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quantum inspired evolutionary algorithm for ordering problems

EXPERT SYSTEMS WITH APPLICATIONS 2017年第0期67卷 71-83页

作者： da Silveira, Luciano Reis Tanscheit, Ricardo Vellasco, Marley M. B. R. Pontificia Univ Catolica Rio de Janeiro PUC Rio Dept Elect Engn Rua Marques de Sao Vicente 225 Rio De Janeiro RJ Brazil

This paper proposes a new quantum-inspired evolutionary algorithm for solving ordering problems. quantum-inspired evolutionary algorithms based on binary and real representations have been previously developed to solve combinatorial and numerical optimization problems, providing better results than classical genetic algorithms with less computational effort. However, for ordering problems, order based genetic algorithms are more suitable than those with binary and real representations. This is because specialized crossover and mutation processes are employed to always generate feasible solutions. Therefore, this work proposes a new quantum-inspired evolutionary algorithm especially devised for ordering problems (QIEA-O). Two versions of the algorithm have been proposed. The so-called pure version generates solutions by using the proposed procedure alone. The hybrid approach, on the other hand, combines the pure version with a traditional order-based genetic algorithm. The proposed quantum-inspired order-based evolutionary algorithms have been evaluated for two well-known benchmark applications - the traveling salesman problem (TSP) and the vehicle routing problem (VRP) - as well as in a real problem of line scheduling. Numerical results were obtained for ten cases (7 VRP and 3 TSP) with sizes ranging from 33 to 101 stops and 1 to 10 vehicles, where the proposed quantum-inspired order-based genetic algorithm has outperformed a traditional order-based genetic algorithm in most experiments. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： quantum inspired evolutionary algorithm Ordering optimization problem quantum bit Vehicle routing problem

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quantum inspired evolutionary algorithm to Improve the Accuracy of a Neuronal Model of the Electric Power Exchange 17

Quantum Inspired Evolutionary Algorithm to Improve the Accur...

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IEEE EUROCON -17th International Conference on Smart Technologies

作者： Tchorzewski, Jerzy Rucinski, Darusz Univ Nat Sci & Humanities Fac Sci Comp Sci Dept Siedlce Poland Univ Nat Sci & Humanities Comp Sci Dept Siedlce Poland

ISBN: (纸本)9781509038435

This work intends to improve research on artificial neural network model of the Electric Power Exchange using Date of the Day Ahead Market in MATLAB and Simulink environment using evolutionary algorithm inspired by quantum computer science. The developed artificial neural network was initially trained using data for the Day Ahead Market, assuming the joint volume of supplied and sold electrical energy as the input quantities in each hour of the twenty for hours day, and average prices as output quantities. The obtained model of the exchange system was then improved using the evolutionary algorithm. Lastly, we have proposed further improvement to the accuracy of the model, by supplementing the existing evolutionary algorithm with quantum inspired evolutionary algorithm as a solutions type of Hybrid Intelligent Systems, such as the initial population, crossover and mutation operators, selection, fitness function etc. The reprint shows how you can build a model of artificial neural network inspired by quantum solutions in Simulink environment, which is the purpose of further research.

关键词： Artificial Neural Network Day Ahead Market Hybrid Intelligent Systems Neural Modeling quantum inspired evolutionary algorithm Electric Power Exchange

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Parallel improved quantum inspired evolutionary algorithm to solve large size Quadratic Knapsack Problems

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SWARM AND evolutionary COMPUTATION 2016年 26卷 175-190页

作者： Patvardhan, C. Bansal, Sulabh Srivastav, A. Dayalbagh Educ Inst Fac Engn Agra 282005 Uttar Pradesh India Univ Kiel Inst Informat Olshaussenstr 40 D-24098 Kiel Germany 1-25 Hazuri Bhawan Agra 282003 Uttar Pradesh India

Quadratic Knapsack Problem (QKP), an extension of the canonical simple Knapsack Problem, is NP Hard in the stronger sense. No pseudo-polynomial time algorithm is known to exist which can solve QKP instances. QKP has been studied intensively due to its simple structure yet challenging difficulty and numerous applications. A few attempts have been made to solve large size instances of QKP due to its complexity. quantum inspired evolutionary algorithm (QIEA) provides a generic framework that has often been carefully tailored for a given problem to obtain an effective implementation. In this work, an improved and parallelized QIEA, dubbed IQIEA-P is presented. Several additional features make it more balanced in exploration and exploitation and thus have better applicability. Computational experiments are presented on large QKP instances of 1000 and 2000 items. The improvements are inherently parallelizable and, therefore, good speedups are obtained on a multi-core machine. No parallel algorithm is available for QKP. The solutions provided by QIEA-P are competitive with those obtained from the state of the art algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Combinatorial Optimization Large Size Quadratic Knapsack Problem Parallel algorithm quantum inspired evolutionary algorithm

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Solving the 0-1 Quadratic Knapsack Problem with a competitive quantum inspired evolutionary algorithm

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2015年 285卷 86-99页

作者： Patvardhan, C. Bansal, Sulabh Srivastav, A. Dayalbagh Educ Inst Fac Engn Agra 282005 Uttar Pradesh India Univ Kiel Inst Informat D-24098 Kiel Germany

Quadratic Knapsack Problem (QKP) extends the canonical simple Knapsack Problem where the value obtained by selecting a subset of objects is a function dependent not only on the value corresponding to individual objects selected but also on their pair-wise selection. QKP is NP Hard in stronger sense i.e. no pseudo-polynomial time algorithm is known to exist which can solve QKP instances. QKP has been studied intensively due to its simple structure yet challenging difficulty and numerous applications. quantum inspired evolutionary algorithm (QIEA) belongs to the class of evolutionary algorithms and exhibits behaviour of an Estimation of Distribution algorithm (EDA). QIEA provides a generic framework that has to be carefully tailored for a given problem to obtain an effective implementation. Thus, several forms of QIEA exist in the literature. These have been successfully applied on many hard problems. A new QIEA, QIEA-PSA is proposed with improved exploration and exploitation capabilities. Computational experiments on these benchmarks show that QIEA-PSA is improved significantly both in terms of the quality of solutions and speed of convergence on several benchmark QKP instances. The ideas incorporated are general enough and can be utilized with advantage on other similar and not so similar problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Quadratic Knapsack Problem quantum inspired evolutionary algorithm

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quantum-inspired evolutionary algorithm for difficult knapsack problems

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MEMETIC COMPUTING 2015年第2期7卷 135-155页

作者： Patvardhan, C. Bansal, Sulabh Srivastav, Anand DEI Fac Engn Dept Elect Engn Agra 282005 Uttar Pradesh India Univ Kiel Inst Informat D-24118 Kiel Germany

quantum inspired evolutionary algorithms (QIEAs) are evolutionary algorithms which use concepts and principles of quantum computing. The 0/1 knapsack problem (KP) is a well known combinatorial optimization problem that has been typically used to validate the performance of QIEAs. However, there are some variants of KPs called difficult knapsack problems (DKPs) that are known to be more difficult to solve. QIEAs have not yet been fully explored for solving these. In this work, an improved QIEA, called QIEA-PSA is presented. A novel method to initialize the qubit individuals based on heuristic information for the KP instance and a method for size reduction for each new generation are introduced in the presented QIEA-PSA. Experiments are carried out for several types of DKPs that are much larger in size than those attempted hitherto. QIEA-PSA provides much better solutions than QIEA with much lesser computation times. Even a serial implementation of QIEA-PSA competes favorably on the same problem instances with a parallel implementation of an exact algorithm given recently in literature. A comparison is made which shows QIEA-PSA outperforms a recently applied population based search technique to solve benchmark KP instances. The ideas used for developing QIEA-PSA are general and may be utilized with advantage on other problems.

关键词： quantum inspired evolutionary algorithm Combinatorial optimization Knapsack problem

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Comparative Study Between Q-NAS and Traditional CNNs for Brain Tumor Classification 25th

Comparative Study Between Q-NAS and Traditional CNNs for Bra...

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25th International Conference on Engineering Applications of Neural Networks (EANN)

作者： Cardoso, Fabio Vellasco, Marley Figueiredo, Karla Pontifical Catholic Univ Rio De Janeiro Rio De Janeiro Brazil Univ Estado Rio De Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9783031624940;9783031624957

Brain tumours caused approximately 251,329 deaths worldwide in 2020, with the primary diagnostic method for these tumours involving medical imaging. In recent years, many works and applications have observed the use of Artificial Intelligence-based models using Convolution Neural Networks (CNNs) to identify health problems using images. In our study, we searched for new architectures based on CNN using the Q-NAS algorithm. We compared its performance and number of parameters with traditional architectures such as VGG, ResNet, and MobileNet to classify types of brain tumors in MRI images. The best architecture found by Q-NAS achieved an accuracy of 92% on the test data set, with a model with less than one million parameters, which is much smaller than that found in the selected traditional architectures for this study. It shows the potential of the Q-NAS algorithm and highlights the importance of efficient model design in the context of accurate and feature-aware medical image analysis.

关键词： quantum inspired evolutionary algorithm Neural Architecture Search Classification Brain Tumor

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Design and Implementation of an Effective Fuzzy Logic Controller based on quantum inspired evolutionary algorithm

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JOURNAL OF COMPUTERS 2012年第3期7卷 586-596页

作者： Shill, Pintu Chandra Hossain, Md. Amjad Hossain, Md. Kowsar Amin, Md. Faijul Murase, Kazuyuki Univ Fukui Dept Syst Design Engn 3-9-1 Bunkyo Fukui 910 Japan KUET Dept Comp Sci & Engn khulna 9203 Bangladesh

This paper proposes a new approach based on quantum inspired evolutionary algorithm (QIEA) for effective selection and definition of fuzzy if-then control rules as well as the shapes of membership functions (MFs) to design fuzzy logic controllers (FLCs). The majority of works done on designing FLCs rely on the knowledge base derived from imprecise heuristic knowledge of experienced operators or persons. These traditional methods, however, are cumbersome to implement and very time consuming to evaluate. Our proposed approach is a self-learning adaptive method and decomposes a problem in such a way that leads to more effective knowledge acquisition and improved control performance with the FLCs. In order to verify the effectiveness of this self-learning adaptive method, a standard test-bed, the truck backer-upper problem, is considered as the test problem. During each generation, the rules are updated and the MFs' parameters are altered using a complementary double mutation operator (CDMO) and a discrete crossover (DC). This paper also demonstrates the effect of different fuzzification and defuzzification methods on the response of the FLC. The center of gravity (COG) and modified COG are used as defuzzifier to analyze the results of the fuzzy controller. The experimental results show that the proposed approach with different fuzzification and MCOG to design FLCs performs better than the traditional methods with triangular fuzzification and COG in terms of required time to backing up the truck.

关键词： Fuzzy Logic Controller Fuzzy Rule base quantum inspired evolutionary algorithm Optimization Defuzzification Backing up a truck

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A New quantum inspired algorithmic Approach to Finding Minimum Spanning Tree 20

A New Quantum Inspired Algorithmic Approach to Finding Minim...

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20th IEEE India Council International Conference, INDICON 2023

作者： Kaur, Amrit Bansal, Sulabh Manipal University Jaipur Dept. of Computer Application Jaipur India Manipal University Jaipur Department of Information Technology Jaipur India

ISBN: (纸本)9798350305593

One of the most common and well-known problems in combinatorial optimization is the minimum spanning tree. With the emergence of complex data structures, new, more complex, but asymptotically efficient algorithms have been proposed. In this research, we present a new evolutionary algorithm influenced by quantum mechanics, known as quantum-inspired evolutionary algorithm (QIEA) to find the minimum spanning tree of a complete weighted undirected graph as input and computational results to demonstrate the effectiveness of proposed algorithm. The proposed algorithm is named QIEA-MST and is based on concept and principle such as qubits and the superposition, coherence and the entanglement of different qubits of quantum state. The algorithm is simple and easy to understand and easy to implement. Some types of scientific and engineering problems will benefit from the proposed method. © 2023 IEEE.

关键词： evolutionary algorithm Graph Theory Minimum Cost Spanning Tree quantum inspired evolutionary algorithm Qubits Spanning Tree

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Forecasting annual electricity consumption in China by employing a conformable fractional grey model in opposite direction

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ENERGY 2020年 202卷 117682-117682页

作者： Xie, Wanli Wu, Wen-Ze Liu, Chong Zhao, Jingjie Nanjing Normal Univ Inst EduInfo Sci & Engn Nanjing 210097 Peoples R China Cent China Normal Univ Sch Econ & Business Adm Wuhan 430079 Peoples R China Inner Mongolia Agr Univ Sch Sci Hohhot 010018 Peoples R China

Electric power makes a significant contribution to economic development. Predicting annual electricity consumption is becoming increasingly crucial for electric power utility planning and economic development. To address this problem, a novel conformable fractional grey model in opposite direction is presented to predict annual electricity consumption in China. Firstly, the computational formulas for the novel model are deduced by grey modelling method and the effectiveness of the novel model is proved by matrix perturbation theory. Secondly, the optimal parameters are determined by quantum inspired evolutionary algorithm. Thirdly, two empirical examples are taken to validate the prediction accuracy of the novel model. Finally, the proposed model is applied to predict electricity consumption of Beijing, Fujian and Shandong. The results show that the novel model is superior to other six competitive models. Besides, electricity consumption of these regions in next five years are predicted, which can well serve a benchmark research and provide a relatively reliable reference for economic and electric sectors. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Grey model Conformable fractional accumulation quantum inspired evolutionary algorithm Electricity consumption

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An Effective quantum inspired Genetic algorithm for Continuous Multiobjective Optimization 5

An Effective Quantum Inspired Genetic Algorithm for Continuo...

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5th International Conference on Science in Information Technology (ICSITech)

作者： Abraham, Julisa Bana Wahyunggoro, Oyas Setiawan, Noor Akhmad Univ Gadjah Mada Dept Elect & Informat Engn Yogyakarta Indonesia

ISBN: (纸本)9781728123806

Multiobjective Optimization Problems (MOP) can be found in many issues of scientific research, engineering, and in everyday social life. A MOP problem has several objectives that conflict with one another which must be optimized simultaneously. This paper presents a quantum-inspired evolutionary algorithm (QEA) to solve continuous multiobjective optimization problem (MOP). The proposed method employs Fast Nondominated Sorting and Crowding Distance from NSGA-II and implements all common operators of genetic algorithms (GA), such as crossover and mutations with additional quantum Gate quantum operators. The proposed method is then run in a distributed manner and is proven to be able to significantly outperform the hypervolume and MOEA/D metrics and have hypervolumes that are comparable to NSGA-II while maintaining a better average Delta' in all testing problems. From this result, it is concluded that using quantum-inspired individual genetic algorithms to solve continuous MOP can produce hypervolume and Delta' metrics that are good in all specified test problems.

关键词： Genetic algorithm quantum inspired evolutionary algorithm Fast Nondominated Sorting

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