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The Game Theory-based Multi-Agent System (GTMAS) of Salhi and Töreyen, and implements a loosely coupled hybrid algorithm that may involve any number of algorithms suitable, a priori, for the solution of a given optimisation problem. The system allows the available algorithms to cooperate toward the solution of the problem in hand as well as compete for the computing facilities they require to run...
Most real-world negotiation involves multiple interdependent issues, which makes an agent's utility functions nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; they can produce excessively high failure rates, when there are...
In this paper, a segmentation technique of multi-spectral magnetic resonance image of the brain using a new differential evolution based crisp clustering is proposed. Real-coded encoding of the cluster centres is used for this purpose. Here assignments of points to different clusters are made based on the Euclidean distance. The proposed method is applied on several simulated T1-weighted, T2-weighted...
Firstly, the paper makes a briefly analysis and comment about the fuzzy c-means clustering algorithm. Then a new kind of hybrid genetic algorithm is proposed on the base of the combination of genetic algorithm and simulated annealing algorithm, and it is applied in fuzzy c-means clustering. It overcomes the locality and the Sensitivity to initial clustering central of fuzzy c-means clustering, by...
This article deals with the development of an improved clustering technique for categorical data that is based on the identification of points having significant membership to multiple classes. Cluster assignments of such points are difficult, and they often affect the actual partitioning of the data. As a consequence, it may be more effective if the points that are associated with maximum confusion...
The K-means algorithm is one of the widely used clustering algorithms in the image classification systems. However, the K-Means algorithm is easily trapped into the local optimal solutions. Several optimization techniques have been proposed to solve this problem such as genetic algorithms, simulated annealing and swarm intelligence. In this paper, we develop hybrid techniques using different particle...
Categorical data clustering has been gaining significant attention from researchers, because most of the real life data sets are categorical in nature. In contrast to numerical domain, no natural ordering can be found among the elements of a categorical domain. Hence no inherent distance measure, like the Euclidean distance, would work to compute the distance between two categorical objects. In this...
Optimizing the system performance metric directly is an important method for correcting wave-front distortions in adaptive optics(AO) systems. Appropriate stochastic parallel optimization control algorithm is the key to correcting distorted wave front successfully. Based on several stochastic parallel optimization control algorithms, an adaptive optics system with a 32-element deformable mirror was...
Clustering is a relevant problem that takes place in many practical environments. This paper presents some meta-heuristic approaches as an alternative to the traditional clustering techniques, like K-means or C-means. They are based on some metaheuristic optimization algorithms as tabu search, simulated annealing, genetic algorithms and ant colony. The developed techniques have the advantage that...
Web site clustering consists in finding meaningful groups of related Web sites. How related is some Web site to another is a question that depends on how we represent Web sites. Traditionally, vectors and graphs have been two important structures to represent individuals in a population. Both representations can play an important role in the Web area if hyper structure is considered. By analyzing...
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