Free Access
Issue
RAIRO-Oper. Res.
Volume 54, Number 2, March-April 2020
Page(s) 451 - 469
DOI https://doi.org/10.1051/ro/2019096
Published online 27 February 2020
  • Artelys, Knitro nonlinear optimization solver. https://www.artelys.com/en/optimization-tools/knitro (2019). [Google Scholar]
  • J.R. Banga, Optimization in computational systems biology. BMC Syst. Biol. 2 (2008) 1–47. [Google Scholar]
  • R.H. Byrd, J. Nocedal and R.A. Waltz, Knitro: an integrated package for nonlinear optimization. In: Large-scale Nonlinear Optimization. Springer (2006) 35–59. [CrossRef] [Google Scholar]
  • C.A.C. Coello, Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191 (2002) 1245–1287. [Google Scholar]
  • A.R. Conn, G. Gould and P.L. Toint, In: 17 of LANCELOT: A Fortran Package for Large-scale Nonlinear Optimization (Release A). Springer Science & Business Media (2013). [Google Scholar]
  • A.V. Fiacco and G.P. McCormick, Nonlinear Programming: Sequential Unconstrained Minimization Techniques. John Wiley and Sons, New York (1968). [Google Scholar]
  • GAMS World site. Cute models section. http://www.gamsworld.org/performance/princetonlib/htm/group5stat.htm (2019). [Google Scholar]
  • W.L. Goffe, G.D. Ferrier and J. Rogers, Global optimization of statistical functions with simulated annealing. J. Econ. 60 (1994) 65–99. [CrossRef] [Google Scholar]
  • J.D. Griffin and T. Kolda, Nonlinearly constrained optimization using heuristic penalty methods and asynchronous parallel generating set search. Appl. Math. Res. Express 2010 (2010) 36–62. [Google Scholar]
  • J. Hald and K. Madsen, Combined lp and quasi-newton methods for minimax optimization. Math. Program. 20 (1981) 49–62. [Google Scholar]
  • N. Hansen. A Python implementation of CMA-ES. https://github.com/CMA-ES/pycma (2017). [Google Scholar]
  • N. Hansen and S. Kern, Evaluating the CMA evolution strategy on multimodal test functions. Springer (2004) 282–291. [Google Scholar]
  • N. Hansen and A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In: Proceedings of IEEE International Conference on Evolutionary Computation. IEEE (1996) 312–317. [Google Scholar]
  • N. Hansen, S.D. Müller and P. Koumoutsakos, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11 (2003) 1–18. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • A. Homaifar, C.X. Qi and S.H. Lai, Constrained optimization via genetic algorithms. Simulation 62 (1994) 242–253. [Google Scholar]
  • M. Kaucic, A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization. J. Global Optim. 55 (2013) 165–188. [CrossRef] [Google Scholar]
  • J. Kennedy and R. Eberhart, Pso optimization. In: Vol. 4 of Proc. IEEE Int. Conf. Neural Networks. IEEE Service Center, Piscataway, NJ (1995) 1941–1948. [Google Scholar]
  • S. Kirkpatrick, C.D. Gelatt and M.P. Vecchi, Optimization by simulated annealing. Science 220 (1983) 671–680. [Google Scholar]
  • A. Lee, Particle swarm optimization (PSO) with constraint support. https://github.com/tisimst/pyswarm (2015). [Google Scholar]
  • K.S. Lee and Z.W. Geem, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194 (2005) 3902–3933. [Google Scholar]
  • D.G. Luenberger, Linear and Nonlinear Programming. Addison-Wesley, Menlo Park, CA (1984). [Google Scholar]
  • S. Luke, Essentials of Metaheuristics, 2nd edition. Lulu. Available for free at http://cs.gmu.edu/sean/book/metaheuristics/ (2013). [Google Scholar]
  • M.E.-B. Menai, M. Batouche, Efficient initial solution to extremal optimization algorithm for weighted maxsat problem. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer (2003) 592–603. [Google Scholar]
  • Z. Michalewicz, Genetic algorithms for numerical optimization. Int. Trans. Oper. Res. 1 (1994) 223–240. [Google Scholar]
  • Z. Michalewicz and C.Z. Janikow, Genetic algorithms for numerical optimization. Stat. Comput. 1 (1991) 75–91. [Google Scholar]
  • K.E. Parsopoulos and M.N. Vrahatis, Particle swarm optimization method for constrained optimization problems. Int. Technol.–Theory App.: New Trends Intell. Technol. 76 (2002) 214–220. [Google Scholar]
  • D.R. Penas, P. González, J.A. Egea, J.R. Banga and R. Doallo, Parallel metaheuristics in computational biology: an asynchronous cooperative enhanced scatter search method. Proc. Comput. Sci. 51 (2015) 630–639. [CrossRef] [Google Scholar]
  • D.R. Penas, D. Henriques, P. González, R. Doallo, J. Saez-Rodriguez and J.R. Banga, A parallel metaheuristic for large mixed-integer nonlinear dynamic optimization problems, with applications in computational biology. PLoS One 12 (2017) 1–32. [Google Scholar]
  • R. Poli, J. Kennedy and T. Blackwell, Particle swarm optimization. Swarm Intell. 1 (2007) 33–57. [CrossRef] [Google Scholar]
  • M. Powell, A tolerant algorithm for linearly constrained optimization calculations. Math. Program. 45 (1989) 547–566. [Google Scholar]
  • R.L. Rardin and R. Uzsoy, Experimental evaluation of heuristic optimization algorithms: a tutorial. J. Heuristics 7 (2001) 261–304. [CrossRef] [Google Scholar]
  • M. Rieck, M. Richter, M. Bittner and F. Holzapfel, Generation of initial guesses for optimal control problems with mixed integer dependent constraints. In: ICAS 29th International Conference (2014). [Google Scholar]
  • J. Robert, Vanderbei website. University of Princeton. https://vanderbei.princeton.edu/ampl/nlmodels/ (2019). [Google Scholar]
  • B. Suman and P. Kumar, A survey of simulated annealing as a tool for single and multiobjective optimization. J. Oper. Res. Soc. 57 (2006) 1143–1160. [Google Scholar]
  • T. Takahashi, Metaheuristic Algorithms Python. https://github.com/tadatoshi/ (2015). [Google Scholar]
  • E. Talbi, Metaheuristics: From Design to Implementation, 1st edition. Wiley (2009). ISBN 9780470278581. [Google Scholar]
  • Z. Ugray, L. Lasdon, J. Plummer, F. Glover, J. Kelly and R. Mart, Scatter search and local NLP solvers: a multistart framework for global optimization. INFORMS J. Comput. 19 (2007) 328–340. [Google Scholar]
  • University of Magdeburg, The best known packings of equal circles in a square. http://hydra.nat.uni-magdeburg.de/packing/csq/csq.html (2013). [Google Scholar]
  • Ö. Yeniay, Penalty function methods for constrained optimization with genetic algorithms. Math. Comput. App. 10 (2005) 45–56. [Google Scholar]
  • F. Zhang, A.C. Reynolds and D.S. Oliver, An initial guess for the levenberg–marquardt algorithm for conditioning a stochastic channel to pressure data. Math. Geol. 35 (2003) 67–88. [Google Scholar]

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