Download Intelligent Computing Technology: 8th International by Xia Hong, Sheng Chen (auth.), De-Shuang Huang, Changjun PDF

By Xia Hong, Sheng Chen (auth.), De-Shuang Huang, Changjun Jiang, Vitoantonio Bevilacqua, Juan Carlos Figueroa (eds.)

This e-book constitutes the 1st of three volumes of refereed convention lawsuits of the eighth foreign convention on clever Computing, ICIC 2012, held in Huangshan, China, in July 2012. The 242 revised complete papers offered have been conscientiously reviewed and chosen from 753 submissions. The eighty four papers integrated during this quantity are geared up in topical sections on evolutionary studying and genetic algorithms, fuzzy thought and versions, swarm intelligence and optimization, kernel tools and helping vector machines, nature encouraged computing and optimization, structures biology and computational biology, wisdom discovery and information mining, graph idea and algorithms, desktop studying idea and techniques, biomedical informatics concept and strategies, advanced structures concept and strategies, pervasive/ubiquitous computing idea and techniques, clever computing in bioinformatics, clever computing in trend acceptance, clever computing in photograph processing, clever computing in robotics, clever computing in laptop imaginative and prescient, clever computing in Petri nets/transportation structures, clever information fusion and data protection, clever sensor networks, wisdom representation/reasoning and professional platforms, hybrid optimization, and bio-inspired computing and application.

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Extra info for Intelligent Computing Technology: 8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012. Proceedings

Example text

Bω ∈ bnb ω0 bnb ω1 · · · bnb ωd The matrix M has rank 1 and its singular value decomposition is of the form ⎡ ⎤ δM 0 · · · 0 ⎢ 0 0 ··· 0⎥ T ⎥ M=Γ⎢ (28) ⎣ .......... , (d + 1)) are orthonormal vectors. δM is the sole non-zero singular value of M. b and ω can be obtained using ˆ = δ M Γ1 , ω ˆ = Δ1 b (29) ˆ ←− β b, ˆ ω ˆ ←− ω/β ˆ b (30) followed by where β = (1 + na j=1 a ˆj )/( nb ˆ j=1 bj ). 8 X. Hong and S. Chen Note that the standard Bai’s approach as above may suffer a serious numerical problem that the matrix M turns out to have rank higher than one, resulting in the parameters estimator far from usable.

2. Minimum of the total voyage. n m S2 = min  sij (3) i =1 j =1 n expresses the number of aircraft. m is the number of point. sij expresses the more voyage of the i aircraft at the point j . 3. Minimum of the gap of total voyage between the aircrafts. si ( j ) = S3 = min si − s j (4)  ( xi ( j )+1 − xi ( j ) ) + ( yi ( j )+1 − yi ( j ) ) , si is the total voyage of the 19 2 i ( j ) =1 2 aircraft i and s j is the total voyage of the aircraft j . 3 Scatter Search Algorithm The SS algorithm is a kind of optimization algorithm to solve the problem of decision planning and constraint policy.

Principle of Scatter Search. European Journal of Operationl Research 169, 359–372 (2006) 10. : A Scatter Search Methodology for the Nurse Rostering Problem. R. R. R. com Abstract. As an efficient and effective evolutionary algorithm, Differential evolution (DE) has received ever-increasing attention over recent years. However, how to make DE suitable for multi-objective optimization is still worth further studying. Moreover, various means from different perspectives are promising to promote the performance of the algorithm.

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