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5th International Conference System Modeling and Advancement in Research Trends (SMART)

作者： Singh, Jasmeet Singh, Mohit Kurukshetra Univ Dept Elect & Commun Kurukshetra Haryana India

ISBN: (纸本)9781509035434

With the skyrocketing needs of rapid processing speed and miniaturization, classical computers are not able to keep up the pace with these few necessary parameters. As classical computers work on classical mechanics there expansion is at zenith. Due to these limitation quantum mechanics is taking the role of game changer in race of computation. quantum computing studies quantum computer with the quantum mechanics phenomenon of superposition, entanglement, tunneling and annealing to solve problems that can't be solved in life span of human beings. This review aims for summarizing timeline of the quantum computing along with quantum mechanics behind it.

关键词： quantum Computing quantum Bits quantum Information Theory quantum Computers quantum-inspired evolutionary algorithm quantum Entanglement and quantum Superposition

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Optimal time points sampling in dynamic pathway modelling

Optimal time points sampling in dynamic pathway modelling

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26th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Hu, SY Polytech Univ Dept Comp & Informat Sci Brooklyn NY 11201 USA

ISBN: (纸本)0780384393

Modelling cellular dynamics based on experimental data is at the heart of System Biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

关键词： computational system biology quantum-inspired evolutionary algorithm dynamic pathway modelling signal transduction parameter estimation

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quantum evolutionary Membrane algorithm and Its Application for Economic Dispatch in Power System with Wind Farms

Quantum Evolutionary Membrane Algorithm and Its Application ...

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The Second Asian Conference on Membrane Computing(ACMC2013)

作者： Wenjin Luo Li Zhang Manuel Alcazar-Ortega Alexander Domijan Electrical engineering department University at Buffalo NY USA School of Electrical and Information Engineering Xihua Universtiy

Wind is a clean, renewable and reliable energy source and analysis of the economics of wind farms in terms of the cost/MWh energy provided is a primer topic in electricity economics. This paper presents a novel evolutionary algorithm named as quantum evolutionary membrane algorithm, which combines the concept and theory of quantum-inspired evolutionary algorithm and membrane computation, to solve the economic dispatch problem in power system with on land and offshore wind farms. The case derived from real data is used for simulation tests. Another conventional evolutionary algorithm is also used to solve the same problem for comparison. The experimental results show that the proposed algorithm is quick and accurate to obtain the optimal solution which is the minimum cost for electricity supplied by wind farm system.

关键词： membrane computation economic dispatch on land wind farm offshore wind farm quantum-inspired evolutionary algorithm

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An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 171卷 114629-114629页

作者： Cai, Xing Zhao, Huimin Shang, Shifan Zhou, Yongquan Deng, Wu Chen, Huayue Deng, Wuquan Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Chongqing Univ Cent Hosp Dept Endocrinol Key Lab Biorheol Sci & TechnolMinist Educ Chongqing 400014 Peoples R China

In order to overcome the slow convergence speed, poor global search ability and difficult designing rotation angle of quantum-inspired evolutionary algorithm (QEA), an improved quantum-inspired cooperative co evolution algorithm based on combining the strategies of cooperative co-evolution, random rotation direction and Hamming adaptive rotation angle, namely MSQCCEA is proposed, which is employed to propose a new airport gate allocation optimization method in this paper. In the proposed MSQCCEA, the cooperative co evolution strategy is used to improve the global search capability. The random rotation direction strategy is developed to change the quantum evolution direction from one to two in order to avoid local optimal solution and realize the full search of the solution space. A new Hamming adaptive rotation angle strategy is designed to enable individuals to adaptively adjust the rotation angle according to the difference degree between the individual and the target individual, so as to improve the global search ability and convergence speed. A new airport gate allocation optimization method using MSQCCEA is realized to effectively allocate airport gates to the flights. Finally, the knapsack problem and the actual airport gate allocation problem are used to verify the effectiveness of the proposed MSQCCEA and gate allocation optimization method, respectively. The comparison experiment results demonstrate that the proposed MSQCCEA has faster convergence speed and higher convergence accuracy, and the proposed gate allocation optimization method takes on great potential to make decisions for actual airport management.

关键词： quantum-inspired evolutionary algorithm Cooperative co-evolutionary algorithm Multi-strategy Knapsack problem Airport gate allocation

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Disruption Management for Predictable New Job Arrivals in Cloud Manufacturing

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JOURNAL OF INTELLIGENT SYSTEMS 2017年第4期26卷 683-695页

作者： Liu, Mi Yi, Shuping Wen, Peihan Song, Haicao Chongqing Univ Coll Mech Engn Shazheng St Chongqing 400044 Peoples R China Shihezi Univ Coll Mech & Elect Engn Xinjiang 832003 Peoples R China

Manufacturing resources are shared and centrally managed on the cloud platform in cloud manufacturing, which is a new model of modern manufacturing. The production data are collected, which can be used to predict the manufacturing events. Based on those, disruption problems of scheduling should be researched from a new point of view. In this paper, new job arrivals were considered as the disruption event. The time of the occurrence of disruption was predictable in contrast to uncertainty. Alternative subcontractors chosen from the cloud platform were available for outsourcing with different processing prices and transporting distances. The objective of the original scheduling, the deviation between the new schedule and the old one, and the outsourcing cost were all considered. To express the problem, mathematical models and a three-field notation model were constructed. To solve the problem, a hybrid quantum-inspired chaotic group leader optimization algorithm was proposed, in which a hybrid encoding way was applied. To verify the algorithm, experiments were carried out. The results showed that the proposed algorithm performs well.

关键词： Cloud manufacturing disruption management quantum-inspired evolutionary algorithm group leader optimization algorithm outsourcing

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Efficient Solutions of the Density Classification Task in One-Dimensional Cellular Automata: Where Can They Be Found?

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COMPLEX SYSTEMS 2020年第3期29卷 669-688页

作者： Laboudi, Zakaria Univ Oum El Bouaghi RELA CS Lab 2 Oum El Bouaghi Algeria

The density classification task is one among other benchmark problems for studying the ability of cellular automata to solve problems through emergent collective computations. The density classification task attempts to find a local rule that can perform majority voting in an arbitrary initial configuration of a cellular automaton. Solutions for this problem were designed by means of several training mechanisms, more particularly optimization algorithms, due to the lack of standard procedures for selecting suitable local rules. In this paper, we propose new investigations into density determination in one-dimensional cellular automata of radius r = 4. We show that our proposal allows retaining a considerable number of unknown rules, some of which may outperform the current efficient solutions. Moreover, we give explanations about the computational mechanisms making global computations emerge so that the considered problem is solved. This is a key element for improving both the general understanding of the way in which computational tasks are solved by emergence and the selection of suitable local state-transitions rules.

关键词： complex systems emergent global computation cellular automata density determination symmetry property number-conserving property quantum-inspired evolutionary algorithm

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An Improved quantum Evolution algorithm and Performance Analysis

An Improved Quantum Evolution Algorithm and Performance Anal...

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2012年计算机应用与系统建模国际会议

作者： Zhang Yi Yang Xiuxia Zhou Shaolei Department of Control Engineering Naval Aeronautical and Astronautical University

To tackle the shortcoming of deficient using of feedback information in quantum-inspired evolutionary computing and catastrophe such as quantum mutation, the improved quantum-inspired evolutionary algorithm (IQEA) is proposed. The crossover and mutation operators of genetic algorithm are introduced to the quantum evolution algorithm, at the same time, the better individuals information is applied to the generation of the population applying the probability evaluation model of the estimation of distribution algorithm. Theory analyze and test function simulation experiments show that the improved quantum genetic algorithm is characterized by rapid convergence , excellent robustness and so on.

关键词： Multiple quantum computation quantum-inspired evolutionary algorithm genetic optimization estimation of distribution

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A method for fault diagnosis of analog circuits based on optimized fuzzy inference

A method for fault diagnosis of analog circuits based on opt...

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Pacific-Asia Conference on Circuits, Communications and Systems, PACCS

作者： Zhiyong Luo Min Xiang Haofei Xie Ping Wang Key Laboratory ofNetwork control & Intelligent Instrument Ministry ofEducation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781424479696

Because of the difficulty of disposing of fuzzy inference rules for fault diagnosis, a systematic approach for fault diagnosis of analog circuits based on optimized fuzzy inference is presented. The fuzzy logic system for based on fuzzy inference analog circuits fault diagnosis, quantum-inspired evolutionary algorithm (QEA) is used to optimize the membership function of the rules of the fault diagnosis fuzzy logic system, then self adapted genetic algorithm to select the optimum fuzzy rule aggregate, so the number of fuzzy rules is decreased to make it easy to dispose fault diagnosis of analog circuits. The simulation results of a analog power magnifier circuits show the fault diagnosis method of the analog circuits with optimized fuzzy inference is effective.

关键词： adapted genetic algorithm analog circuits fault diagnosis fuzzy inference quantum-inspired evolutionary algorithm Fault diagnosis analog circuits Fuzzy inference Fuzzy logic Fuzzy rules fuzzy logic system Membership functions

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QEAM: an Approximate algorithm Using P Systems with Active Membranes

QEAM: an Approximate Algorithm Using P Systems with Active M...

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The Second Asian Conference on Membrane Computing(ACMC2013)

作者： Gexiang Zhang Jixiang Cheng Marian Gheorghe Florentin Ipate School of Electrical Engineering Southwest Jiaotong University Department of Computer Science The University of Sheffield Regent Court Portobello StreetSheffield S1 4DP UK Faculty of Mathematics and Computer Science University of Bucharest Academiei 14Bucharest Romania Faculty of Mathematics and Computer Science University of Pite stiTrgu din Vale 1 110040 Pite sti Romania

This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compartments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark satisfiability problems show that QEAM outperforms QEPS (quantum-inspired evolutionary algorithm based on P systems) and its counterpart quantum-inspired evolutionary algorithm.

关键词： Membrane computing active membranes approximate optimization approach quantum-inspired evolutionary algorithm satisfiability problem

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