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

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12 Ant Colony Optimization Average of Solution Value 0e+00 2e+05 4e+05 6e+05 Average of Solution Value 500 1500 2500 Average of Solution Value 0 20 40 60 80 10 0 10 0 10 0 10 1 10 1 10 1 sphere−100 dimensions 10 Function evaluations 2 10 3 10 4 rastrigin−100 dimensions 10 Function evaluations 2 10 3 10 4 salomon−100 dimensions 10 Function evaluations 2 10 3 10 4 Sep−ACOr−ant2−q00001−k100 Sep−ACOr−ant2−q00001−k50 Sep−ACOr−ant2−q01−k100 Sep−ACOr−ant2−q01−k50 10 5 10 5 10 6 Sep−ACOr−ant2−q00001−k100 Sep−ACOr−ant2−q00001−k50 Sep−ACOr−ant2−q01−k100 Sep−ACOr−ant2−q01−k50 10 6 Sep−ACOr−ant2−q00001−k100 Sep−ACOr−ant2−q00001−k50 Sep−ACOr−ant2−q01−k100 Sep−ACOr−ant2−q01−k50 10 5 10 6 Average of Solution Value 0 5 10 15 20 Average of Solution Value 0 2000 4000 6000 Average of Solution Value 0 20 40 60 80 100 10 0 10 0 10 0 10 1 ackley−100 dimensions Sep−ACOr−ant2−q00001−k100 Sep−ACOr−ant2−q00001−k50 Sep−ACOr−ant2−q01−k100 Sep−ACOr−ant2−q01−k50 10 1 10 1 10 Function evaluations 2 10 3 10 4 griewank−100 dimensions 10 Function evaluations 2 10 3 10 4 10 Function evaluations 2 10 3 10 4 10 5 10 5 10 6 Sep−ACOr−ant2−q00001−k100 Sep−ACOr−ant2−q00001−k50 Sep−ACOr−ant2−q01−k100 Sep−ACOr−ant2−q01−k50 weierstrass−100 dimensions Sep−ACOr−ant2−q00001−k100 Sep−ACOr−ant2−q00001−k50 Sep−ACOr−ant2−q01−k100 Sep−ACOr−ant2−q01−k50 Figure 2.4: The development of the solution quality over the number of function evaluations for Sep-ACOR with different parameter configurations. The adopted parameter configurations for Sep-ACOR are shown in the legends. 10 5 10 6 10 6
2.2 Further Investigation on ACOR Solution value Solution value Solution value 1e−02 1e−08 1e−14 1e−20 1e−26 1000 500 200 100 20.0 10.0 5.0 2.0 1.0 0.5 ant2−q00001−k100 ant2−q00001−k100 ant2−q00001−k100 sphere−100 dimensions ant2−q00001−k50 ant2−q00001−k50 ant2−q01−k100 rastrigin−100 dimensions ant2−q01−k100 salomon−100 dimensions ant2−q00001−k50 ant2−q01−k100 ant2−q01−k50 ant2−q01−k50 ant2−q01−k50 Solution value Solution value 1e−02 1e−06 1e−10 1e−14 1e+00 1e−05 1e−10 1e−15 1e−20 1e+02 1e+01 1e+00 1e−01 1e−02 ant2−q00001−k100 ant2−q00001−k100 ant2−q00001−k100 ackley−100 dimensions ant2−q00001−k50 ant2−q00001−k50 ant2−q01−k100 griewank−100 dimensions ant2−q00001−k50 ant2−q01−k100 ant2−q01−k100 ant2−q01−k50 ant2−q01−k50 weierstrass−100 dimensions Figure 2.5: The box-plots show the solution quality after 1E+06 function evaluations for Sep-ACOR with different parameter configurations. Solution value ant2−q01−k50 13
Page 1: Université Libre de Bruxelles Facu
Page 5: Acknowledgements I thank Prof. Marc
Page 9 and 10: Contents Abstract iii Acknowledgeme
Page 11 and 12: Chapter 1 Introduction Metaheuristi
Page 13: them in the smallest function evalu
Page 16 and 17: 6 Ant Colony Optimization Algorithm
Page 18 and 19: 8 Ant Colony Optimization Algorithm
Page 20 and 21: 10 Ant Colony Optimization of Sep-A
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 and 74:
6.1 Mathematical formulation of eng
Page 75 and 76:
Bibliography [1] K. Abhishek, S. Le
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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