By Xuesong Yan, Qinghua Wu, Zhihua Cai (auth.), De-Shuang Huang, Donald C. Wunsch II, Daniel S. Levine, Kang-Hyun Jo (eds.)
This ebook - along side the 2 volumes LNCS 5226 and LNAI 5227 - constitutes the refereed complaints of the Fourth foreign convention on clever Computing, ICIC 2008, held in Shanghai, China in September 2008.
The clever computing know-how contains a diversity of recommendations similar to synthetic intelligence, perceptual and trend reputation, evolutionary and adaptive computing, informatics theories and purposes, computational neuroscience and bioscience, gentle computing, case established and limited reasoning, brokers, networking and machine supported co-operative operating, human machine interface concerns.
ICIC subject unifies the image of latest clever computing thoughts as an imperative idea that highlights the tendencies in complicated computational intelligence and bridges theoretical learn with applications.
Read or Download 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 PDF
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Extra resources 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 hybrid algorithm (GASA), based on a genetic algorithm (GA) and a simulated annealing algorithm (SA), is proposed for solving the model. The performance of the GASA is compared with the GA over various test problems. The results show that, in terms of solutions’ quality, both the GASA and GA can find the same best solution for small-sized problems and the GASA performs better than the GA for mediate and large-sized problems. Moreover, the impact of buffer size on MMAL’s performance is investigated.
Ij + ∑ Δτ ijk (10) t =1 ⎧Q ⎪ Δτ = ⎨Sk ⎪0 ⎩ k ij if thekth ant walksedge(i, j) (11) otherwise where Sk is the distance of the kth ant at current cycle. 5 Improved Ant Colony When the pheromone intensity of one route is higher heavily than other routes, ant colony model will be premature and can’t find the most optimal solution. To overcome this drawback, we improve the ant colony model through introducing genetic algorithm. The purpose of introducing genetic algorithm is improving the weak local exploitation capability.
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