Mining functional modules in Protein-Protein Interaction (PPI) networks is a very important research for revealing the structure-functionality relationships in biological processes. More recently, some swarm intelligence algorithms have been successfully applied in the field. This paper presents a new nature-inspired approach, ACC-FMD, which is based on ant colony clustering to detect functional modules. First, some proteins with the higher clustering coefficients are, respectively, selected as ant seed nodes. And then, the picking and dropping operations based on ant probabilistic models are developed and employed to assign proteins into the corresponding clusters represented by seeds. Finally, the best clustering result in each generation is used to perform the information transmission by updating the similarly function. Experimental results on some benchmarked datasets show that ACC-FMD outperforms the CFinder and MCODE algorithms and has comparative performance with the MINE, COACH, DPClus and Core algorithms in terms of the general evaluation metrics.

In relational databases and their applications, there are opportunities for evaluating a stream of K NN queries submitted one by one at different times. For this issue, we propose a new method with learning-based techniques, region clustering methods and caching mechanisms. This method uses a knowledge base to store related information of some past K NN queries, groups the search regions of the past queries into larger regions, and retrieves the tuples from the larger regions. To answer a newly submitted query, our strategy tries to obtain a majority or all of the results from the previously retrieved tuples cached in main memory. Thus, this method seeks to minimize the response time by reducing the search region or avoiding the accesses to the underlying database. Meanwhile, our method remains effective for high-dimensional data. Extensive experiments are carried out to measure the performance of this new strategy and the results indicate that it is significantly better than the state-of-the-art naïve methods of evaluating a stream of K NN queries for both low-dimensional (2, 3 and 4) and high-dimensional (25, 50 and 104) data.

In this paper, for a given service quality gap problems of emergency WeChat platform, we discuss the general method for determining the trait of emergency WeChat platform, as well as quality framework, evaluation system of emergency WeChat service. For the determination of the trait of emergency WeChat platform, using SPSS mathematical software to draw an initial shape, through the mathematical method of mean value and balanced scorecard, we build a model, get the coordinate of services quality evaluation initial indexes system of emergency WeChat platform; For quality framework and evaluation system of emergency WeChat service, we define the five dimensionalities items, and optimize the original evaluation system indicators. For the empirical study, we selected six representative platform as the evaluation sample from the emergency platform of Wechat in China, send 100 questionnaires and designed 14 items in questionnaire which corresponding with the evaluation index system. Using the method of multidimensional scaling analysis, we disclose the properties of the multiple samples and the degree of similarity between them. Using the method of grey correlation analysis, we find the degree of quality gap and perform quantitative ordering about the service quality of emergency Wechat platform.

The phone 3D animation automatically generating system is to achieve the goal that the user sent the information to the server. After a serial process of information extracting, plot planning and scenario planning, the system would finally generated and sent a video animation which is related to the information contents to the receiver. Here, animation scenes planning module plans the various details of the plot basing on the plot qualitative and quantified into a three-dimensional animation scene file. Basing on animated plot planning, this paper mainly studies the spatial layout of the three-dimensional scene, make a layout of the three-dimensional available scene space according to semantic information of the object, design and achieve a three-dimensional scene layout repository, and ultimately achieve a reasonable placement of three dimensional objects. Experiment results show that this system could not only ensure unobstructed placed objects but also allows the same object to be added more, which makes the objects more diversity and reflects the semantic information of an object.

A protein-protein interaction (PPI) network is a biomolecule relationship network that plays an important role in biological activities. Studies of functional modules in a PPI network contribute greatly to the understanding of biological mechanism. With the development of life science and computing science, a great amount of PPI data has been acquired by various experimental and computational approaches, which presents a significant challenge of detecting functional modules in a PPI network. To address this challenge, many functional module detecting methods have been developed. In this survey, we first analyze the existing problems in detecting functional modules and discuss the countermeasures in the data preprocess and postprocess. Second, we introduce some special metrics for distance or graph developed in clustering process of proteins. Third, we give a classification system of functional module detecting methods and describe some existing detection methods in each category. Fourth, we list databases in common use and conduct performance comparisons of several typical algorithms by popular measurements. Finally, we present the prospects and references for researchers engaged in analyzing PPI networks.

To solve the poor performances of evolution speed,solution precision and distribution in the multi-objective estimation of distribution algorithm,this paper proposes a new algorithm that based on chaos optimization and grid selection strategies. The algorithm first performs initialization using chaos models to obtain better initial results. Then,a chaotic local optimization strategy is applied to get nondominating individuals in iterations,which makes the population effectively approximate the Pareto optimal front. Finally,a simple grid selection strategy is employed to keep a uniform distribution and enhance the diversity of the elite population. Experimental results on eight test problems using three performance metrics show that the new algorithm has a certain advantage compared to the most representative RM-MEDA algorithm in terms of converging to the true Pareto front and maintaining the diversity of the population,moreover,it is also much faster than RM-MEDA.

To aim at the travelling salesman problem with time windows(TSPTW),an ant colony optimization algorithm with Mutation Features based on Magnetic Field(MFM-ACOMF) was put forward.It improved the heuristic function in the traditional ant colony optimization(ACO) algorithm,to meet the time requirement of customers and reduce the probability of getting a local optimal.Moreover,when it obtained the preliminary solution after all the iterations,a mutation strategy was used to optimize the customer nodes that did not reach the time window limit.The simulation results show that the MFMACOMF algorithm has certain improvement on both the optimal solution quality and customer satisfaction,compared with the ACO algorithm.

The whole life-cycle automatic animation technique is that making the story written in some limited natural language to be a 3D animation cartoon automatically. We use this technique in automatic generation of mobile phones animation system. As an important part of automatic generation of mobile phones animation system, the deforming system can make the animation more vivid by deforming the objects in the animation. It was made up of qualitative layer and quantitative layer, which we will introduce in this article.

Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.

When solving the multi-task coalition problem(MTCP),the ant colony optimization(ACO) algorithm showed deficiencies such as too many iterations and low solution accuracy.For problems above,the ACO algorithm based on relative distance and association frequency was proposed,which adopted two strategies in view of search mechanism and pheromone increment model.First,in order to improve the utilization of resources,the concept of relative distance was introduced,based on which,a more effective search mechanism was proposed.Then,to strengthen the collaborations among ants and make full use of answer information obtained,a pheromone increment model based on association frequency was established.Experiment shows that the proposed algorithm can not only get much more optimal solutions but also greatly enhance convergence speed compared with related algorithms.

Given a set of n objects, the objective of the 0-1 multidimensional knapsack problem (MKP_01) is to find a subset of the object set that maximizes the total profit of the objects in the subset while satisfying m knapsack constraints. In this paper, we have proposed a new artificial bee colony (ABC) algorithm for the MKP_01. The new ABC algorithm introduces a novel communication mechanism among bees, which bases on the updating and diffusion of inductive pheromone produced by bees. In a number of experiments and comparisons, our approach obtains better quality solutions in shorter time than the ABC algorithm without the mechanism. We have also compared the solution performance of our approach against some stochastic approaches recently reported in the literature. Computational results demonstrate the superiority of the new ABC approach over all the other approaches.

Mining functional modules in a protein-protein interaction (PPI) network contributes greatly to the understanding of biological mechanism, thus how to effectively detect functional modules in a PPI network has a significant application. In this paper, we present a hybrid approach using ant colony optimization and multi-agent evolution for detection functional modules in PPI networks. The proposed algorithm enhances the performance of ant colony optimization by incorporating multi-agent evolution for detecting functional modules. In the ant colony optimization process, a new heuristic, which merges topological characteristics with functional information function, is introduced to effectively conduct ants searching in finding optimal results. Thereafter, the multi-agent evolutionary process based on an energy function is performed to move out of local optima and obtain some enclosed connecting subgraphs which represent functional modules mined in a PPI network. Finally, systematic experiments have been conducted on four benchmark testing sets of yeast networks. Experimental results show that the hybrid approach is more effective compared to several other existing algorithms. (C) 2013 Elsevier B.V. All rights reserved.

One basic approach to learn Bayesian networks (BNs) from data is to apply a search procedure to explore the set of candidate networks for the database in light of a scoring metric, where the most popular stochastic methods are based on some meta-heuristic mechanisms, such as Genetic Algorithm, Evolutionary Programming and Ant Colony Optimization. In this paper, we have developed a new algorithm for learning BNs which employs a recently introduced meta-heuristic: artificial bee colony (ABC). All the phases necessary to tackle our learning problem using this meta-heuristic are described, and some experimental results to compare the performance of our ABC-based algorithm with other algorithms are given in the paper.

Ant Colony Optimization(ACO) had not a well efficiency and ignored the traffic jam which was a serious problem in the real traffic,so it was hard to obtain a good solution.Therefore,a highly efficient ACO was proposed.First,the destination emitted fragrance information which could attract ants close to the destination and make the search of ants have directivity;Second,the route in road networks was classified,and dynamic classification strategy was combined to avoid the shortcoming such as stagnation.Result shows that this algorithm is better than ACO algorithm in the stability and quality of optimum solution.