《诗经》中的组合问及其修辞价值

《诗经》组合问在形式上有二句组合问、多句组合问等类型,一般都有疑问标记,且在形式上较《离骚》、《天问》简单。作为问答序列(含零答句形式),《诗经》组合问颇具修辞价值,有助于接受者认知、审美与认同。

《老子》中组合问及其互文辩证性

《老子》中的组合问包含二句组合问和多句组合问,体现出一定的互文辩证性。在形式上有利于语篇的衔接连贯,有助于互文见义,在内容上凸显了语篇的思辨性。在时间上相去不远的"苏格拉底的诘难"与老子的组合问在形式和旨趣上较为接近。

《边城》和《金锁记》组合问的语用比较

沈从文《边城》和张爱玲《金锁记》较为典型的组合问共49例,以“形合”为主,即绝大部分组合问内部至少有一个构成问句带有疑问标记语,仅仅只有1例是“意合”(组合问内部构成问句均无标记,均为零标记)而成的。《边城》和《金锁记》中的二句组合问句在各自整体语篇中组合问句所占的比重最大。《边城》中的疑问标记语在形式上较为单一,特指问、是非问、正反问、选择问等各种形式的句类呈现较为均衡。《金锁记》的疑问标记语则在形式上较为复杂,各种配套的标记语频繁使用。各种形式的句类之间在数量上不太平衡,比如设问的比例明显较大。组合问作为一种特殊的句组,首句引领关涉后续句。在组合问中,首句的后续句是以问答问,其句际衔接较为特殊。无论是首句的数量还是相对比例,《边城》中的设问(非真实疑问句)均小于《金锁记》,沈从文《边城》的主体间性在此意义上比张爱玲《金锁记》更强。

《墨子·兼爱》和《庄子·齐物论》组合问的语用比较

《墨子·兼爱》和《庄子·齐物论》在语用上具有一定的可比性。综览二者的语篇分布特征和总体疑问标记,藉此可初步管窥二者致思理路之异同,还可借助疑问语气词这一颇具口语色彩的语用标记为《史记》语焉不详的墨子里籍等历史文化问题提供实证性佐证。其次,《墨子·兼爱》和《庄子·齐物论》的问句组合形式可以形成句式、语气、话轮等方面的错综。组合问内部问句之间有对比、解说、递进等语义关联。

空间组合理论在景观设计中的应用

将建筑学中有关空间及空间组合的理论引入到现代景观设计当中,介绍了空间的概念、范围、特性,以及空间组合的基本原则、要求和处理手法,并结合国内外著名园林实例,分析了空间组合理论在景观设计中的实际应用.

ADAPTIVE GENETIC ALGORITHM BASED ON SIX FUZZY LOGIC CONTROLLERS

The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.

Feature combination via importance-inhibition analysis

A new method for combining features via importance-inhibition analysis （IIA） is described to obtain more effective feature combination in learning question classification. Features are combined based on the inhibition among features as well as the importance of individual features. Experimental results on the Chinese questions set show that, the IIA method shows a gradual increase in average and maximum accuracies at all feature combinations, and achieves great improvement over the importance analysis（IA） method on the whole. Moreover, the IIA method achieves the same highest accuracy as the one by the exhaustive method, and further improves the performance of question classification.

Hopfield neural network based on ant system

Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters. This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.

A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach

Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.

Joint Resource Allocation Using Evolutionary Algorithms in Heterogeneous Mobile Cloud Computing Networks

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

Membrane-inspired quantum bee colony optimization and its applications for decision engine

In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.

Optimizing bus services with variable directional and temporal demand using genetic algorithm

As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost and considering a temporally and directionally variable demand. An integrated bus service, consisting of all-stop and stop-skipping services is proposed and optimized subject to directional frequency conservation, capacity and operable fleet size constraints. Since the research problem is a combinatorial optimization problem, a genetic algorithm is developed to search for the optimal result in a large solution space. The model was successfully implemented on a bus transit route in the City of Chengdu, China, and the optimal solution was proved to be better than the original operation in terms of total cost. The sensitivity of model parameters to some key attributes/variables is analyzed and discussed to explore further the potential of accruing additional benefits or avoiding some of the drawbacks of stop-skipping services.

Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm

The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.

Investigation of a Network Failure Problem with a Significant Path, from the Perspective of Crisis Management

The objective of this study is to investigate a network failure problem with a significant path, emerging from the context of crisis management, such as in the case of natural disasters. For a given tree with m failed edges, we assume that we have sufficient resources to recover k edges of the m edges. Each node has a positive weight. In this situation, we consider which k edges should be fixed in order to maximize the sum of the weights of the nodes reachable from the significant path. In this paper, we formulate such a problem as a combinatorial problem. Further, we show that a part of our problem may be solved by translating it into the terms of the so-called tree knapsack problem.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

ANALYSIS OF AFFECTIVE ECG SIGNALS TOWARD EMOTION RECOGNITION

Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.

GA and PSO culled hybrid technique for economic dispatch problem with prohibited operating zones

This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.

MODS： A Novel Metaheuristic of Deterministic Swapping for the Multi-Objective Optimization of Combinatorials Problems

This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.

A Water Line Network Failure Application of Network Design Problems

This study investigated a water supply recovery problem involving municipal water service piping. The problem consisted in recovering full service after network failure, in order to rapidly satisfy all urgent citywide demands. The optimal recovery solution was achieved through the application of so-called network design problems （NDPs）, which are a form of combinatorial optimization problem. However, a conventional NDP is not suitable for addressing urgent situations because （1） it does not utilize the non-failure arcs in the network, and （2） it is solely concerned with stable costs such as flow costs. Therefore, to adapt the technique to such urgent situations, the conventional NDP is here modified to deal with the specified water supply problem. In addition, a numerical illustration using the Sendai water network is presented.

Genetic approach for Cell-by-Cell dynamic spectrum allocation in the heterogeneous scenario

In this paper,a genetic algorithm (GA) is investigated to deal with cell-by-cell dynamic spectrumallocation (DSA) in the heterogeneous scenario with temporal and spatial traffic demand changes,whichis also known as a difficult combinatorial optimization problem.A new two-dimensional chromosome encodingscheme is defined according to characteristics of the heterogeneous scenario,which prevents forminginvalid solutions during the genetic operation and enables much faster convergence.A novel randomcoloring gene generation function is presented which is the basic operation for initialization and mutationin the genetic algorithm.Simulative comparison demonstrates that the proposed GA-based cell-by-cellDSA outperforms the conventional contiguous DSA scheme both in terms of spectral efficiency gain andquality of service (QoS) satisfaction.