Two Improvements in Grover＇s Algorithm

When the current Grover algorithm is applied to searching some targets in an unsorted quantum database, the differences of the significance for each target are not taken into consideration. When the targets are more than a quarter of the total items, the probability of finding targets rapidly falls with the increase of the targets, and when the targets are more than half of the total items, the algorithm will be invalidated. Aiming at these problems, flrstly, an improvement based on the weighted targets is proposed in which each target is endowed a weight coefficient according to its significance. Using these different weight coefficients, we give a quantum superposition of all target states, which may make the probability for each target equal to its weight coefficient. Secondly, an improved phase matching is proposed in which two directions of phase rotation are same, and two magnitudes are determined by the inner product of the superposition of target states and the initial system state. When this inner product is more than 1/2, using the improved phase matching. The targets can be found with a probability of 100% and by the only one Grover iteration. Finally, the validity of these measures is validated by a simple searching example.     

Secure Authentic Digital Signature Scheme Using Quantum Fingerprinting

An authentic digital signature scheme using quantum fingerprinting was presented. In this scheme, Bob prepares N EPR （Einstein-padolsky-rosen） pairs then distributes the second qubit of each pair to Alice. After performing a local unitary operation on these qubits, Alice encrypts them and sends them hack. Thus she can encode message M and get its signature IS）, while Bob can recover the message M and directly authenticates the signature IS） by performing Bell-base measurement on the combined EPR pairs. If some disputes happen, the process may enter into an arbitration phase. Our scheme was designed to use quantum cryptographic algorithm to guarantee unconditional security （different from computational security）, as well as use quantum fingerprinting to avoid trick attacks and to reduce the communication complexity.     

An Improved Genetic Algorithm for Test Cases Generation Oriented Paths

This paper discusses a method that can automatically generate test cases for selected paths using a special genetic algorithm. The special algorithm is called Queen-bee evolutionary genetic algorithm（QBEA）. In this algorithm, sequences of operators iteratively executes for test cases to evolve to target paths. The best chromosome called queen among the current population is crossover with drones selected according to a certain crossover prob- ability, which enhances the exploitation of searching global optimum. A comparative experiment results prove that the proposed method is actually a great improvement in opti- mization efficiency and optimization effect.     

Automatic Design of P Systems for Five Basic Arithmetic Operations within One Framework

To solve the programmability of a mem- brane system, this paper presents an automatic design method of a cell-like P system framework for performing five basic arithmetic operations. This method proved that different P systems can be designed with same framework by programming. A technique is introduced to remove re- dundant rules in the process of the design of a P system. The effectiveness and feasibility of this method is verified by experimental results.     

An Efficient Quantum Mechanism Based Fair Non-Repudiation Protocol

In this paper, an efficient quantum public key signature based fair non-repudiation message exchanging protocol is proposed. This protocol could guarantee fairness which means both the receiver and the sender could get the evidences of each other at the end of message transmission. Compared with the traditional nonrepudiation protocols, this protocol provides higher security because of the superiority of quantum cryptography as well as enhances the running time efficiency and reliability since it avoids the involvement of TTP （Trusted third party）.     

A Population-Membrane-System-Inspired Evolutionary Algorithm for Distribution Network Reconfiguration

This paper proposes a Population- membrane-system-inspired evolutionary algorithm （PM- SIEA）, which is designed by using a population P system and a Quantum-inspired evolutionary algorithm （QIEA）. PMSIEA uses the population P system with three cells to organize three variants of QIEAs, where communications between cells are performed at the level of genes, instead of the level of individuals reported in the existing membrane algorithms. This work provides a useful framework for synthesizing different algorithms at macro level and exchanging genic information at micro scale. Experimental results conduced on knapsack problems show that PMSIEA is superior to four representative QIEAs and our previous work with respect to the quality of solutions and the elapsed time. We also use PMSIEA to solve the optimal distribution system reconfiguration problem in power systems for minimizing the power loss.     

A Novel Least-Entanglement-Assisted Asymmetric Quantum Codes Based on Sliding Grill

We propose new methods for constructing novel entanglement-asslsted asymmetric quantum codes based on combinatorial designs and sliding grill. Based on a special case of combinatorial designs, our proposed method can construct a type of parity check matrix with greater column weight and better decoding performance. Combined with the characteristic of asymmetric quantum chan- nel, we propose new method of sliding grill. Mother matrix and submatrix can be constructed. The null space of the parlty-check matrix gives entanglement-asslsted asymmetric quantum code. Tanner graph of the code has a girth of at least 6. Our proposed quantum code has higher code rate with the same error correction capability. These families of entanglement-asslsted asymmetric quantum codes consume only one copy of maximally entangled state （ebit）, and outperform other quantum codes based on similar construction method according to our numerical computation.     

A Fuzzy Similarity-based Clustering Optimized by Particle Swarm Optimization

Traditional fuzzy clustering algorithms based on objective function is unable to determine the optimum number of clusters, sensitive to the initial cluster centers, and easily sunk into the issue of local optimum. A Fuzzy similarity-based clustering （FSBC） algorithm is proposed in this paper. This method consists three phases： first, the objective function is modified by integrating Fuzzy C-means （FCM） and Possibilistic C-means （PCM） method; second, using the density function from data for similarity-based clustering to automatically generate initial prototype without requesting users to specify; finally, the iteration process optimized by Particle swarm optimization （PSO） to obtain appropriate adjustment parameters that can provide better results, which avoids the local minimum problems of traditional methods. The experimental results on the synthetic data and UCI standard data sets show that the proposed algorithm has greater searching capability, less computational complexity, higher clustering precision.     

An improved quantum-inspired evolutionary algorithm for coding resource optimization based network coding multicast scheme

This paper investigates how to minimize the required coding resources in network-coding-based multicast scenarios. An evolutionary algorithm (MEQEA) is proposed to address the above problem. Based on quantum-inspired evolutionary algorithm (QEA), MEQEA introduces multi-granularity evolution mechanism which allows different chromosomes, at each generation, to have different rotation angle step values for update. In virtue of this mechanism, MEQEA significantly improves its capability of exploration and exploitation, since its optimization performance is no longer overly dependant upon the single rotation angle step scheme shared by all chromosomes. MEQEA also presents an adaptive quantum mutation operation which is able to prevent local search efficiently. Simulations are carried out over a number of network topologies. The results show that MEQEA outperforms other heuristic algorithms and is characterized by high success ratio, fast convergence, and excellent global-search capability.     

Time Agility Optimization for Dual-stage Collaborative Spectrum Sensing

Cooperative spectrum sensing has been shown to be an effective method to mitigate the impact of hidden terminal and shadow fading in cognitive radio networks. Currently most works focused on either coop- erative sensing or sensing scheduling as a viable means to improve the detection performance without studying their interactions. This paper proposed a dynamic and variable time-division multiple-access scheduling mechanism that was incorporated into a Dual-stage collaborative spectrum sensing （DCSS） model in a less ideal radio environment. Moreover, this paper derived closed-form expressions of sensing time for DCSS, and addressed the critical range of timeslot length. An optimized algorithm to minimize sens- ing time was deduced and verified. The simulation results indicate that average sensing time with DCSS is shortened by 11.5~V0 when compared with that of the Single collabora- tive spectrum sensing （SCSS） while guaranteeing detection error rate requirement of less than 1%.     

基于EQPSO-GS优化SVM的入侵检测模型研究

支持向量机(Support Vector Machine,SVM)的惩罚因子C和核参数σ影响模型分类精度和泛化性能。传统进化算法优化支持向量机时主要存在早熟收敛、优收敛速度慢、收敛精度低以及泛化性能不高等问题。针对这些问题,将精英策略与量子粒子群算法融合,提出了精英量子粒子群算法(Elite Quantum Particle Swarm Optimization Algorithm,EQPSO),既提高了种群收敛速度,也一定程度上避免了算法局部收敛,凭借其全局探索能力初探参数范围,并通过网格搜索算法(Grid Search Algorithm,GS)提高开发能力做进一步的优化,将优化好的SVM模型用于入侵检测。利用入侵检测数据集UNSW-NB15对传统的进化算法优化支持向量机模型进行仿真实验,通过交叉验证对比EQPSO-GS-SVM、QPSO-SVM、PSO-SVM、DE-SVM模型,得出EQPSO-GS优化的SVM模型具有更好的泛化性能。     

Learning Bayesian Networks with the Combination of Evolutionary Algorithm and EMI Method

Most Bayesian network （BN） learning algorithms use EMI algorithm to deal with incomplete data. But EMI algorithm is of low efficiency due to its iterative parameter refinement, and the problem will become even worse when multiple runs of EMI algorithm are needed. Besides, EMI algorithm usually converges to local maxima, which also degrades the accuracy of EMI based BN learning algorithms. In this paper, we replace EMI algorithm used in BN learning tasks with EMI method to deal with incomplete data. EMI is a very efficient method, which estimates probability distributions directly from incomplete data rather than performs iterative refinement of parameters. Base on EMI method, we propose an effec- tive algorithm, namely EMI-EA. EMI-EA algorithm uses EMI method to estimate probability distribution over local structures in BNs, and evaluates BN structures with a variant of MDL scoring function. To avoid getting into local maxima of the search process, EMI-EA evolves BN struc- tures with an Evolutionary algorithm （EA）. The experi- mental results on Alarm, Asia and an examplar network show that EMI-EA algorithm outperforms EMI-EA for all samples and E-TPDA algorithms for small and middle size of samples in terms of accuracy. In terms of efficiency, EMI-EA is comparable with E-TPDA algorithm and much more efficient than EMI-EA algorithm. EMI-EA also out- performs EMI-EA and M-V algorithm when learning BNs with hidden variables.     

Evolutionary Generation Approach of Test Data for Multiple Paths Coverage of Message-passing Parallel Programs

Test data generation, the premise of soft- ware testing, has attracted scholars in the software engi- neering community in recent years. Influenced by task par- titioning, process scheduling, and network delays, parallel programs are executed in a non-deterministic way, which makes test data generation of parallel programs different from that of serial programs in essence. This paper inves- tigated the problem of generating test data for multiple paths coverage of message-passing parallel programs. A mathematical model of the above problem was built based on each given path and its equivalent ones. It was solved by using a genetic algorithm to generate all desired data in one run. The proposed method was applied to five bench- mark programs, and compared with the existing methods. The experimental results show that the proposed method greatly shortens the number of iterations and time con- sumption without reducing the coverage rate.     

Partition-Based Cache Replacement to Manage Shared L2 Caches

The Least recently used （LRU） replacement has been widely employed by on-chip shared caches. With the increase of cache associativity, the performance gap between LRU and theoretical optimal replacement algorithm is significant, motivating us to design alternative replacement algorithms to improve cache performance. We propose a Partltlon-based cache replace- ment （PCR） algorithm to manage multi-core shared L2 caches. PCR takes into consideration recency and fre- quency information of cache access, and cache resource interference among multiple competing applications. PCR is effective to flter out the less frequently reused blocks through a dynamic insertion/promotlon policy based on the target partitions~ and ensures that some fraction of the working set is retained in the cache to contribute to more hits. Our evaluation shows that PCR improves performance of an 8-core system by an average of 11.3% over LRU-based cache.     

HIAWSC： An Immune Algorithm Based Heuristic Web Service Composition Framework

The introduction of of web services has led to web service composition being a focus of many researchers. Composing web services using workflows is seen as the most realistic method from an industrial viewpoint. Amongst other method, the use of natural computing methods has been proposed previously to automate web service composition. The need for a fast response when computing the most suitable sequence of services is addressed in this paper. In particular, we propose a novel heuristic immune algorithm with an efficient encoding and mutation method. The algorithm involves two steps： an immune selection operation, which is maintaining antibody population diversity and the clonal selection. The use of a vaccine during the evolution provides heuristic information that accelerates the convergence. Our experimental results illustrate that the proposed heuristic immune algorithm is very effective in improving the convergence speed. We also provide a schema analysis for this method.     

The Multi-Weight Neuron with Geometry Algorithm and Its Application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multiweight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes： fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.     

Fingerprint Recognition on Various Authentication Sensors

Fingerprint is a very popular and an ancient biometric technology to uniquely identify a person. In this paper, a fingerprint matcher is proposed which uses the global and local adaptive binarization and global minutia features. The fingerprint data is collected using three different authentication devices based on optical sensors. The experimental results are compared with the National Institute of Standards and Technology(NIST) Bozorth algorithm and various authentication fingerprint sensors. The accuracy of the proposed algorithm has been improved significantly compared with that of the NIST Bozorth algorithm.     

A Security-enhanced Key Distribution Scheme for AODVjr Routing Protocol in ZigBee Networks

ZigBee network is a kind of flexible wireless network technology for control and monitoring applications and new techniques of security measures are essential for high-survivability network. Based on the effectiveness of AODVjr （Ad hoe on-demand distance vector junior） routing protocol in ZigBee networks, in this paper, we proposed a new security-enhanced key distribution scheme for AOD- Vjr routing protocol in ZigBee networks. The key distribution was scheme implemented by combining the parameter exchange of Diffle-Hellman algorithm into the handshake protocol for node＇s joining a ZigBee network. Especially, the major improvement of Diffle-Hellman algorithm is to mix the parameters of key exchange with XOR operation so as to defend against typical man-in-the-middle attacks. Meanwhile, we analyzed the effect of XOR operation on key parameters by deducing the related theorems. Through the security analysis, the key distribution scheme demonstrates stronger security. We can verify that the AODVjr routing protocol with security enhancement has larger flexible application in ZigBee networks.     

A Memetic Algorithm for Metabolomics Based Feature Weighting Data Classification

A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCIVIA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed al- ternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for op- timizing the linkage weight vectors a feature weighting al- gorithm for metabolomlcs data is formed to identify relevant metabolite features and reveal their exact relation- ships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.