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Data clustering algorithms play an important role in effective analysis and organization of massive amounts of information. The K-means algorithm is the most commonly used partitional data clustering algorithm because of its simplicity in implementation and its high convergence rate. However, it suffers from the inability to always converge to the global optima, depending on how the data items are...
In this paper, we have presented a new method of the cultural-based particle swarm optimization for dynamical environment which uses belief space representation to accommodate more types of knowledge such as history knowledge, and topographical knowledge. Also, additional influence functions have been developed to utilize these types of knowledge. This study focuses on the knowledge needed to track...
In this paper, the particle swarm optimizer is modified to create the multi-swarm accelerating PSO which is applied to dynamic continuous functions. Different from the existing multi-swarm PSOs and local versions of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these swarms are regrouped frequently by using various regrouping schedules...
This paper studies hybrid dynamical evolutionary algorithm in the context of classification rule discovery. Nature inspired search algorithms such as genetic algorithms, Ant colonies and particle swarm optimization have been previously studied on data mining tasks, in particular, classification rule discovery. We extended this work by applying a hybrid algorithm which combines dynamical evolutionary...
The particle swarm optimization, a stochastic, population-based optimization technique, suffers from a phenomenon called premature convergence. That is, the system often loses diversity of the population at an early stage of searching. In this paper, a novel method called the thermodynamical particle swarm optimization (TDPSO)is proposed, which adopts the concepts of the temperature and entropy in...
The particle swarm optimization is a stochastic, population-based optimization technique. A modified PSO algorithm is proposed in this paper to avoid premature convergence with the new select mechanism. This mechanism is simulating the principle of molecular dynamics, which attempts to activate all particles as the most possible along with their population evolution. Two stopping criteria of the algorithm...
The particle swarm optimization is a stochastic, population-based optimization technique that can be applied to a wide range of problems. A multi- subpopulation accelerating particle swarm optimization(MAPSO)is proposed to improve the performance of the original algorithm. MAPSO views the excellent individuals as attractors and generates local small populations in the neighbor of them to maintain...
In this paper, we propose a particle swarm based algorithm to cluster peer-to-peer network hosts. Previously, the clustering of network hosts are mainly according to their connectivity [?], according to the RTTs between the hosts by probing each other, or cluster randomly. In our work, the information used to cluster the network hosts are getting from the network positioning system. The algorithm...
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