Advanced Intelligent Computing Theories and Applications. by Xuesong Yan, Qinghua Wu, Zhihua Cai (auth.), De-Shuang

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

This e-book - at the side of the 2 volumes LNCS 5226 and LNAI 5227 - constitutes the refereed lawsuits of the Fourth foreign convention on clever Computing, ICIC 2008, held in Shanghai, China in September 2008.

The clever computing expertise contains a variety of strategies resembling man made intelligence, perceptual and development reputation, evolutionary and adaptive computing, informatics theories and purposes, computational neuroscience and bioscience, smooth computing, case established and limited reasoning, brokers, networking and laptop supported co-operative operating, human desktop interface concerns.

ICIC subject matter unifies the image of up to date clever computing thoughts as an indispensable idea that highlights the developments in complex computational intelligence and bridges theoretical examine with applications.

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Additional 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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A New GA – Based and Graph Theory Supported Distribution System Planning 9 Fig. 1. Concept of minimum spanning tree the total length of branches is minimum. This algorithm is applied for generation of initial population in order to guarantee the two first major constraints of the problem and feasibility of the solution. It should be notice that the MST algorithm is only applied in the initialization of GA and during the progress of GA only special coding of crossover and mutation guarantees the feasibility of the solutions [10].

If ΔC’<0, accept S’, let S**= S’, q=0, moreover, if C(S* )> C(S’ ), let S* = S’ ’ IfΔC >0, accept S’ by the probability of Pa, if S’ is accepted, let S’(l+1)=S’, q=q+1, else let S’(l+1)=S’(l). ④ l=l+1. If termination condition (q>qmax or l>lmax) satisfied, continue the following steps, else go to ②. Replace S with S**. ③ ; ⑤ where, l is the length of Markov chain, lmax is the maximum length of Markov chain, Pa is the probability of inferior solutions being accepted, q is the times of the current best solution remain unchanged, qmax is the maximum times of the current best solution remains unchanged.

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. IEEE Trans. Power Deliver. : Applications of Graph Theory Algorithms.

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