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In this paper we consider the problem of coordinating the motion of a group of Automated Guided Vehicles (AGVs) utilized in industrial environments for logistics operations. In particular, we consider a hierarchical coordination strategy, where the environment is partitioned into sectors: coordination on the top layer defines the sequence of sectors to be traveled, while coordination on the bottom...
This paper proposes an algorithm to perform an online fuzzy system construction was called FsXCS (Fuzzy Strength-Based XCS). FsXCS is the extended XCS system combining XCS with fuzzy logic theory to tackle the multi-step continuous input-output problems. Indeed, XCS is a great attention discrete-valued system considering from the rule generalization system. However, it becomes more difficult when...
With increasing data clouds in different geographical areas, the availability of a datacenter and the cost of using the datacenter are two concerned factors of clouds users. The present research aims to present a method using K-means clustering and NSGA-II multi-objective algorithm to maximize availability and minimizes cost in selecting a datacenter. The proposed approach was applied to some real...
An important application field of swarm intelligence algorithms is fuzzy rule acquisition. However, their limitations are showed in two aspects. On one hand, it takes a long process to create fuzzy rules during the iterations; on the other, the swarm intelligence algorithms obtain local optimal solution at times. To overcome these disadvantages, a dynamic hybrid swarm intelligence approach is proposed...
An island partitioning method based on cloud adaptive genetic algorithm is proposed. The traditional genetic algorithm is modified by making use of the trait of cloud theory, which shows both randomness and a tendency of stability. The traditional crossover operator is replaced by cloud crossover operator, aiming at improving global searching ability and avoiding falling into local minimums. The concept...
This paper presents a novel discrete-time decentralized control law for the Voronoi-based self-deployment of a Multi-Agent dynamical system. The basic control objective is to let the agents deploy into a bounded convex polyhedral region and maximize the coverage quality by computing locally the control action for each agent. The Voronoi tessellation algorithm is employed to partition dynamically the...
Initialization is an extremely important part of the mixture estimation process. There exists a series of initialization approaches in the literature concerning the mixture initialization. However, the majority of them is directed at initialization of the expectation-maximization algorithm widely used in this area. This paper focuses on the initialization of the mixture estimation with normal components...
Horizontal and vertical partitioning can increase the performance of the database and simplify data management. However traditional partitioning techniques can't deal with stream queries efficiently. In this paper we present WSPS: a workload-driven stream partitioning system to solve the above problembytheintegrationofpartitioningtechnologyandstreaming framework. We construct a dynamic data model,...
Finding communities or clusters in social networks is a famous topic in social network analysis. Most algorithms are limited to static snapshots so they cannot handle dynamics within the underlying graph. In this paper, we present a modification of the Lou-vain community detection method to handle changes in the graph without rerunning the full algorithm. Also, we adapted the Louvain greedy approach...
This paper proposes two parallel hybrid heuristics aiming for the reduction of the average bandwidth of sparse matrices, process used in systems of equations preconditioning. Based on a direct processing of the matrix, the first method combines a heuristic inspired from the laws of physics, with a greedy selection of rows/columns to be interchanged. The second one improves the previous heuristic through...
Introducing methods that can work out the problem of noisy image segmentation is necessary for real-world vision problems. This paper proposes a new computational algorithm for segmentation of gray images contaminated with impulse noise. We have used Fuzzy C-Means (FCM) in fusion with Particle Swarm Optimization (PSO) to define a new similarity metric based on combining different intensity-based neighborhood...
Dynamic community detection has been of great significance on analyzing network structure and community evolution. Among state-of-the-art methods, incremental algorithms based on modularity have been used widely, for the fully utilization of both current and historical information. Unfortunately, they are difficult to uncover small community due to problem called “resolution limit” and also sensitive...
Community and cluster detection is a popular field of social network analysis. Most algorithms focus on static graphs or series of snapshots. In this paper we present an hierarchical algorithm, which detects communities in dynamic graphs. The method is based on the shortest paths to high-connected nodes, so called hubs. Due to local message passing, we can update the clustering results with low computational...
Influence maximization specifies a set of nodes that maximizes the influences in social networks. The influence maximization problem due to its importance in targeted marketing has been explored by many researchers. All proposed algorithms are not scalable and are too time consuming for large-scale social network. In this paper, an efficient and fast algorithm called ComPath+ is proposed for influence...
Tag collision is a very important issue in RFID system. In order to improve the identification efficiency of the system, this paper proposes a collision avoidance anti-collision algorithm based on subset partition (CABSP). The CABSP narrows the identification range and reduces the probability of tag collision by splitting tag subsets. On this basis, the process of tag identification is divided into...
Data clustering is one of the most popular branches in machine learning and data analysis. Partitioning-based type of clustering algorithms, such as K-means, is prone to the problem of producing a set of clusters that is far from perfect due to its probabilistic nature. The clustering process starts with some random partitions at the beginning, and it tries to improve the partitions progressively...
The skyline query is a most useful tool to find out attractive products. However, it does little to help select the product combinations with the maximum discount rate. Motivated by this, we identify an interesting problem, a most preferential skyline product (MPSP) combination discovering problem, which is NP-hard, for the first time in the literature. This problem aims to report all skyline product...
In the dynamic complex networks, the community structure is constantly changing, such as generation, maintaining, merger, die, fusion, etc. These changes will lead to the dynamic evolution of the structure and morphology in an entire community. Current static community division methods can not be well used to analyze the dynamic network evolution. In this paper, based on events in the community evolution,...
Clustering is a fundamental and important technique under many circumstances including data mining, pattern recognition, image processing and other industrial applications. During the past decades, many clustering algorithms have been developed, such as DBSCAN, AP and CFS. As the latest clustering algorithm proposed in Science magazine in 2014, clustering by fast search and find of density peaks,...
Data mining is one of the significant research domains in the field of computer science and it is defined as the extraction of hidden knowledge from the large data repositories. Important data mining techniques are classification, clustering, association rule generation, summarization, time series analysis and etc. Association rule is used to determine frequent patterns, association and correlations...
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