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quantum Teaching-Learning-Based Optimization algorithm for sizing optimization of skeletal structures with discrete variables

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STRUCTURES 2021年 32卷 1798-1819页

作者： Kaveh, A. Kamalinejad, M. Hamedani, K. Biabani Arzani, H. Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran Shahid Rajaee Teacher Training Univ Dept Civil Engn Tehran Lavizan Iran

In terms of optimization algorithm improvement, researchers utilize some strategies to establish the right balance between local and global search phases to escape from a local minimum in some NP-hard problems. The approach of quantum-behaved particles, as a novel strategy, uses uncertainty law and a distinct formulation obtained from solving the time-independent Schrodinger differential equation in the delta-potential-well function to update the solution candidates' positions. New formulation defines the local attractors as probable solutions between the best solution and the others to explore all solution space domain. Also, mentioned formulation employs the difference between the average solution and other ones as a new step size. Both local attractors and new step sizes guarantee diversification besides the inherent intensification. In the present paper, the teacher phase of the Teaching-Learning-Based Optimization algorithm (TLBO), which has been introduced recently, is improved using the formulation obtained from solving the time-independent Schrodinger equation to predict the probable positions of optimal solutions. The results show that QTLBO, an acronym for the quantum Teaching- Learning- Based Optimization, improves the stability and robustness of the TLBO by defining the quantum teacher phase. The two planner rigid frames and two trusses with strength and serviceability constraints are chosen to verify the quality and performance of QTLBO. Comparing the results obtained from the proposed algorithm with those of the standard version of the TLBO algorithm and other literature methods shows that QTLBO increases the chance of finding a better solution besides improving the statistical criteria compared to the current TLBO.

关键词： quantum-inspired evolutionary algorithm Teaching-Learning-Based Optimization Population-based algorithm Rigid frames quantum behaved particles quantum teacher

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quantum Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financing

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COMPUTATIONAL ECONOMICS 2024年第2期63卷 919-950页

作者： Chen, Chi Ming Tso, Geoffrey Kwok Fai He, Kaijian City Univ Hong Kong Dept Management Sci 7-261 7-F Lau Ming Wai Acad Bldg AC3 Tat Chee A Hong Kong Peoples R China Hunan Normal Univ Coll Tourism 36 Lushan Rd Changsha 410081 Peoples R China

Mobile e-commerce has grown rapidly in the last decade because of the development of mobile network services, computing capabilities and big data's applications. Financial institutions have been undergoing fundamental transformation in credit risk areas, specifically to traditional credit policy, that is now inadequate for accurately evaluating an individual's credit risk profile in a timely manner. A big-scale dataset representing deep mobile usage of 450,722 anonymous mobile users with a 28-month loan history and mobile behavior of both iOS and Android is designed, can add value for credit scoring in terms of better accuracy and lower feature acquisition cost by introducing a cost-based quantum-inspired evolutionary algorithm (QIEA) feature selection method. The QIEA adopts quantum-based individual representation and quantum rotation gate operator to improve feature exploration capability of conventional genetic algorithm (GA). The expected feature yield fitness function introduced in QIEA able to identify cost-effective feature subsets. Experimental results show that quantum-based method achieves good predictive performances even with only 70-80% number of features selected by GAs, and hence achieve lower feature acquisition costs with budget constraints. Additionally, computational time can be reduced by 30-60% compared with GAs depending on different feature set sizes.

关键词： quantum-inspired evolutionary algorithm Cost constraint feature selection Credit scoring Big data Mobile behavior Optimization

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quantum-inspired Distributed Memetic algorithm

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Complex System Modeling and Simulation 2022年第4期2卷 334-353页

作者： Guanghui Zhang Wenjing Ma Keyi Xing Lining Xing Kesheng Wang the School of Information Science and Technology and the Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China the School of Information Science and Technology Hebei Agricultural UniversityBaoding 071001China. the State Key Laboratory for Manufacturing System Engineering and the Systems Engineering Institute Xi’an Jiaotong UniversityXi’an 710049China the School of Electronic Xidian UniversityXi’an 710071China the Department of Production and Quality Engineering Norwegian University of Science and TechnologyTrondheim 7491Norway

This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global *** exploration evolves three independent populations by heterogenous *** exploitation evolves an external elite archive in parallel with exploration to balance global and local *** transfer is based on a point-ring communication topology to share successful experiences among distinct search *** restart adopts an adaptive perturbation strategy to control search diversity *** computation is a newly emerging technique,which has powerful computing power and parallelized ***,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum *** QDMA integrates the superiorities of distributed,memetic,and quantum *** experiments are carried out to evaluate the superior performance of *** results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum *** superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.

关键词： distributed evolutionary algorithm memetic algorithm quantum-inspired evolutionary algorithm quantum distributed memetic algorithm

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A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems

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SOFT COMPUTING 2014年第9期18卷 1771-1781页

作者： Chiang, Hua-Pei Chou, Yao-Hsin Chiu, Chia-Hui Kuo, Shu-Yu Huang, Yueh-Min Natl Cheng Kung Univ Dept Engn Sci Tainan 70101 Taiwan Natl Chi Nan Univ Dept Comp Sci & Informat Engn Puli Taiwan

In this study, we propose a novel quantum-inspired evolutionary algorithm (QEA), called quantum inspired Tabu search (QTS). QTS is based on the classical Tabu search and characteristics of quantum computation, such as superposition. The process of qubit measurement is a probability operation that increases diversification;a quantum rotation gate used to searching toward attractive regions will increase intensification. This paper will show how to implement QTS into NP-complete problems such as 0/1 knapsack problems, multiple knapsack problems and the traveling salesman problem. These problems are important to computer science, cryptography and network security. Furthermore, our experimental results on 0/1 knapsack problems are compared with those of other heuristic algorithms, such as a conventional genetic algorithm, a Tabu search algorithm and the original QEA. The final outcomes show that QTS performs much better than other heuristic algorithms without premature convergence and with more efficiency. Also on multiple knapsack problems and the traveling salesman problem QTS verify its effectiveness.

关键词： quantum computing Combinatorial optimization quantum-inspired evolutionary algorithm Tabu search Knapsack problem

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Classical and quantum-inspired Tabu Search for Solving 0/1 Knapsack Problem

Classical and Quantum-Inspired Tabu Search for Solving 0/1 K...

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

作者： Chou, Yao-Hsin Yang, Yi-Jyuan Chiu, Chia-Hui Natl Chi Nan Univ Puli 54561 Taiwan

ISBN: (纸本)9781457706530

In this paper, we propose a novel quantum-inspired evolutionary algorithm, called quantum-inspired Tabu search (QTS). QTS is based on the classical Tabu search and the characteristic of quantum computation, such as superposition. We will present how we implement QTS to solve 0/1 knapsack problem. Furthermore, the results of experiment are also compared with the original quantum-inspired evolutionary algorithm (QEA)' and the other heuristic algorithms' experimental results. The final outcomes shows that QTS performs much better than the others on 0/1 knapsack problem, without premature convergence and more efficiency.

关键词： quantum computing combinatorial optimization quantum-inspired evolutionary algorithm tabu search knapsack problem

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On the adoption of Metaheuristics for Solving 0-1 Knapsack Problems 12

On the adoption of Metaheuristics for Solving 0-1 Knapsack P...

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12th International Symposium on Parallel Architectures, algorithms and Programming (PAAP)

作者： Qiu, Yang Li, Haomiao Wang, Xiao Dib, Omar Wenzhou Kean Univ Dept Comp Sci Wenzhou 325060 Peoples R China

ISBN: (纸本)9781665496391

0-1 knapsack problem is a classical Non-deterministic Polynomial Hard (NP-Hard) problem in combinatorial optimization. It has a wide range of applications in real life, such as the distribution of goods in logistics companies, capital calculation, storage, and distribution, etc. This paper studies the adoption of three meta-heuristic approaches for solving the 0-1 knapsack problem. A novel quantum-inspired Tabu Search (QTS) that combines a classical Tabu Search (TS) and quantum-inspired evolutionary algorithm (QEA), Ant Colony swarm intelligence algorithm (ACO), and Genetic algorithm (GA) with augmented fitness function. Based on empirical analysis of computer simulations and comparative demonstrations, we show that applying such metaheuristic results in high efficiency in terms of the quality of obtained solutions, computational time, and robustness when dealing with the underlying 0-1 knapsack optimization problem.

关键词： 0-1 knapsack Tabu Search quantum-inspired evolutionary algorithm Genetic algorithm Ant Colony Optimization

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Resource Optimization for Elective Surgical Procedures Using quantum-inspired Genetic algorithms.

Resource Optimization for Elective Surgical Procedures Using...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Gonzalez, Rene Vellasco, Marley Figueiredo, Karla Pontificia Univ Catolica Rio de Janeiro Elect Engeneering Dept Rio de Janeiro Brazil Univ Estado Rio De Janeiro Computat Sci Dept Rio De Janeiro Brazil

ISBN: (纸本)9781450361118

Currently, Health Units in a large number of countries in the world present service demand that exceeds their real capacities. This problem causes the inevitable emergence of long waiting lists. The optimization of such waiting list is very challenging, due to the large number of resources that must be considered. This paper proposes a new model, based on a quantum-inspired evolutionary algorithm, to optimize the scheduling of for elective surgical procedures. The quantum-inspired evolutionary algorithm for Healthcare (QIEA-H) model, aims not only to designate the necessary resources to the patients in order to achieve the successful completion of the chirurgical procedure, but also to reduce the total time used to perform all surgeries and the number of surgeries out of term. For the validation of the proposed model, a waiting list of 2000 surgeries was created artificially and using a simulation tool also developed in this work. The model achieved a reduction in the time of all surgeries of up to 16.25% and the number of surgeries out of date of up to 13.04%.

关键词： quantum-inspired evolutionary algorithm Health Scheduling Optimization Genetic algorithms

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GFSOP Based Ternary quantum Logic Synthesis

GFSOP Based Ternary Quantum Logic Synthesis

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Conference on Optics and Photonics for Information Processing IV

作者： Khan, Mozammel H. A. East West Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh

ISBN: (纸本)9780819482938

quantum technology is one of the most promising technologies for future computing systems, since quantum algorithms solve problems much more efficiently than classical algorithms. All quantum algorithms are made up of quantum logic circuits. A quantum logic circuit is made up of quantum gates (reversible in nature) and is designed using reversible logic synthesis methods. For a given Hilbert space, ternary quantum system requires 0.63 times qutrits than the corresponding number of qubits. Thus, the ternary quantum system provides a much more compact and efficient information encoding. Beside other technologies, ternary quantum logic system can be realized using photon polarization. These advantages of ternary quantum system open avenue for developing ternary quantum algorithms. Galois field sum of products (GFSOP) based synthesis of ternary quantum logic circuit is the most practical approach, since any ternary logic function with many inputs can be represented as GFSOP expression and the GFSOP expression can be implemented as cascade of ternary quantum gates. Here we discuss minimization of ternary logic function as GFSOP expression using quantum-inspired evolutionary algorithm. We also discuss a method of realization of ternary GFSOP expression using ternary quantum gates. Experimental results are given to show the effectiveness of the ternary GFSOP minimization technique.

关键词： Galois field sum of products expression optical quantum system quantum-inspired evolutionary algorithm ternary quantum computation ternary quantum gates ternary quantum logic synthesis

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Investigation on Introducing Qubit Convergence Measure to QEA in Maximum Cut Problem 10

Investigation on Introducing Qubit Convergence Measure to QE...

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10th IEEE International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Moriyama, Yoshifumi Iimura, Ichiro Nakayama, Shigeru Prefectural Univ Kumamoto Fac Adm Studies Higashi Ku 3-1-100 Tsukide Kumamoto 8628502 Japan Kagoshima Univ 1-21-40 Korimoto Kagoshima 8900065 Japan

ISBN: (纸本)9781538604694

The quantum-inspired evolutionary algorithm (QEA) and QEA with a pair-swap strategy (QEAPS), where each gene is represented by a quantum bit (qubit), and the qubit is updated by a unitary transformation in both algorithms. QEA and QEAPS can automatically shift from a global search to a local search and have shown superior search performance to the classical genetic algorithm. However, it is important to appropriately manage the convergence speed of qubits. In this study, we have proposed a measure that can confirm convergence state of qubits, and applied the measure to a method for keeping diversity. From the results of the computational experiment in the maximum cut problem, we have clarified that the proposed measure can confirm the state of the qubit, and the discovery rate of the optimal solution improves with to apply the method for maintenance of diversity.

关键词： quantum-inspired evolutionary algorithm QEA QEA with pair-swap strategy QEAPS maximum cut problem graph optimization

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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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