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The quantum-inspired evolutionary algorithm in the Parametric Optimization of Lithium-Ion Battery Housing in the Multiple-Drop Test

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BATTERIES-BASEL 2024年第9期10卷 308页

作者： Ruranski, Adam Kus, Waclaw Silesian Tech Univ Fac Mech Engn PL-44100 Gliwice Poland

Recent developments in lithium-ion batteries have improved their capacity, which allows them to be used in more applications like power tools. However, they also carry higher risks, such as thermal runaway, which can happen if they are damaged. To make these batteries safer, it is important to improve the design of their housings subjected to multiple drops during their use. This article introduces a new method for optimizing the design of lithium-ion battery housings using a quantum-inspired evolutionary algorithm (QEA). Previously used mainly in theoretical settings, the authors have adapted QEA for practical engineering tasks. Multiple-drop test simulations were performed, and QEA was used to identify the best housing designs that minimize damage. To support this, a program was developed that automates all drop tests and rebuilds the model. The damage is obtained on the basis of the finite element method (FEM) analyses. The findings show that the algorithm successfully identified designs with the least damage during these tests. This research helps make battery housings safer and explores new uses for QEA in mechanical engineering.

关键词： quantum-inspired evolutionary algorithm optimization multiple-drop test FEM Li-ion battery housing

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quantum-inspired evolutionary algorithm for Optimal Service-Matching Task Assignment

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INFORMATION 2022年第9期13卷 438页

作者： Vendrell, Joan Kia, Solmaz Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA

This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect.

关键词： quantum-inspired evolutionary algorithm service-matching task assignment greedy algorithm Hungarian method Simplex

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Manoeuvring modelling of a containership in shallow water based on optimal truncated nonlinear kernel-based least square support vector machine and quantum-inspired evolutionary algorithm

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OCEAN ENGINEERING 2020年第0期195卷 106676-000页

作者： Xu, Haitong Guedes Soares, C. Univ Lisbon Inst Super Tecn Ctr Marine Technol & Ocean Engn CENTEC Av Rovisco Pais P-1049001 Lisbon Portugal

Nonlinear implicit models are proposed for manoeuvring simulation of a container model in shallow water. A series of Planar Motion Mechanism tests of the DTC ship model carried out in a towing tank with shallow water is used for training the nonlinear implicit manoeuvring model. A novel method, nonlinear kernel-based Least Square Support Vector Machine (LS-SVM), is proposed to approximate the nonlinear manoeuvring model. It is a robust method for regression modelling with a low computational cost by reducing the dimensionality of the kernel matrix. The radial basis function kernel is employed in the SVMs to guarantee the performance of the approximation. The quantum-inspired evolutionary algorithm (QEA) is used to search the optimal value of the predefined parameters of the nonlinear kernel-based LS-SVM. The optimal truncated LS-SVM is used to train the nonlinear regression models for ship manoeuvring, and the generalization performance of the obtained nonlinear manoeuvring models is further tested against the validation set. The R-2 goodness-of-fit criterion is used to demonstrate the accuracy of the obtained models.

关键词： Optimal truncated LS-SVM Shallow water manoeuvring quantum-inspired evolutionary algorithm Radial basis function kernel

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Chaotic quantum-inspired evolutionary algorithm: enhancing feature selection in BCI

Chaotic Quantum-inspired Evolutionary Algorithm: enhancing f...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Ramos, Alimed Celecia Vellasco, Marley Pontificia Univ Catolica Rio de Janeiro Elect Engn Dept Rio de Janeiro Brazil

ISBN: (纸本)9781728169293

quantum-inspired evolutionary algorithms (QiEAs) have demonstrated to be very effective in several applications. In particular, employing this algorithm for feature selection as a wrapper technique in Brain-Computer Interfaces applications was recently proposed with great results. Moreover, the training time of the model was decreased while maintaining a high classification accuracy, both essential conditions for a successful BCI. The drawback of this model was the sensitiveness to changes in the direction and magnitude of the rotation angle, which can produce adverse effects in both performance and convergence time. Chaotic systems and evolutionary algorithms, when combined, can enhance the convergence rate and speed of the evolutionary process, incrementing the capacity of reaching the global optima. In this paper we explore the effects of adding ergodicity to a QiEA by the employment of chaotic maps in two operators: chaotic uniform crossover and chaotic quantum update gate. To validate the proposed approach, six commonly used chaotic maps arc tested with data of Motor Imagery (MI) Electroencephalography (EEG) of right and left hand movement. The results of these experiments are compared with the ones of a QiEA and a classical Genetic algorithm (GA). In the proposed model, Wavelet Packet Decomposition is employed as the time-frequency analysis to characterize the signal, whereas a Multilayer Perceptron Neural Network is used as a classifier. The results demonstrated that Chaotic QiEAs can significantly improve the convergence time of the model with only a small loss in the final accuracy.

关键词： Feature selection Chaotic maps quantum-inspired evolutionary algorithm BCI applications Motor Imagery classification

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Optimally robust H_∞ polynomial fuzzy controller design using quantum-inspired evolutionary algorithm

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2018年第12期49卷 2601-2617页

作者： Yu, Gwo-Ruey Huang, Yu-Chia Cheng, Chih-Yung Natl Chung Cheng Univ Dept Elect Engn 168 Univ Rd Chiayi 62102 Taiwan Natl Taiwan Ocean Univ Dept Elect Engn Keelung Taiwan

This paper proposes an optimally robust H polynomial fuzzy controller design using quantum-inspired evolutionary algorithm (QEA) for continuous/discrete time polynomial fuzzy systems with model uncertainties and external disturbances. To improve control performance, QEA is adopted to evolve optimal control gains with a fitness function that is defined by performance requirements. The stability and robustness of the control system are then guaranteed by the proposed robust H-infinity stability conditions, which are formed by the sum of squares (SOS) method. By using the principle of copositivity, novel relaxed SOS-based stability conditions are derived to reduce the conservativeness of solving SOS-based stability conditions, while the feasible solution space is broadened. Four numerical examples demonstrate the effectiveness of the proposed approaches.

关键词： Polynomial fuzzy control robust H-infinity criterion quantum-inspired evolutionary algorithm

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quantum rotation gate in quantum-inspired evolutionary algorithm: A review, analysis and comparison study

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SWARM AND evolutionary COMPUTATION 2018年 42卷 43-57页

作者： Xiong, Hegen Wu, Zhiyuan Fan, Huali Li, Gongfa Jiang, Guozhang Wuhan Univ Sci & Technol Coll Machinery & Automat Wuhan 430081 Hubei Peoples R China Jiangxi Agr Univ Coll Sci Nanchang 330045 Jiangxi Peoples R China

quantum-inspired evolutionary algorithm (QEA) is a kind of intelligent algorithm which widely and effectively used in many fields. In QEA, the basic and common operations usually include quantum chromosome observation and quantum gate update. quantum rotation gate (QRG) is the most commonly used operator for the operation of quantum gate update, which has a significant influence on the performance of QEA. Many kinds of QRGs have been proposed with different methods to set the only parameter of QRG, i.e., rotation angle. In this paper, a study on classification of QRG is first conducted with respect to rotation direction and magnitude of rotation angle by analyzing and summarizing various kinds of QRGs in literature, and then the corresponding definitions, descriptions and analyses are presented. Furthermore, in order to investigate and compare performances of different QRGs, we set 21 kinds of QRG schemes based on the classification of rotation direction and magnitude of rotation angle. Four typical complex function optimization problems and a 0-1 knapsack problem are selected as experiment objects to test the 21 kinds of schemes. Comprehensive processing and analyzing for the experiment data are conducted, which draws some valuable conclusions for the more reasonable and more effective applications of QEA.

关键词： quantum-inspired evolutionary algorithm quantum rotation gate Function optimization problem Knapsack problem

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A quantum-inspired evolutionary algorithm Using Gaussian Distribution-Based Quantization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第2期43卷 471-482页

作者： Tirumala, Sreenivas Sremath Auckland Univ Technol Auckland New Zealand

Recent advances lead to the increase in the capability of evolutionary algorithms for tacking optimization problems. Particularly quantum-inspired computation leads to a new direction for enhancing the effectiveness of these algorithms. Existing studies on quantum-inspired algorithms focused primarily on evolving a single set of homogeneous solutions. This paper expands the scope of current research by applying quantum computing principles, in particular the quantum superposition principle proposing a new quantization approach. As a result, a quantum-inspired competitive coevolution algorithm (QCCEA) is proposed in this paper. Unlike its predecessors and traditional quantum-inspired algorithms, the proposed QCCEA uses a novel approach for quantization using Gaussian distribution which alters the selection procedure in order to identify the optimal fitness. This new approach which incorporates a new qubit architecture and processing has proved effective for numerical optimization problems as well as multiobjective problems. Experiments were performed on 20 benchmark numerical optimization problems as well as a combinatorial maze problem. The experiment results show that the proposed quantization technique has improved the performance and accuracy of the traditional non-quantized algorithms significantly for the both types of problems. This paper further discusses the impact of population before and after quantization as well as sensitivity of the parameters. The novel approach of quantization that improved the efficiency and performance is the key contribution of this work.

关键词： evolutionary algorithm quantum-inspired evolutionary algorithm quantum-inspired competitive coevolution QCCEA

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An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第6期28卷 1441-1457页

作者： Wu, Xiuli Wu, Shaomin Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Univ Kent Kent Business Sch Canterbury CT2 7PE Kent England

The flexible job shop scheduling problem (FJSP) is vital to manufacturers especially in today's constantly changing environment. It is a strongly NP-hard problem and therefore metaheuristics or heuristics are usually pursued to solve it. Most of the existing metaheuristics and heuristics, however, have low efficiency in convergence speed. To overcome this drawback, this paper develops an elitist quantum-inspired evolutionary algorithm. The algorithm aims to minimise the maximum completion time (makespan). It performs a global search with the quantum-inspired evolutionary algorithm and a local search with a method that is inspired by the motion mechanism of the electrons around atomic nucleuses. Three novel algorithms are proposed and their effect on the whole search is discussed. The elitist strategy is adopted to prevent the optimal solution from being destroyed during the evolutionary process. The results show that the proposed algorithm outperforms the best-known algorithms for FJSPs on most of the FJSP benchmarks.

关键词： Flexible job shop scheduling problem quantum-inspired evolutionary algorithm Convergence speed Local search

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A Real-Coded Multi-Objective quantum-inspired evolutionary algorithm and its Application 29

A Real-Coded Multi-Objective Quantum-Inspired Evolutionary A...

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第29届中国控制与决策会议

作者： Li Yong Wu Xiaohong Zhang Yuxian Shenyang University of Technology

ISBN: (纸本)9781509046584

A real-coded multi-objective quantum evolutionary algorithm is proposed with the extension of a real-coded single-objective quantum evolutionary algorithm,and it is applied to the optimization design problem of low voltage large current vehicle-based *** update mechanism and intelligent search mechanism are improved for multi-objective problem *** crowd distance and population mutation are also taken into *** proposed algorithm is applied to test functions with two objectives and three *** shows better performance both in convergence and distribution compared with *** to design requirements,motor efficiency and material costs are selected as objective functions,and then the multi-objective model of low voltage large current vehicle-based generator is *** the proposed algorithm to the model,better designing schemes are obtained compared with original *** validity of the algorithm is proved.

关键词： Multi-objective optimization quantum-inspired evolutionary algorithm Motor optimization design

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An Improved quantum-inspired evolutionary algorithm for Knapsack Problems 3rd

An Improved Quantum-Inspired Evolutionary Algorithm for Knap...

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3rd International Conference on Cloud Computing and Security (ICCCS)

作者： Xiang, Sheng He, Yigang Chang, Liuchen Wu, Kehan Zhang, Chaolong Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China Anqing Normal Univ Sch Phys & Elect Engn Anqing 246011 Peoples R China Univ New Brunswick Dept Elect & Comp Engn Fredericton NB E3B 5A3 Canada

ISBN: (纸本)9783319685427;9783319685410

As a well-known combinatorial optimization problem, knapsack problems commonly arise in security areas. In this paper, an improved quantum-inspired evolutionary algorithm (PEQIEA) is proposed to solve knapsack problems. In PEQIEA, in each iteration, the state preference of the elite group is used to update the group. The elite group of each iteration consists of a certain number of individuals which are selected by their fitness values. A state preference is proposed to improve the efficiency of the algorithm. A new quantum-inspired gate is obtained by the elite group and their state preference. The Q-gate is then used to make the evolution of the group. The parameters in PEQIEA, which affect the accuracy and efficiency of the algorithm, are discussed empirically. The performance of PEQIEA is then evaluated through extensive experiments.

关键词： quantum-inspired evolutionary algorithm Elite group State preference Knapsack problem

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