By Xuesong Yan, Qinghua Wu, Zhihua Cai (auth.), De-Shuang Huang, Donald C. Wunsch II, Daniel S. Levine, Kang-Hyun Jo (eds.)

This booklet - along side the 2 volumes LNCS 5226 and LNAI 5227 - constitutes the refereed complaints of the Fourth overseas convention on clever Computing, ICIC 2008, held in Shanghai, China in September 2008.

The clever computing expertise features a variety of recommendations equivalent to man made intelligence, perceptual and development reputation, evolutionary and adaptive computing, informatics theories and functions, computational neuroscience and bioscience, delicate computing, case established and limited reasoning, brokers, networking and machine supported co-operative operating, human desktop interface matters.

ICIC topic unifies the image of latest clever computing suggestions as an quintessential idea that highlights the tendencies in complicated computational intelligence and bridges theoretical examine with applications.

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Extra info for Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques: 4th International Conference on Intelligent Computing, ICIC 2008 Shanghai, China, September 15-18, 2008 Proceedings

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New Multiobjective Tabu Search Algorithm for Fuzzy Optimal Planning of Power Distribution Systems. IEEE Trans. Power Sys. : Electric Power Distribution Systems Engineering. : Electricity Distribution Network Design. : Electrical Distribution Engineering. : Power Distribution Planning Reference Book. : Power distribution planning: A review of models and issues. IEEE Trans. Power Sys. : Ant Colony System Algorithm for the Planning of the Primary Distribution Circuits. IEEE Trans. Power Sys. : Optimization of Electrical Distribution Feeders Using Simulated Annealing.

Fig. 4 shows the results of optimal feeder routing for 20-kV feeder Fig. 3. Optimal system configuration (first test case) A New GA – Based and Graph Theory Supported Distribution System Planning 13 Fig. 4. Optimal network configuration (second test case) Fig. 5. Trajectory of best solution of GA networks (continuous segments) and the feasible routes (dashed segments) to build future feeders configuration. The feasible feeder (dashed lines) in the figure includes both existing and new proposed feeder routes.

Fig. 1. Convergence curves for different production plans (4 machines) Firstly, assume that the MMAL has 4 machines and the buffer size between every two successive machines is 1. Computational results by the GASA and the GA, respectively, are shown in Table 4 for different production plans. Fig. 1 shows the convergence curves. It can be seen, for small-sized problems, the same best total objective function value, TOFV, can be obtained by both the GASA and GA. However, for mediate and large-sized problems, the best solutions obtained by the GASA are better than those obtained by the GA.

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