unprecedented efficient optimisation as possible. This paper introduces the adaptation of quantum processing to crowding, one of the genetic algorithmic procedures to secure undeveloped individual chromosomes in pursuit of diversifying the target population. We argue that the nature of genetic algorithm to find the best solution in the process of optimisation can be greatly enhanced by the capability of quantum computing to perform multiple computations in parallel. By introducing the relevant quantum mathematics based on Grover's selection algorithm and constructing its mechanism in a quantum simulator, we come to conclusion that our proposed approach is valid in such a way that it can precisely reduce the amount of computation query to finish the crowding process without any impairment in the middle of genetic operations.

关键词： quantum evolutionary algorithm population diversification

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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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