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Aiming at the floorplanning problem with only hard modules, it is usually constructed as a combinatorial optimization model, but the time cost of solving process is high. In order to improve the solving efficiency, this paper proposed a floorplanning algorithm based on non-smooth analytic mathematical programming. Based on the coordinate representation of modules, this paper established a non-smooth mathematical programming model, which was a generalization of the optimization models corresponding to various optimization stages of different cases that were solved by the conjugate sub-gradient algorithm (CSA). Aiming at the fixed-outline floorplanning problem, this paper achieved wirelength optimization under fixed-outline constraint through the general framework consisted of three stages: global floorplanning, legalization and local optimization. To address the case without fixed-outline constraint, this paper proposed a conjugate sub-gradient algorithm with golden section strategy (CSA_GSS). The algorithm adopted golden section strategy to reduce the area of the fixed-outline and to achieve the dual effect of both area and wirelength optimization. Compared with floorplanning algorithm based on B-tree on GSRC test circuit, the proposed algorithm had advantages in terms of wirelength and time for large-scale circuits. Experimental results show that the algorithm can obtain better wirelength with lower time complexity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advancement of Internet technology and social network, a multitude of real-world issues can be modeled as combinatorial optimization problems on networks, attracting widespread attention. In the optimization process, agents often engage in strategic behavior driven by personal interests to maximize their utilities. This "selfish" behavior can, on one hand, affect other participants, while on the other hand, the strategies of all agents directly determine the achievement of societal objectives. Therefore, cooperation and competition coexist among participants, giving rise to combinatorial optimization games. This paper aims to delve into three challenging combinatorial optimization games on networks: public goods games, vertex cover games, and routing games. These three categories of games not only hold significant positions in the fields of combinatorial optimization and theoretical computer science, but also have extensive applications across multiple interdisciplinary areas including management science and engineering, economics, and more. To this end, we will provide a systematic introduction to these three types of combinatorial optimization games and thoroughly review their recent research progress and breakthroughs. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the development of graph neural network (GNN) and deep reinforcement learning provides new methods to solve combinatorial optimization problems. Currently, most of these methods do not consider the problem of algorithm parameter learning. This paper developed an intelligent optimization model based on graph attention networks to solve this problem. The model automatically learned neighborhood search operators and destructive sequence terminators according to a significant amount of problem data, and trained model parameters based on reinforcement learning. This article used standard examples to test the model and conducted three different groups of experiments. The experimental results show that the learned neighborhood search operator has a remarkable ability to optimize and converge, while significantly reducing the training memory occupation. It can solve CVRP problems containing hundreds of nodes in a short time and has the potential for expansion. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm is a new swarm intelligence optimization algorithm inspired by humpback whale hunting behavior, and its performance is far better than the traditional algorithms. In order to track the latest research results and promote the research in the field of swarm intelligence optimization algorithm, this paper firstly introduced the basic principle of whale optimization algorithm, briefly described its implementation steps, and compared and analyzed its pe1formance characteristics. In view of its shortcomings of slow convergence speed and low convergence accuracy, this paper summarized and analyzed the improved methods including other intelligent optimization algorithms, Levy flight strategy, chaos strategy, as well as their advantages and limitations. Then, it described its applications and effect in combinatorial optimization, image segmentation, data prediction, and so on. Finally, this paper summarized and prospected the future development trend. [ABSTRACT FROM AUTHOR]

Copyright of Architectural Journal / Jian Zhu Xue Bao is the property of Architectural Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Message propagation algorithms from statistical physics are widely used in various fields of artificial intelligence, especially in solving combinatorial optimization problems. According to the related literatures of message propagation algorithm, this paper summarized the history of message propagation algorithm and its related application. According to the development of message propagation algorithm, it introduced the concepts of information propagation algorithm for solving the satisfiability problem, which mainly involved the warning propagation algorithm, the belief propagation algorithm and the survey propagation algorithm. This paper described the convergence and effectiveness research of the three kind of algorithms, summarized the application of each algorithm in related fields, and summarized the research path and application direction of message propagation algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The minimum cost and large flow problem is a combinatorial optimization problem, which has important research significance and application value in the fields of economy and industry. In view of the low efficiency of the algorithm for solving part of the minimum cost and maximum flow problem,according to the linear programming equation of the minimum cost and maximum flow problem, this paper mapped the problem model to the corresponding factor graph model, improved the description function, gave the iteration equation, and designed the minimum cost belief propagation algorithm for maximum flow problem. It used the iterative equation to perform convergence calculation on the maximum feasible flow eigenvalue firstly, and obtained the maximum flow, and set the maximum flow threshold. On this basis, the minimum cost calculation was performed to obtain the optimal solution of the problem. Finally, this paper selected a number of weighted directed graph models for numerical experiments to verify the feasibility and effectiveness of the algorithm. The results show that the algorithm is superior to some algorithms in terms of solving efficiency. [ABSTRACT FROM AUTHOR]

The rectangular layout problem with balance constraints is a combinatorial optimization problem, which is difficult to solve when the problem scale increases. To improve the performance, this paper designed a bee colony algorithm. By analyzing the distribution of solutions, it provided a greedy initialization scheme for the population, selected an effective mutation operator, and limited the search space around the optimal solution. In addition, it designed a local search module to further improve the quality of the solution. On 10 open test cases, the algorithm was compared with the best known existing algorithms, and the performance of the proposed algorithm exceeded the existing algorithms on 9 large-scale cases. The theoretical analysis and experimental results show that the proposed algorithm can significantly improve the efficiency of solution. [ABSTRACT FROM AUTHOR]

This paper proposed the mathematical model which based on the open location routing problem with fuzzy demands considering with the sum of location cost,routing cost,lost opportunity and additional distances.For solving the mathematical model,this paper proposed a new swarm intelligence algorithm named hybrid discrete mushroom algorithm.The algorithm used partly mapping crossover and path relinking algorithm to replaced artificial wind of original mushroom reproduction algorithm(MRA),and used the method of selecting different neighborhoods with a certain probability in the local search.It used stochastic simulation program to simulate the designed routes to compute additional distances and lost opportunity resulting from serving failure.The algorithm was successfully applied to the combinatorial optimization problems.Finally,this paper verifies the validity of the model and the performance of hybrid discrete mushroom reproduction algorithm through a series of tested instances and comparisons. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The node-weighted Steiner tree problem is a classic NP-hard problem in the combinatorial optimization. The existing algorithms for studying the problem have the disadvantages of high time complexity and unavailability of the optimal solution. In view of the shortcomings of existing algorithms, this paper presented a backtracking algorithm based on reduced-order technique. Firstly, this paper studied the mathematical properties of the node-weighted Steiner tree problem and utilized its mathematical properties to reduce the scale of the problem. Then, it designed an upper bound sub-algorithm and a lower bound subalgorithm and utilized such sub-algorithms to prune the solution space tree of the problem and improved the search efficiency. Finally, it designed a backtracking algorithm to solve the above-mentioned problem by utilizing the upper and lower bound subalgorithms and mathematical properties. Example analysis and experimental results demonstrate that the algorithm is not only with lower time complexity, but also can obtain the optimal solution of the problem. [ABSTRACT FROM AUTHOR]

The three-dimensional path planning problem is a combinatorial optimization problem to find the optimal path be- tween the starting point and the destination in the interference environment. Aiming at the shortcomings of traditional swarm in- telligence algorithm in solving this problem,such as low convergence accuracy and easy to fall into local optimum, this paper proposed an adaptive moth-flame optimization algorithm to solve the problem. By introducing the dynamic adjustment strategy of the flight direction and the position intersection strategy,the improved algorithm continuously generated new individuals while dynamically adjusting the flight direction of the moth, which effectively avoided the algorithm from falling into local optimum ; by adaptively adjusting the number of flames, so as to the algorithm enhanced population diversity in the global exploration stage a- void premature convergence. It used the adaptive moth-flame optimization algorithm and other swarm intelligence algorithms to solve the three-dimensional path planning problem. The experimental results show that the improved adaptive moth-flame opti- mization algorithm has the smallest cost value and the fastest convergence speed among all algorithms. The algorithm has better solution ability in 3D path planning problem. [ABSTRACT FROM AUTHOR]

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The constrained competitive location problem is a classic NP-hard problem in combinatorial optimization. When the existing algorithms study this problem, the optimal solution cannot be obtained or the solution speed is slow. Aiming at the shortcomings of the existing algorithms, this paper modified this classic problem and constructed a new mathematical model. Then it studied the mathematical properties of the model, and proposed upper and lower bound algorithms and lower bound algorithms on the basis of mathematical properties. The order sub-algorithm reduced the order of the problem and achieved the purpose of reducing the search solution space of the problem. There were both single order reduction and batch reduction in the order reduction process. Then it designed a backtracking sub-algorithm on the basis of the previous one to solve the optimal solution of the problem. Finally, the principle of the algorithm was explained more clearly through the analysis of two examples, and the result proves that the algorithm can find the optimal solution faster. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposed an adaptive-evolution-based genetic algorithm to solve the problems in conventional genetic algorithm, such as slow convergence speed and easy to fall into local optimal solution. Firstly, in order to choose the optimal genetic operator, this paper designed an adaptive adjust formula for the genetic operator according to the current evolution state to improve the convergence speed and the global searching capability of the algorithm. Secondly, in order to further improve the convergence speed of the proposed adaptive-evolution-based genetic algorithm, it designed a rapid evolution mechanism and added it into the algorithm. Experimental results show that the proposed algorithm can achieve better synthesized performances in convergence speed, searching global optimal solution and execution speed. [ABSTRACT FROM AUTHOR]

This paper took the electric vehicle routing problem (EVRP) with constraints of time,load and battery capacity as the research object, it considered its recharging need in transit, constructed a mathematic model aiming at minimizing the total route length, and proposed an algorithm RL-EVRP based on reinforcement learning. The algorithm generated instances sampled from a given distribution, and trained a model by applying a policy gradient method while keeping the route feasible. The trained model could solve other instances from similar distribution without the need to re-train. Simulation results show that the RL-EVRP can get shorter total route length and less number of vehicles and that the reinforcement learning can be applied to complicated combinatorial optimization problem successfully. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at improving the effect of network intrusion detection, this paper introduced an intrusion detection algorithm based on FAST feature selection algorithm, adaptive binary quantum-inspired gravitational search algorithm and support vector machine(FAST-ABQGSA-SVM). It used FAST algorithm to remove some irrelevant features to form the candidate feature subset. It used BQGSA to solve the problem of combinatorial optimization to obtain the optimal feature subset and the classifier parameters. During the process of repeated learning optimization, using a dynamic adaptive adjustment strategy to update the quantum rotation angle,it could balance the global search ability and local search ability. Designing the adaptive mutation probability based on population evolutionary degree and individual fitness value,it improved the adaptive mutation ability. Using Q-bit crossover operation when the population evolutionary stagnation occurred, it could help the population get rid of local extremum. Simulation results by KDD CUP 99 show that the proposed FAST-ABQGSA-SVM algorithm has better robustness, learning accuracy and detection performance compared with other similar detection algorithms. [ABSTRACT FROM AUTHOR]

Minimum vertex cover problem is a combinatorial optimization problem, it has a wide application in the real world, and it is a NP-hard problem. This paper presented a hybrid chemical reaction optimization algorithm for the minimum vertex cover problem. First of all. according to the undirected graph adjacency matrix, it designed chemical reactions molecular coding and the objective function. At the same time, it blended the greedy algorithm thought creatively into the four important operator of chemical reaction optimization algorithm to speed up the search process of local optimal solution. And searched the optimal solution in the solution space by simulating the process of chemical reaction in which potential energy gradually stabilize. The experimental result proves that the algorithm is effective for solving minimum vertex cover problem of undirected graph, and it performs remarkably better in solving efficiency. [ABSTRACT FROM AUTHOR]

Vehicle routing problem (VRP) is a kind of typical combination optimization problems. Most of the researches focus on VRP with fixed transportation capacity; however, the capacity of vehicles is varied with the cargo shape and customer requirement. Therefore, we propose a varied capacitated vehicle routing problem (VCVRP) to deal with the practical problems. In this paper, a fast dynamic programming algorithm is proposed for large-scale VCVRP. And it is based on the traditional best fit decreasing (BFD) algorithm and minimum spanning tree (MST) algorithm. A K-backtracking strategy and a principle of short-priority are introduced to the dynamic programming algorithm. In this fast algorithm, the bin packing problem and routing problem are decoupled approximately. Analysis of the upper bound of objective total distance is given theoretically. Finally, experimental results of practical logistics of passenger vehicles are discussed to illustrate the quality and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studied minimum ratio traveling salesman problem (MRTSP), which objective function was the ratio between the total distances and total benefits, and presented a discrete bat algorithm for solving MRTSP. In this improved bat algorithm, on the one hand, it redefined the subtraction operator of location and location, the multiplication operator of real number and location, and the addition operator of velocity and location. On the other hand, it introduced the reversing the city subsequence in order to perform the route's local search. The experimental results show that this improved bat algorithm is feasible and can effectively solve the MRTSP instances. [ABSTRACT FROM AUTHOR]

As the core problem for cloud computing, especially the appropriate distribution of resources in heterogeneous environment, job scheduling algorithm has turned to be one of the current research focuses. This paper introduced swarm intelligence algorithm on the basis of the analysis on the default and improved Hadoop job scheduling algorithm, and proposed a new Hadoop job scheduling algorithm based on the improved artificial fish swarm algorithm. It encoded the allocated tasks by random key approach first, with the total execution time of the tasks as heuristic information, and then improved AFSA by introducing devouring and jumping behavior, last applies improved AFSA to Hadoop job scheduling algorithm in order to achieve further performance. The experimental results show that the improved AFSA in heterogeneous environments can improve system performance and reduce the computing time. [ABSTRACT FROM AUTHOR]

This paper studied the problem that standard teaching-learning-based optimization algorithm (TLBO) easily converges to local optima when solving combinatorial optimization, and proposed a simulated annealing TLBO(SATLBO) algorithm. In the method, it used the simulated annealing algorithm. Randomly selected a bit of the bad individuals according to a calculated possibility of simulated annealing algorithm to the new population in the teacher phase and learner phase. It increased the ability of running away from local optima of TLBO by increasing the diversity of the population. It simulated the unmultimodal functions, multimodal functions and rotation functions, and compared the results with some other evolutionary computation algorithms. The results indicate that the improved algorithm has good performance in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper studies the multi-criteria decision-making problem where the criteria values of the alternatives are interval-valued intuitionistic fuzzy numbers and information of the criteria weights is incomplete or unknown. We provide a decision-making approach which is based on prospect theory. We define a new score function (P-score function) and consequently, the interval-valued intuitionistic fuzzy numbers can be transformed into the corresponding real numbers via this P-score function. The prospect decision-making matrix is constructed by calculating the prospect value of each alternative, which uses the zero as the reference point. In addition, we establish the combined weight optimization model in which the goal is based on maximizing the integrated prospect value and the subject conditions include the criteria weights' value being interval and decision-maker's subjective preference. According to this optimization model, we can calculate the coefficients of the criteria weights. Combining the prospect decision-making matrix with the criteria weights, we can order these alternatives by calculating the integrated prospect values. Finally, an example is illustrated to examine the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Aerodynamic configuration optimization study for a hypersonic vehicle is performed in the paper. In the optimization progress, a parameterized model is used and auto grids are achieved efficiently by solving parabolic equations. The aerodynamic force and heat are predicted by the technique which combined effective space marching numerical method and engineering method based on tracking the surface stream trace respectively. The aerodynamic characteristic of longitudinal static stability of the optimized shape is acquired through design optimization for high trimming angle of attack, and the maximum surface heat flux decreased 24% compared to the original shape. The technique promoted is an effective approach suitable for aerodynamic configuration preliminary designs of high speed vehicles. [ABSTRACT FROM AUTHOR]

In order to enhance the operational efficiency of postal transportation network, a model of multi-center vehicle routing problem with parcel pick-up and delivery has been built for the China Post, in which some constrains, such as round-trip collection, carrying capacity, the shortest path and the least vehicle numbers, are also considered in the presented model. To solve this model, the mixed algorithm with taboo search and saving algorithm is presented. Finally, the simulated analysis of the model and algorithm is performed with the production data of Guizhou Post; meanwhile, the optimal results from the model are analyzed. The results show that the distribution strategy of this paper is more efficient, the proposed model and algorithm are valid and practical. [ABSTRACT FROM AUTHOR]

Time series events clustering is the basis of studying the classification of events and mining analysis. Most of the existing clustering methods directly aim at continuous events with time attribute and complex structure, but the transformation of clustering objects is not considered, hence the accuracy of clustering is extremely low, and the efficiency is limited. In response to these problems, a time series events oriented dynamic matrix clustering method RDMC is proposed. Firstly, the r-nearest neighbor evaluation system is established to measure the representativeness of the event according to the evaluation value, and the candidate set of RDS (representative and diversifying sequences) is constructed by the backward difference calculation strategy of the nearest neighbor score. Secondly, a method of RDS selection based on combinatorial optimization is proposed to obtain the optimal solution of RDS from the candidate set quickly. Finally, on the basis of dynamically constructing the distance matrix between RDS and the data set, a matrix clustering method based on K-means is proposed to realize the effective division of time series events. Experimental results show that compared with the existing methods, the method proposed in this paper has obvious advantages in clustering accuracy, clustering reliability, and clustering efficiency.

Traditional neural network always adopts gradient descent method as search method, which is easily fall into local optimum. Quantum genetic algorithm was introduced into neural network, and an improved quantum genetic neural network based speech watermark algorithm was proposed in this paper. Firstly, identify a rough solution by using the improved quantum genetic algorithm to optimize the initial coefficient of BP neural network. Then search out the optimal coefficient of BP neural network with the use of gradient descent method, thus raise the accuracy of network convergence. Theoretical analysis and experimental results show, compared with traditional BP network and GA based network, our method can achieve smaller error and bigger watermark capacity. [ABSTRACT FROM AUTHOR]

Aiming at the problem of deformation of stroke thickness and serif when composing Chinese characters by components, this paper proposed a method of generating the target glyph by directly using an optimized scaling transformation. By vising transformation sequence generating algorithm and stroke operation algorithm, this method achieved direct transformation from original components to target components. Experimental results of prototype system demonstrate that the method can effectively achieve Chinese characters' distortionless resizing, improve the quality of glyphs generated by Chinese character composing system, and thus enhance the efficiency of automatic generation of Chinese fonts. [ABSTRACT FROM AUTHOR]

Recently, the hybrid wireless-optical broadband network integrating optical backbone networks, passive optical networks, and wireless access networks have been proposed to provide the high-bandwidth, low-cost, and ubiquitous communication connections. This paper considered the design of network coding-based multicast applications in such networks with the objective of maximizing the total network utility and minimizing the deployment cost. The problem was formulated as a mixed integer nonlinear programming problem and the exact solution is prohibitively complex. In order to make the problem more tractable, it developed a two-step optimization procedure that iteratively selected the optical network unit and gateways for the multicast sessions. During each iteration, it solved two subproblems a network coding design problem for the optical network and a user assignment and bandwidth allocation problem for the wireless network. The former was solved in a distributed way based on the Lagrangian-dual decomposition; the latter was solved based on the generalized bender decomposition. Simulation results illustrate the effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

In order to improve multicast state scalability problem, this paper proposed an advanced aggregated multicast algorithm. The algorithm was on the basis of generic dynamic on-line algorithm. When matching group-tree, the algorithm determined the feasible range of aggregated trees to reduce matching and calculation times and to get lower time complexity. In order to optimize aggregated tree structure, remove unnecessary intermediate node, increase aggregated degree and reduce node forwarding state, the algorithm improved the optimal matching aggregated tree selection algorithm and regularly adjusted aggregation tree in the network. Simulation results show that, when compared to similar algorithm, the algorithm can get higher aggregated degree and state reduction ratio in lower time complexity, and the improvement is much more obviously in the higher number of concurrent multicast group and group density. [ABSTRACT FROM AUTHOR]

The traditional clustering analysis method has weaknesses for analysis of pattern clustering statistical data trajectories, such as too much emphasis on changing trends, larger data noise, different distribution patterns and so on. Therefore, this paper proposed a method that based on K-means to optimize the functions used in clustering analysis. Before modeling, it eliminated the error of data sample and gave the experimental data normalized treatment, smoothing processing and critical peak matching method. Artificial and real data processing results show that the proposed method performs better than classic clustering method. [ABSTRACT FROM AUTHOR]

Considering carbon emissions, this paper proposed a problem of logistics network optimization. It established the distribution center selection and demand matching model considering carbon emissions and the distribution routing optimization model on minimum carbon emissions respectively in two stages, and used the software Lingo 9.0 to solve the models. Through using the model of the second stage in conjunction with the model of the first stage, some decision problems about the distribution center location, it solved the demand matching between different logistics nodes as well as the vehicle routing optimization. Finally, an actual example verified the proposed models, and compared the optimization results when considering and not considering carbon emissions. The results show the proposed method of minimizing comprehensive costs of entire logistics process considering carbon emissions is more practical value than the conventional method of not considering carbon emissions. [ABSTRACT FROM AUTHOR]

In order to balance the abilities of global detective and local search, this paper proposed a modified central force optimization (MCFO) algorithm based on crossover and mutation for solving constrained optimization problems. The proposed algorithm constructed the initial population by using the principles of the good point set method was to strengthen the diversity of particles. The optimal particle and the randomly selected particle are carried out arithmetic crossover operator which led gradually the population to the global optimum. It utilized diversity mutation operator to avoid the premature convergence, and tested two well-known benchmark functions and two engineering optimization application problems. The results demonstrate that the MCFO algorithm is an effective method for differential constrained optimization problems. [ABSTRACT FROM AUTHOR]

Aiming to resolve the problems of the traditional K-means clustering algorithm such as random selecting of initial clustering centers, lacking the ability of handling boundary objects of data, the low efficiency, and low clustering accuracy, this paper proposed a new K-means clustering algorithm. This algorithm firstly introduced in the granular computing theory and selected the initial centers based on the density and max-min distance means, so that the initial clustering center could be in the different class. Then the algorithm combined with rough set and adjusted dynamically the boundary objects by changing the weighting factors of upper approximation set and boundary set, to settle the clustering problem of boundary data. Finally, it used criterion function of equalization based on the within-class distance/the distance between classes as the judgment condition of algorithm termination, to get better clustering effect. The results of experiments show that this algorithm has higher accuracy, less iteration times, and reduces the sensitive degree of the noise. [ABSTRACT FROM AUTHOR]