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Bat Algorithm for Topology Optimization                  in Microelectronic Applications            Xin-She Yang, Mehmet K...
Introduction   Topology/Shape DesignTopology/Shape DesignGiven a geometry (say, a rectangle), how to distribute two differe...
Introduction   Should we use different methods for different problems?Should we use different methods for different problems?C...
Introduction   Bat Algorithm, Developed by Xin-She Yang (in 2010)Bat Algorithm, Developed by Xin-She Yang (in 2010)BBC Vid...
Introduction   Bat Algorithm (Yang 2010)Bat Algorithm (Yang 2010)Acoustics of bat echolocation                            ...
Introduction   Bat Algorithm (Yang 2010)Bat Algorithm (Yang 2010)Acoustics of bat echolocation                            ...
Introduction   AdvantagesAdvantagesDynamic exploration and exploitation    Simple to implement, and it searches for optima...
Introduction   Variants and ApplicationsVariants and Applications      Continuous optimization      Binary bat algorithm f...
Introduction   Speed Reducer/Gear Box DesignSpeed Reducer/Gear Box DesignMixed-Integer Programming:Continuous variables an...
Introduction                                                  2         2f (x1 , x2 , x3 , x4 , x5 , x6 , x7 ) = 0.7854x1 ...
IntroductionThe best solution obtained by BA is                               fmin = 2993.7495888,with              x∗ = (...
Introduction   Topology/Shape Design in Microelectronic ApplicationsTopology/Shape Design in Microelectronic Applications ...
Introduction   Optimal TopologyOptimal TopologyDistributions of two materials (left) and temperature (right). The material...
Introduction   Change of Objective Leads to Different TopologyChange of Objective Leads to Different TopologyNow the objecti...
Introduction   BibliographyBibliography      A. Evgrafov, K. Maute, R. G. Yang and M. L. Dunn, Topology optimization      ...
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Bat algorithm for Topology Optimization in Microelectronic Applications

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This is the presentation for the paper of the same title at the Future Generation Computer Technology 2012.

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Bat algorithm for Topology Optimization in Microelectronic Applications

  1. 1. Bat Algorithm for Topology Optimization in Microelectronic Applications Xin-She Yang, Mehmet Karamanoglu and Simon Fong @ FGCT2012Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 1 / 14
  2. 2. Introduction Topology/Shape DesignTopology/Shape DesignGiven a geometry (say, a rectangle), how to distribute two differentmaterials, with thermal conductivities K1 and K2 , respectively, so as tomeet a specific design problem for heat transfer applications?To maximize |TA − TB |? Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 2 / 14
  3. 3. Introduction Should we use different methods for different problems?Should we use different methods for different problems?Changing the landscape:Space mapping, surrogate, trust-region, dimension reduction ... Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 3 / 14
  4. 4. Introduction Bat Algorithm, Developed by Xin-She Yang (in 2010)Bat Algorithm, Developed by Xin-She Yang (in 2010)BBC VideoMicrobats use echolocation for hunting Ultrasonic short pulses as loud as 110dB with a short period of 5 to 20 ms. Frequencies of 25 kHz to 100 kHz. Speed up the pulse-emission rate, and increase loudness, when homing at a prey. Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 4 / 14
  5. 5. Introduction Bat Algorithm (Yang 2010)Bat Algorithm (Yang 2010)Acoustics of bat echolocation v λ = ∼ 2 mm to 14 mm. fRules used in the bat algorithm: fi = fmin + (fmax − fmin )β, β ∈ [0, 1], vt+1 = vit + (xt − x∗ )fi , i i xt+1 = xt + vt . i i i Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 5 / 14
  6. 6. Introduction Bat Algorithm (Yang 2010)Bat Algorithm (Yang 2010)Acoustics of bat echolocation v λ = ∼ 2 mm to 14 mm. fRules used in the bat algorithm: fi = fmin + (fmax − fmin )β, β ∈ [0, 1], vt+1 = vit + (xt − x∗ )fi , i i xt+1 = xt + vt . i i iVariations of Loudness and Pulse Rate At+1 ← αAt , i i α ∈ (0, 1], rit+1 = ri0 [1 − exp(−γt)]. Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 5 / 14
  7. 7. Introduction AdvantagesAdvantagesDynamic exploration and exploitation Simple to implement, and it searches for optimality using frequency tuning. Initially, BA focuses on more explorative moves, and then switch to more exploitation when optimality is approaching. Balance between exploration and exploitation is not static, it is dynamic! Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 6 / 14
  8. 8. Introduction Variants and ApplicationsVariants and Applications Continuous optimization Binary bat algorithm for image processing and classifications Spam filtering Training neural networks Multobjective bat algorithm Clustering ... Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 7 / 14
  9. 9. Introduction Speed Reducer/Gear Box DesignSpeed Reducer/Gear Box DesignMixed-Integer Programming:Continuous variables and integers. Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 8 / 14
  10. 10. Introduction 2 2f (x1 , x2 , x3 , x4 , x5 , x6 , x7 ) = 0.7854x1 x2 (3.3333x3 + 14.9334x3 − 43.0934) 2 2 3 3 2 2 −1.508x1 (x6 + x7 ) + 7.4777(x6 + x7 ) + 0.7854(x4 x6 + x5 x7 ),subject to 27 397.5 g1 = 2 x1 x2 x3 − 1 ≤ 0, g2 = 2 2 x1 x2 x3 − 1 ≤ 0, 1.93x 3 1.93x5 3 g3 = x x d44 − 1 ≤ 0, g4 = 4 x2 x3 d2 − 1 ≤ 0, 2 3 1 g5 = 110x 3 ( 745x4 )2 + 16.9 × 106 − 1 ≤ 0, 1 hx3 6 1 745x5 2 g6 = 85x 3 ( hx3 ) + 157.5 × 106 − 1 ≤ 0, 7 g7 = x403 − 1 ≤ 0, 2x g8 = 5x12 − 1 ≤ 0, x x1 g9 = 12x2 − 1 ≤ 0, g10 = 1.5xx4 6 +1.9 − 1 ≤ 0, g11 = 1.1xx57 +1.9 − 1 ≤ 0.Simple bounds are 2.6 ≤ x1 ≤ 3.6, 0.7 ≤ h ≤ 0.8, 17 ≤ x3 ≤ 28,7.3 ≤ x4 ≤ 8.3, 7.8 ≤ x5 ≤ 8.3, 2.9 ≤ x6 ≤ 3.9, and 5.0 ≤ x7 ≤ 5.5. zmust be integers. Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 9 / 14
  11. 11. IntroductionThe best solution obtained by BA is fmin = 2993.7495888,with x∗ = (3.5, 0.7, 17, 7.3, 7.8, 3.34446445, 5.285350625),which is better than the solution in the literature (Cagnina et al., 2008) f∗ = 2996.348165. Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 10 / 14
  12. 12. Introduction Topology/Shape Design in Microelectronic ApplicationsTopology/Shape Design in Microelectronic Applications Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 11 / 14
  13. 13. Introduction Optimal TopologyOptimal TopologyDistributions of two materials (left) and temperature (right). The material(K2 K1 ) in the middle has lower conductivity so that |TA − TB | ismaximum.Temperature: Red=high, blue=low. Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 12 / 14
  14. 14. Introduction Change of Objective Leads to Different TopologyChange of Objective Leads to Different TopologyNow the objective is to maximize |TA − TB | where A and B are on thehorizontal middle axis.Temperature: Red=high, blue=low. Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 13 / 14
  15. 15. Introduction BibliographyBibliography A. Evgrafov, K. Maute, R. G. Yang and M. L. Dunn, Topology optimization for nano-scale heat transfer, Int. J. Num. Methods in Engrg., 77 (2), 285-300 (2009). X. S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010). X. S. Yang, bat algorithm for multi-objective optimisation, Int. J. Bio-Inspired Computation, Vol. 3, 267-274 (2011). X. S. Yang, Engineering Optimization: An Introduction With Metaheuristic Applications, John Wiley and Sons, USA, (2010). V. V. Zhirnov, R. K. Cavin, J. A. Hutchby, G. I. Bourianoff, Limits to binary logic switch scaling - a gedanken model, Proc. of the IEEE, 91(11), 1934-1939 (2003).Thank you very much :) Yang,Karamanoglu,Fong (NPL) Bat Algorithm @ FGCT2012 14 / 14

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