A parallel multi-objective optimization algorithm for the calibration of mathematical models

Swarm and Evolutionary Computation

Volumn 8, Issue , 2013, Pages 13-25

  Muraro, Daniele ^a,b   Dilão, Rui ^a  

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^b UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Cluster analysis; Evolutionary algorithms; Genetic regulation; Global optima; Multi objective optimization; Parameter identification

Indexed keywords

COVARIANCE MATRIX ADAPTATION; DATA SETS; EVOLUTIONARY COMPUTATION TECHNIQUES; EVOLUTIONARY STRATEGIES; GENETIC REGULATION; GLOBALLY CONVERGENT; GLOBALOPTIMUM; IDENTIFICATION OF THE PARAMETERS; INFINITE NUMBERS; MODEL EQUATIONS; MULTI OBJECTIVE; MULTI OBJECTIVE OPTIMIZATIONS (MOO); OPTIMIZATION PROBLEMS; PARETO SET; PHENOTYPIC PLASTICITY; REACTION-DIFFUSION MODELS; REGULATORY MECHANISM; SEARCH SPACES; SPEED OF CONVERGENCE; TEST FUNCTIONS;

CLUSTER ANALYSIS; COMPUTER SIMULATION; EVOLUTIONARY ALGORITHMS; GENE EXPRESSION; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84872339132     PISSN: 22106502     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.swevo.2012.07.004     Document Type: Article

References (37)

1. H.Md. Azamathulla, F.C. Wu, A. Ab Ghani, S.M. Narulkar, N.A. Zakaria, and C.K. Chang Comparison between genetic algorithm and linear programming approach for real time operation Journal of Hydro-environment Research 2 2008 172 181
2. A. Zhoua, B. Qu, H. Li, S. Zhao, P.N. Suganthan, and Q. u Zhang Multiobjective evolutionary algorithms: a survey of the state of the art Swarm and Evolutionary Computation 1 2011 32 49
3. Global Optimization Toolbox. Matlab R2012a, MathWorks.
4. R. Brits, A.P. Engelbrecht, and F. van den Bergh Locating multiple optima using particle swarm optimization Applied Mathematics and Computation 189 2007 1859 1883
5. N. Hansen, and A. Ostermeier Completely derandomized self-adaptation in evolution strategies Evolutionary Computation 9 2 2001 159 195
6. N. Hansen The CMA evolution strategy: a comparing review J.A. Lonzano, P. Larranga, I. Inza, E. Bengoetxea, Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms 2006 Springer 75 102
7. N. Hansen, The CMA Evolution Strategy: A Tutorial, Internet Document 〈 http://www.bionik.tu-berlin.de/user/niko/cmatutorial.pdf 〉, 2009.
8. J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks, IV, 1995, pp. 1942-1948.
9. Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: Proceedings of IEEE International Conference on Evolutionary Computation, 1998, pp. 69-73.
10. A. Colorni, M. Dorigo, V. Maniezzo, Distributed optimization by ant colonies, actes de la premi'ere conf'erence europ'eenne sur la vie artificielle, Paris, France, Elsevier Publishing, 1991, pp. 134-142.
11. M. Dorigo. Optimization, Learning and Natural Algorithms, Ph.D. Thesis, Politecnico di Milano, Italia, 1992.
12. D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
13. K. Parsopoulos, M. Vrahatis, Particle swarm optimization method in multiobjective problems, in: Proceedings of the ACM Symposium on Applied Computing (SAC), 2002, pp. 603-607.
14. C. Coello Coello, M. Salazar Lechuga, MOPSO: a proposal for multiple objective particle swarm optimization, in: Congress on Evolutionary Computation (CEC'2002), pp. 1051-1056.
15. N. Hansen, R. Ros, N. Mauny, M. Schoenauer, A. Auger, PSO Facing Non-separable and Ill-conditioned Problems, INRIA Research Report number RR-6447, 2008.
16. N. Hansen, A. Ostermeier, A. Gawelczyk, On the adaptation of arbitrary normal mutation distributions in evolution strategies: the generating set adaptation, in: ICGA95, 1995, pp. 57-64.
17. N. Hansen, A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation, in: ICEC96, IEEE Press, 1996, pp. 312-317.
18. N. Hansen, S. Müller, and P. Koumoutsakos Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation CMA-ES Evolutionary Computation 11 1 2003 89
19. A. Auger, N. Hansen, A restart CMA evolution strategy with increasing population size, in: CEC'05, IEEE Press, 2005, pp. 1769-1776.
20. N. Hansen, et al., Comparison of evolutionary algorithms on a Benchmark function set, in: CEC'05 Special Session, 2005.
21. R. Dilão, and D. Muraro mRNA diffusion explains protein gradients in Drosophila early development Journal of Theoretical Biology 264 2010 847 853
22. J. Li, Mixture Models 〈 http://www.stat.psu.edu/jiali/course/ stat597e/notes2/mix.pdf 〉.
23. M. Fleischer, The measure of Pareto optima, applications to multi-objective metaheuristics, in: C.M. Fonseca, et al. (Eds.), Evolutionary Multi-Criterion Optimization, Second International Conference, EMO 2003, Lecture Notes in Computer Science, vol. 2632, Springer, Faro, Portugal, 2003, pp. 519-533.
24. R. Dilão, D. Muraro, M. Nicolau, M. Schoenauer, Validation of a morphogenesis model of Drosophila early development by a multi-objective evolutionary optimization algorithm, in: Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in BioInformatics, EvoBIO 2009, Springer Verlag, Tubingen, Germany, April 15-17, 2009.
25. C. Igel, N. Hansen, and S. Roth Covariance matrix adaptation for multi-objective optimization Evolutionary Computation 15 1 2007 1 28
26. T. Voß, N. Hansen, C. Igel, Recombination for learning strategy parameters in the MO-CMA-ES, in: EMO Conference on Evolutionary Multi-Criterion Optimization 2009, Proceedings.
27. T. Voß, N. Hansen, C. Igel, Improved step size adaptation for the MO-CMA-ES, in: Branke, et al. (Eds.), ACM Genetic and Evolutionary Computation Conference GECCO 2010, Proceedings, ACM, pp. 487-494.
28. T. Voß, N. Beume, G. Rudolph, C. Igel, Scalarization versus indicator-based selection in multi-objective CMA evolution strategies, in: IEEE Congress on Evolutionary Computation IEEE, 2008, pp. 3036-3043.
29. E. Zitzler, and L. Thiele Multiobjective optimization using evolutionary a comparative case study A.E. Eiben, T. Bäck, M. Schoenauer, H.P. Schwefel, Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V) 1998 Springer-Verlag 292 301
30. J.D. Knowles, D.W. Corne, M. Fleisher, Bounded archiving using the Lebesgue measure, in: Proceedings of CEC'2003, vol. 4, IEEE Press, 2003, pp. 2490-2497.
31. K. Deb, P. Zope, A. Jain, Distributed Computing of Pareto-Optimal Solutions with Evolutionary Algorithms, Evolutionary Multi-Criterion Optimization, vol. 2632/2003, Springer, 2003, p. 11.
32. W. Gropp, E. Lusk, and R. Thakur Using MPI-2: Advanced Features of the Message-Passing Interface 1999 MIT Press
33. D. Buntinas, C. Coti, T. Hrault, P. Lemarinier, L. Pilard, A. Rezmerita, E. Rodriguez, and F. Cappello Blocking vs. non-blocking coordinated checkpointing for large-scale fault tolerant MPI Future Generation Computer Systems 24 January (1) 2008 73 84
34. A. Spirov, K. Fahmy, M. Schneider, E. Frei, M. Nooll, and S. Baumgartner Formation of the bicoid morphogen gradient: an mRNA gradient dictates the protein gradient Development 136 2009 605 614
35. R. Dilão, and J. Sainhas Validation and calibration of models for reaction-diffusion systems International Journal of Bifurcation and Chaos 8 1998 1163 1182
36. N. Hansen, Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed, in: Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, ACM, 2009, pp. 2389-2395.
37. H.-P. Schwefel Numerical Optimization of Computer Models 1981-1995 J. Wiley & Sons New York