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4th International Conference on Cloud Computing and Security (ICCCS)

作者： Zheng, Yu Zhang, Zhenrong Fang, Wei Liu, Wenjie Nanjing Univ Informat Sci & Technol Sch Comp & Software Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Jiangsu Peoples R China

ISBN: (纸本)9783030000219;9783030000202

The computational simulations under the two dimensional hydrophobic-polar (2D-HP) model from protein's amino is a fundamental and challenging problems in computational biology. In this paper, we propose an improved quantum-inspired evolutionary algorithm based on variable angle-distance rotation strategy (QEA-VAR) for this NP-hard combinatorial protein folding problems. The QEA-VAR method is based on the concept and principles of quantum computing, such as quantum bits, quantum rotation gates and superposition of states. Comparing to the previously well-known evolutionary algorithm for the protein folding problem, QEA-VAR can find optimal or near-optimal energy structure from the benchmark sequences with a small simulating samples. Moreover, the proposed method's global convergence is faster than the other evolutionary algorithms. The application studies have demonstrated the superior performance and feasibility of the proposed algorithm for the protein folding problem.

关键词： quantum-inspired evolutionary algorithm quantum angle-distance rotation (2D-HP) hydrophobic-polar Protein folding

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Fake-Face Image Classification using Improved quantum-inspired evolutionary-based Feature Selection Method

Fake-Face Image Classification using Improved Quantum-Inspir...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Mittal, Himanshu Saraswat, Mukesh Bansal, Jagdish Chand Nagar, Atulya Jaypee Inst Informat Technol Noida India South Asian Univ New Delhi India Liverpool Hope Univ Liverpool Merseyside England

ISBN: (纸本)9781728125473

Deep learning models have been quite successful in discriminating synthesized or edited fake-face images. However, in the case of small training data, transfer-learning is rather preferable. This is a complex process for high dimensional feature space due to the curse of dimensionality. To mitigate the same, this paper proposes a new feature selection method for the classification of manually created fake-face images. In the proposed method, a pre-trained deep learning model is used to extract features of an image. Next, an optimal feature subset is selected from the extracted features through an improved quantum-inspired evolutionary algorithm. Lastly, the elicited features are considered to perform the classification. Experiments are conducted on a publicly available manually created fake-face image dataset, namely Real and Fake Face Detection by Yonsei University. The performance of the proposed method is compared with two methods in terms of classification accuracy and the number of selected features. The experimental comparisons exhibit that the proposed method achieves promising results among the considered methods.

关键词： Deep Learning Fake-Face Image Classification Feature Selection quantum-inspired evolutionary algorithm

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Hybrid evolutionary algorithm for Multi-Objective Job Shop Scheduling

Hybrid Evolutionary Algorithm for Multi-Objective Job Shop S...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Qin, Chaoyong Zhu, Jianjun Zheng, Jianguo Guangxi Univ Sch Math & Informat Sci 100 Daxue Rd Nanning 530004 Peoples R China Donghua Univ Sch Business & Management Shanghai Peoples R China

ISBN: (纸本)9781424447541

In this paper, a hybrid quantum-inspired evolutionary algorithm (HQEA) for Multi-objective JSSP is proposed In the HQEA, a quantum bit is employed to represent processing priority of two operations executed on the same machine Updating operator of quantum gate is used to speed up individuals converge toward the current best solution Conventional crossover is performed as well However, an individual produced by updating operator and crossover operator may represent no feasible schedule To repair Illegal solution, harmonization algorithm is employed At last, local search operator is designed to exploit the space around the current best solution Experiments are conducted on benchmark test problems, the results show that the proposed approach can search for the near-optimal and non dominated solutions by optimizing the makespan and mean flow time The results of comparisons demonstrates, that the proposed approach outperform another well established multi-objective evolutionary algorithm based JSSP approach

关键词： Multi-objective job shop scheduling quantum-inspired evolutionary algorithm Local search

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Adaptive niche quantum-inspired immune clonal algorithm

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NATURAL COMPUTING 2016年第2期15卷 297-305页

作者： Liu, Jianyong Wang, Huaixiao Sun, Yangyang Li, Ling PLA Univ Sci & Technol Coll Field Engn Nanjing 210007 Jiangsu Peoples R China PLA Univ Sci & Technol Coll Def Engn Nanjing 210007 Jiangsu Peoples R China

The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effectively and make the function converge to as many as possible extreme value points. The quantum coding can better explore the solution space, the niche mechanism ensures the algorithm to converge to multi-extremum and the adaptive mechanism is introduced according to the characteristics of each procedure of the algorithm to improve the effect of the algorithm. Example analysis shows that the ANQICA is better in exploration and convergence. Therefore, the ANQICA can be used to solve the problem of multi-modal function optimization effectively.

关键词： Optimization algorithm quantum-inspired evolutionary algorithm Niche Adaptive Immune clonal algorithm

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A robust automatic phase-adjustment method for financial forecasting

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KNOWLEDGE-BASED SYSTEMS 2012年 27卷 245-261页

作者： Araujo, Ricardo de A. Univ Fed Pernambuco Informat Ctr Recife PE Brazil

In this work we present the robust automatic phase-adjustment (RAA) method to overcome the random walk dilemma for financial time series forecasting. It consists of a hybrid model composed of a qubit multilayer perceptron (QuMLP) with a quantum-inspired evolutionary algorithm (QIEA), which is able to evolve the complete QuMLP architecture and parameters, as well as searches for the best time lags to optimally describe financial phenomena. In the attempt to improve the QuMLP parameters supplied by QIEA, each individual of the QIEA population is further trained by the complex back-propagation (CBP) algorithm. Also, for each forecasting model generated, we use a phase fix procedure to adjust time phase distortions that appear in financial time series. Furthermore, an experimental analysis is conducted with the proposed method through six real world financial time series, and the obtained results are discussed and compared to results found with the best methods recently presented in the literature. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Qubit multilayer perceptron quantum-inspired evolutionary algorithm Hybrid models Financial forecasting Random walk dilemma

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A membrane algorithm with quantum-inspired subalgorithms and its application to image processing

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NATURAL COMPUTING 2012年第4期11卷 701-717页

作者： Zhang, Gexiang Gheorghe, Marian Li, Yuquan SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Univ Pitesti Dept Comp Sci Pitesti 110040 Romania

This paper presents a membrane algorithm, called MAQIS, by appropriately combining concepts and principles of membrane computing and quantum-inspired evolutionary approach. MAQIS has four distinct features from the membrane algorithms reported in the literature: initial solutions are only inside the skin membrane;different regions separated by membranes have different components of the algorithm;all the components inside membranes cooperate to produce offspring in a single evolutionary generation;communication rules are performed in a single evolutionary step. Extensive experiments conducted on knapsack problems show that MAQIS outperforms five counterpart approaches and our previous work. The effectiveness of MAQIS is also verified in the application of image processing.

关键词： Membrane computing Membrane algorithm quantum-inspired evolutionary algorithm Knapsack problem Image sparse decomposition

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quantum-inspired hybrid algorithm for integrated process planning and scheduling

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2018年第6期232卷 1105-1122页

作者： Liu, Mi Yi, Shuping Wen, Peihan Chongqing Univ Coll Mech Engn Shazheng St Chongqing 400044 Peoples R China

Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantum-inspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.

关键词： Integrated process planning and scheduling quantum-inspired evolutionary algorithm group leader optimization algorithm chaotic maps hybrid algorithm

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quantum-inspired Differential Evolution with Particle Swarm Optimization for Knapsack Problem

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2015年第5期31卷 1757-1773页

作者： Zouache, Djaafar Moussaoui, Abdelouahab Ferhat Abbas Setif 1 Univ Dept Comp Sci Setif 19000 Algeria

This paper presents a new hybrid algorithm called QDEPSO (quantum inspired Differential Evolution with Particle Swarm Optimization) which combines differential evolution (DE), particle swarm optimization method (PSO) and quantum-inspired evolutionary algorithm (QEA) in order to solve the 0-1 optimization problems. In the initialization phase, the QDEPSO uses the concepts of quantum computing as the superposition state of qubits as well as the quantum measurement to represent and generate the diversity of the initial solutions. The second phase is an alternation between the DE operations (mutation, crossover and selection) and the adaptation of update formula of the velocity and the position of PSO algorithm. The effect of this step is to determine the rotation quantum angle to explore search space of solutions. To evaluate the performance of the proposed algorithm, we use the knapsack 0-1 problem as a class of combinatorial optimization NP-hard problems. The obtained results for 0-1 knapsack problem have proven the superior performance of QDEPSO compared to quantum-inspired evolutionary algorithm (QEA), Adaptive quantum-inspired Differential Evolution algorithm (AQDE), quantum Swarm evolutionary algorithm (QSE) and quantum inspired Harmony Search algorithm (QIHSA).

关键词： differential evolution quantum-inspired evolutionary algorithm particle swarm optimization knapsack problem

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Stochastic Optimal Dispatch of Power System Considering the Correlation of Multiple Wind Farm Outputs

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第6期44卷 616-627页

作者： Yang, Hongming Zhang, Yongxi Wang, Shuang Zhao, Junhua Lai, Mingyong Dong, Zhao Yang Huang, Jingjie Changsha Univ Sci & Technol Sch Elect & Informat Engn Hunan Prov Key Lab Smart Grids Operat & Control Hunan Prov Engn Res Ctr Elect Transportat & Smart Changsha 410114 Hunan Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Shishi Power Ltd Liabil Co Quanzhou Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Guangdong Peoples R China

As an important way of addressing energy and environmental challenges, the market share of wind power generation has increased dramatically in the past decade and has introduced significant challenges to power system operation. In this article, the tail correlation between multiple wind farms is studied. The joint probability distribution of multiple wind farms is estimated by employing the Gumbel copula function. Based on the estimated joint probability distribution, a stochastic optimal dispatch model is proposed to take into account the chance constraints of energy utilization from multiple wind farms in the power system. The sample average approximation method is employed to handle the chance constraints in the proposed model, so as to transform stochastic optimal dispatch into a deterministic non-linear optimization problem. The quantum-inspired evolutionary algorithm is used to solve the proposed model. The proposed model and algorithm are tested with comprehensive case studies to demonstrate their effectiveness.

关键词： stochastic optimal dispatch correlation between multiple wind farm outputs Gumbel copula function chance constraints sample average approximation quantum-inspired evolutionary algorithm

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Interval multi-objective quantum-inspired cultural algorithms

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NEURAL COMPUTING & APPLICATIONS 2018年第3期30卷 709-722页

作者： Guo, Yi-nan Zhang, Pei Cheng, Jian Wang, Chun Gong, Dunwei China Univ Min & Technol Sch Informat & Elect Engn Xuzhou Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

It had been proved that the knowledge may promote more efficient evolution. Considering the knowledge defined in different form, we present interval multi-objective quantum-inspired cultural algorithms so as to effectively utilize the implicit information embodied in the evolution to promote more efficient search. It adopts the dual structure derived from cultural algorithm. In population space, the rectangle's height of each allele in real-encoding quantum individuals is calculated in terms of the possibility dominant rank, instead of the relative fitness values. Three kinds of crowding operators are defined, including the crowding distance of hypercube, the harmonic distance of hypercube and the coverage rate of hypercube to grids, to measure the crowding degree among evolutionary individuals. In belief space, the knowledge is used to guide selection and mutation operations of evolutionary individuals and the update operation of quantum individuals. The statistic simulation results for four benchmark functions indicate that the solutions obtained from the proposed algorithms more close to the true Pareto front uniformly and the uncertainty of non-dominant solutions is less. Furthermore, the knowledge extracted from the evolution plays a positive role in improving the convergence and distribution.

关键词： Knowledge The crowding degree The rectangle's height quantum-inspired evolutionary algorithm Interval multi-objective optimization

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