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24. M.P. Hansen, A. Jaszkiewicz, Evaluating the quality of approximations to the non-dominated
set (1998)
25. N. Hansen, S. Finck, R. Ros, A. Auger, Real-parameter Black-Box optimization benchmarking
2009: noiseless functions definitions. Research Report RR-6829, INRIA, 2009
26. S. Huband, L. Barone, L. While, P. Hingston, A scalable multi-objective test problem toolkit, in
Evolutionary Multi-Criterion Optimization, ed. by C.A. Coello Coello, A. Hernández Aguirre,
E. Zitzler (Springer, Berlin, 2005), pp. 280–295
27. S. Huband, P. Hingston, L. Barone, L. While, A review of multiobjective test problems and a
scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477–506 (2006)
28. D. Irawan, Geometrical optimization of plate-fin heat-sink. Master’s thesis, Chalmers University of Technology, Gothenburg, 2016
29. H. Ishibuchi, N. Tsukamoto, Y. Nojima, Evolutionary many-objective optimization: a short
review, in IEEE Congress on Evolutionary Computation (IEEE, New York, 2008), pp. 2419–
2426
30. D.R. Jones, M. Schonlau, W.J. Welch, Efficient global optimization of expensive black-box
functions. J. Global Optim. 13(4), 455–492 (1998)
31. M. Kitti, History of optimization. http://www.mitrikitti.fi/opthist.html. Last accessed 05 Apr
2018
32. J. Knowles, ParEGO: a hybrid algorithm with on-line landscape approximation for expensive
multiobjective optimization problems. IEEE Trans. Evol. Comput. 10(1), 50–66 (2006)
33. A. Messac, C.A. Mattson, Normal constraint method with guarantee of even representation of
complete pareto frontier. AIAA J. 42(10), 2101–2111 (2004)
34. K. Miettinen, F. Ruiz, A.P. Wierzbicki, Introduction to Multiobjective Optimization: Interactive
Approaches (Springer, Berlin, Heidelberg, 2008), pp. 27–57
35. T. Okabe, Y. Jin, B. Sendhoff, A critical survey of performance indices for multi-objective
optimisation, in The 2003 Congress on Evolutionary Computation, 2003. CEC ’03, vol. 2
(2003), pp. 878–885
36. N. Riquelme, C.V. Lücken, B. Baran, Performance metrics in multi-objective optimization, in
2015 Latin American Computing Conference (CLEI) (2015), pp. 1–11
37. H. Seada, K. Deb, U-NSGA-III: a unified evolutionary optimization procedure for single,
multiple, and many objectives: proof-of-principle results, in Evolutionary Multi-Criterion
Optimization, ed. by A. Gaspar-Cunha, C. Henggeler Antunes, C.C. Coello, (Springer International Publishing, Cham, 2015), pp. 34–49
38. T. Tušar, B. Filipiˇ c, Visualization of pareto front approximations in evolutionary multiobjective
optimization: a critical review and the prosection method. IEEE Trans. Evol. Comput. 19(2),
225–245 (2015)
39. L. van der Maaten, E.O. Postma, H.J. van den Herik, Dimensionality reduction: a comparative
review (2008)
40. D.A.V. Veldhuizen, G.B. Lamont, On measuring multiobjective evolutionary algorithm performance, in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat.
No.00TH8512), July, vol. 1, (2000), pp. 204–211
41. D.A.V. Veldhuizen, G.B. Lamont, Multiobjective evolutionary algorithms: analyzing the stateof-the-art. Evol. Comput. 8(2), 125–147 (2000)
42. P.A. Vikhar, Evolutionary algorithms: a critical review and its future prospects, in 2016
International Conference on Global Trends in Signal Processing, Information Computing and
Communication (ICGTSPICC), December 2016, pp. 261–265
43. T. Wagner, N. Beume, B. Naujoks, Pareto-, Aggregation-, and Indicator-Based Methods in
Many-Objective Optimization (Springer, Berlin, Heidelberg, 2007), pp. 742–756
44. M. Wahde and Dawsonera (e-book collection), Biologically Inspired Optimization Methods:
An Introduction (WIT Press, Southampton, 2008)
45. W.M. Wang, X.Q. Yan, C.W. Fu, A.J. Hanson, P.A. Heng, Interactive exploration of 4d
geometry with volumetric halos (2013)
46. Q. Zhang, H. Li, MOEA/D: a multiobjective evolutionary algorithm based on decomposition.
IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
D. Irawan and B. Naujoks
24. M.P. Hansen, A. Jaszkiewicz, Evaluating the quality of approximations to the non-dominated
set (1998)
25. N. Hansen, S. Finck, R. Ros, A. Auger, Real-parameter Black-Box optimization benchmarking
2009: noiseless functions definitions. Research Report RR-6829, INRIA, 2009
26. S. Huband, L. Barone, L. While, P. Hingston, A scalable multi-objective test problem toolkit, in
Evolutionary Multi-Criterion Optimization, ed. by C.A. Coello Coello, A. Hernández Aguirre,
E. Zitzler (Springer, Berlin, 2005), pp. 280–295
27. S. Huband, P. Hingston, L. Barone, L. While, A review of multiobjective test problems and a
scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477–506 (2006)
28. D. Irawan, Geometrical optimization of plate-fin heat-sink. Master’s thesis, Chalmers University of Technology, Gothenburg, 2016
29. H. Ishibuchi, N. Tsukamoto, Y. Nojima, Evolutionary many-objective optimization: a short
review, in IEEE Congress on Evolutionary Computation (IEEE, New York, 2008), pp. 2419–
2426
30. D.R. Jones, M. Schonlau, W.J. Welch, Efficient global optimization of expensive black-box
functions. J. Global Optim. 13(4), 455–492 (1998)
31. M. Kitti, History of optimization. http://www.mitrikitti.fi/opthist.html. Last accessed 05 Apr
2018
32. J. Knowles, ParEGO: a hybrid algorithm with on-line landscape approximation for expensive
multiobjective optimization problems. IEEE Trans. Evol. Comput. 10(1), 50–66 (2006)
33. A. Messac, C.A. Mattson, Normal constraint method with guarantee of even representation of
complete pareto frontier. AIAA J. 42(10), 2101–2111 (2004)
34. K. Miettinen, F. Ruiz, A.P. Wierzbicki, Introduction to Multiobjective Optimization: Interactive
Approaches (Springer, Berlin, Heidelberg, 2008), pp. 27–57
35. T. Okabe, Y. Jin, B. Sendhoff, A critical survey of performance indices for multi-objective
optimisation, in The 2003 Congress on Evolutionary Computation, 2003. CEC ’03, vol. 2
(2003), pp. 878–885
36. N. Riquelme, C.V. Lücken, B. Baran, Performance metrics in multi-objective optimization, in
2015 Latin American Computing Conference (CLEI) (2015), pp. 1–11
37. H. Seada, K. Deb, U-NSGA-III: a unified evolutionary optimization procedure for single,
multiple, and many objectives: proof-of-principle results, in Evolutionary Multi-Criterion
Optimization, ed. by A. Gaspar-Cunha, C. Henggeler Antunes, C.C. Coello, (Springer International Publishing, Cham, 2015), pp. 34–49
38. T. Tušar, B. Filipiˇ c, Visualization of pareto front approximations in evolutionary multiobjective
optimization: a critical review and the prosection method. IEEE Trans. Evol. Comput. 19(2),
225–245 (2015)
39. L. van der Maaten, E.O. Postma, H.J. van den Herik, Dimensionality reduction: a comparative
review (2008)
40. D.A.V. Veldhuizen, G.B. Lamont, On measuring multiobjective evolutionary algorithm performance, in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat.
No.00TH8512), July, vol. 1, (2000), pp. 204–211
41. D.A.V. Veldhuizen, G.B. Lamont, Multiobjective evolutionary algorithms: analyzing the stateof-the-art. Evol. Comput. 8(2), 125–147 (2000)
42. P.A. Vikhar, Evolutionary algorithms: a critical review and its future prospects, in 2016
International Conference on Global Trends in Signal Processing, Information Computing and
Communication (ICGTSPICC), December 2016, pp. 261–265
43. T. Wagner, N. Beume, B. Naujoks, Pareto-, Aggregation-, and Indicator-Based Methods in
Many-Objective Optimization (Springer, Berlin, Heidelberg, 2007), pp. 742–756
44. M. Wahde and Dawsonera (e-book collection), Biologically Inspired Optimization Methods:
An Introduction (WIT Press, Southampton, 2008)
45. W.M. Wang, X.Q. Yan, C.W. Fu, A.J. Hanson, P.A. Heng, Interactive exploration of 4d
geometry with volumetric halos (2013)
46. Q. Zhang, H. Li, MOEA/D: a multiobjective evolutionary algorithm based on decomposition.
IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)