Improved ant colony optimization algorithms for continuous ... - CoDE

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64 Appendix Table 6.6: The mathematical formulation for the coil spring design problem. f(x, T) = n i=1 Pi = ACi Thot Ti − 1 ⎛ Ti+1 ⎝ Ti ∆xi kdT No Constraint ∆xi ≥ 0, i = 1, ..., n + 1 g1 − T i T i−1 kdT ∆xi−1 g2 g3 Tcold ≤ T1 ≤ T2 ≤ ... ≤ Tn−1 ≤ Tn ≤ Thot n+1 i=1 ∆xi where = L C = 2.5 if T ≥ 71 K C = 4 if 71 K > T > 4.2 K C = 5 if T ≤ 4.2 K ⎞ ⎠
Bibliography [1] K. Abhishek, S. Leyffer, and J.T. Linderoth. Modeling without categorical variables: a mixed-integer nonlinear program for the optimization of thermal insulation systems. Optimization and Engineering, 11(2):185– 212, 2010. [2] M.A. Abramson. Pattern search algorithms for mixed variable general constrained optimization problems. PhD thesis, Rice University, USA, 2002. [3] M.A. Abramson. Mixed variable optimization of a load-bearing thermal insulation system using a filter pattern search algorithm. Optimization and Engineering, 5(2):157–177, 2004. [4] M.A. Abramson, C. Audet, J.W. Chrissis, and J.G. Walston. Mesh adaptive direct search algorithms for mixed variable optimization. Optimization Letters, 3(1):35–47, 2009. [5] B. Akay and D. Karaboga. Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, pages 1–14, 2010. [6] C. Audet and J.E. Dennis Jr. Pattern search algorithms for mixed variable programming. SIAM Journal on Optimization, 11(3):573–594, 2001. [7] A. Auger and N. Hansen. A restart CMA evolution strategy with increasing population size. In Proceedings of IEEE Congress on Evolutionary Computation, CEC 2005, pages 1769–1776, Piscataway, NJ, 2005. IEEE Press. [8] A. Auger, N. Hansen, J. Perez Zerpa, R. Ros, and M. Schoenauer. Experimental comparisons of derivative free optimization algorithms. In Jan Vahrenhold, editor, Experimental Algorithms, volume 5526 of LNCS, pages 3–15, Berlin, Germany, 2009. Springer. [9] P. Balaprakash, M. Birattari, and T. Stützle. Improvement strategies for the F-Race algorithm: Sampling design and iterative refinement. In
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Université Libre de Bruxelles Facu
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Acknowledgements I thank Prof. Marc
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Contents Abstract iii Acknowledgeme
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Chapter 1 Introduction Metaheuristi
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them in the smallest function evalu
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6 Ant Colony Optimization Algorithm
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8 Ant Colony Optimization Algorithm
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10 Ant Colony Optimization of Sep-A
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12 Ant Colony Optimization Average
Page 25 and 26: Chapter 3 An Incremental Ant Colony
Page 27 and 28: 3.2 IACOR with Local Search 17 3.2
Page 29 and 30: 3.3 Experimental Study 19 Algorithm
Page 31 and 32: 3.3 Experimental Study 21 Table 3.2
Page 33 and 34: 3.4 Conclusions 23 3.4 Conclusions
Page 35 and 36: 3.4 Conclusions 25 G−CMA−ES (5
Page 37: 3.4 Conclusions 27 Table 3.4: The m
Page 40 and 41: 30 Ant Colony Optimization for Mixe
Page 66 and 67: 56 Conclusions and Future Work of t
Page 69 and 70: Chapter 6 Appendix 6.1 Mathematical
Page 71 and 72: 6.1 Mathematical formulation of eng
Page 73: 6.1 Mathematical formulation of eng
Page 77 and 78: BIBLIOGRAPHY 67 [20] D. Datta and J
Page 79 and 80: BIBLIOGRAPHY 69 [44] Q. He and L. W
Page 81 and 82: BIBLIOGRAPHY 71 [65] S. Lucidi, V.
Page 83 and 84: BIBLIOGRAPHY 73 [87] Thomas Stützl
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