This paper aims at the combination of the artificial immune network and the support vector domain description for clustering. A new artificial immune antibody network is proposed. In the network, the antibody neighbor...
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This paper aims at the combination of the artificial immune network and the support vector domain description for clustering. A new artificial immune antibody network is proposed. In the network, the antibody neighborhood is represented as a support vector hypersphere, and an adaptive learning coefficient is presented. The input data set is firstly divided into subsets by antibodies, then each subset is mapped into a hypersphere respectively in a high dimensional feature space by support vector domain description. Finally the clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree, which need not a predefined number of clustering. The experimental results with several data sets illustrate the effectiveness of the proposed algorithm.
In this paper,a new lifting scheme of directionlet transform(LDT) is presented,the corresponding multidirectional and anisotropic transform has latticebased separable filtering and subsampling along any two directions...
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In this paper,a new lifting scheme of directionlet transform(LDT) is presented,the corresponding multidirectional and anisotropic transform has latticebased separable filtering and subsampling along any two directions with rational *** design an adaptive compression algorithm based on LDT,using the quad-tree segmentation resulting optimized *** results show that our proposed compression algorithm for image coding outperforms the standard wavelet-based SPIHT and JPEG2000 both in terms of PSNR and visual quality,especially at the low-rate.
A multi-agent social evolutionary algorithm for the precedence and resource constrained single-mode project optimization scheduling (RCPSP-MASEA) is proposed. RCPSP-MASEEA is used to obtain the optimal scheduling sequ...
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A multi-agent social evolutionary algorithm for the precedence and resource constrained single-mode project optimization scheduling (RCPSP-MASEA) is proposed. RCPSP-MASEEA is used to obtain the optimal scheduling sequences so that the duration of the project is minimized. With the intrinsic properties of RCPSP in mind, the multi-agent systems, social acquaintance net and evolutionary algorithms are integrated to form a new algorithm. In this algorithm, all agents live in lattice-like environment. Making use of the designed behaviors, RCPSP-MASEA realizes the ability of agents to sense and act on the environment in which they live, and the local environments of all the agents are constructed by social acquaintance net. Based on the characteristics of project optimization scheduling, the encoding of solution, the operators such as competitive, crossover and self-learning are given. During the process of interacting with the environment and the other agents, each agent increases energy as much as possible, so that RCPSP-MASEA can find the optima. Through a thorough computational study for a standard set of project instances in PSPLIB, the performance of algorithm is analyzed. The experimental results show RCPSP-MASEA has a good performance and it can reach near-optimal solutions in reasonable times. Compared with other heuristic algorithms, RCPSP-MASEA also has some advantages.
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