Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods

  • Antonio MucherinoEmail author
  • Onur Seref
Part of the Springer Optimization and Its Applications book series (SOIA, volume 25)


Many real-life problems can be modeled as global optimization problems. There are many examples that come from agriculture, chemistry, biology, and other fields. Meta-heuristic methods for global optimization are flexible and easy to implement and they can provide high-quality solutions. In this chapter, we give a brief review of the frequently used heuristic methods for global optimization. We also provide examples of real-life problems modeled as global optimization problems and solved by meta-heuristic methods, with the aim of analyzing the heuristic approach that is implemented.


Objective Function Particle Swarm Optimization Simulated Annealing Differential Evolution Forest Inventory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    L. Angelis, G. Stamatellos, Multiple Objective Optimization of Sampling Designs for Forest Inventories using Random Search Algorithms, Computers and Electronics in Agriculture 42(3), 129–148, 2004.CrossRefGoogle Scholar
  2. 2.
    D. Baker, A Surprising Simplicity to Protein Folding, Nature 405, 39–42, 2000.CrossRefGoogle Scholar
  3. 3.
    J.R. Banavar, A. Maritan, C. Micheletti and A. Trovato, Geometry and Physics of Proteins, Proteins: Structure, Function, and Genetics 47(3), 315–322, 2002.CrossRefGoogle Scholar
  4. 4.
    J. Brandao, A Tabu Search Algorithm for the Open Vehicle Routing Problem, European Journal of Operational Research 157(3), 552–564, 2004.MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    W. Ben-Ameur, Computing the Initial Temperature of Simulated Annealing, Computational Optimization and Applications 29(3), 369–385, 2004.MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    S. Cafieri, M. D’Apuzzo, M. Marino, A. Mucherino, and G. Toraldo, Interior Point Solver for Large-Scale Quadratic Programming Problems with Bound Constraints, Journal of Optimization Theory and Applications 129(1), 55–75, 2006.MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
  8. 8.
    G. Ceci, A. Mucherino, M. D’Apuzzo, D. di Serafino, S. Costantini, A. Facchiano, and G. Colonna, Computational Methods for Protein Fold Prediction: an Ab-Initio Topological Approach, Data Mining in Biomedicine, Springer Optimization and Its Applications, Panos Pardalos et al. (Eds.), vol.7, Springer, Berlin, 2007.Google Scholar
  9. 9.
    A.R. Conn and N.I.M. Gould, Trust-Region Methods, SIAM Mathematical Optimization, 2000.Google Scholar
  10. 10.
    P.G. De Vries, Sampling for Forest Inventory, Springer, Berlin, 1986.Google Scholar
  11. 11.
    M. Dorigo and G. Di Caro, Ant Colony Optimization: A New Meta-Heuristic, in New Ideas in Optimization, D. Corne, M. Dorigo and F. Glover (Eds.), McGraw-Hill, London, UK, 11–32, 1999.Google Scholar
  12. 12.
    E. Feinerman and M.S. Falkovitz, Optimal Scheduling of Nitrogen Fertilization and Irrigation, Water Resources Management 11(2), 101–117, 1997.CrossRefGoogle Scholar
  13. 13.
    R. Fletcher, Practical Methods of Optimization, Wiley, New York, Second Edition, 1987.zbMATHGoogle Scholar
  14. 14.
    C.A. Floudas, J.L. Klepeis, and P.M. Pardalos, Global Optimization Approaches in Protein Folding and Peptide Docking, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 47, 141–172, M. Farach-Colton, F. S. Roberts, M. Vingron, and M. Waterman, editors. American Mathematical Society, Providence, RI.Google Scholar
  15. 15.
    Z.W. Geem, J.H. Kim, and G.V. Loganathan, A New Heuristic Optimization Algorithm: Harmony Search, SIMULATIONS 76(2), 60–68, 2001.CrossRefGoogle Scholar
  16. 16.
    F. Glover and F. Laguna, Tabu Search, Kluwer Academic Publishers, Dordrecht, 1997.Google Scholar
  17. 17.
    D.E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley, Reading, MA, 1989.zbMATHGoogle Scholar
  18. 18.
    C.G. Han, P.M. Pardalos, and Y. Ye, Computational Aspects of an Interior Point Algorithm for Quadratic Programming Problems with Box Constraints, Large-Scale Numerical Optimization, T. Coleman and Y. Li (Eds.), SIAM, Philadelphia, 1990.Google Scholar
  19. 19.
    T.X. Hoang, A. Trovato, F. Seno, J.R. Banavar, and A. Maritan, Geometry and Simmetry Presculpt the Free-Energy Landscape of Proteins, Proceedings of the National Academy of Sciences USA 101: 7960–7964, 2004.Google Scholar
  20. 20.
    A.V.M. Ines, K. Honda, A.D. Gupta, P. Droogers, and R.S. Clemente, Combining Remote Sensing-Simulation Modeling and Genetic Algorithm Optimization to Explore Water Management Options in Irrigated Agriculture, Agricultural Water Management 83, 221–232, 2006.CrossRefGoogle Scholar
  21. 21.
    D.F. Jones, S.K. Mirrazavi, and M. Tamiz, Multi-objective Meta-Heuristics: An Overview of the Current State-of-the-Art, European Journal of Operational Research 137, 1–9, 2002.zbMATHCrossRefGoogle Scholar
  22. 22.
    J. Kennedy and R. Eberhart, Particle Swarm Optimization, Proceedings IEEE International Conference on Neural Networks 4, Perth, WA, Australia, 1942–1948, 1995.Google Scholar
  23. 23.
    S. Kirkpatrick, C.D. Gelatt Jr., and M.P. Vecchi, Optimization by Simulated Annealing, Science 220(4598), 671–680, 1983.MathSciNetCrossRefGoogle Scholar
  24. 24.
    K.S. Lee, Z. Geem, S.-H. Lee, and K.-W. Bae, The Harmony Search Heuristic Algorithm for Discrete Structural Optimization, Engineering Optimization 37(7), 663–684, 2005.MathSciNetCrossRefGoogle Scholar
  25. 25.
    J.E. Lennard-Jones, Cohesion, Proceedings of the Physical Society 43, 461–482, 1931.Google Scholar
  26. 26.
    L. Lhotska, M. Macas, and M. Bursa, PSO and ACO, in Optimization Problems, E. Corchado et al. (Eds.), Intelligent Data Engineering and Automated Learning 2006, Lecture Notes in Computer Science 4224, 1390–1398, 2006.Google Scholar
  27. 27.
    M. Mahdavi, M. Fesanghary, and E. Damangir, An Improved Harmony Search Algorithm for Solving Optimization Problems, Applied Mathematics and Computation 188(22), 1567–1579, 2007.MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    S.P. Mendes, J.A.G. Pulido, M.A.V. Rodriguez, M.D.J. Simon, and J.M.S. Perez, A Differential Evolution Based Algorithm to Optimize the Radio Network Design Problem, E-SCIENCE ’06: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing, 2006.Google Scholar
  29. 29.
    N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller, Equation of State Calculations by Fast Computing Machines, Journal of Chemical Physics 21(6): 1087–1092, 1953.CrossRefGoogle Scholar
  30. 30.
    P.M. Morse, Diatomic Molecules According to the Wave Mechanics. II. Vibrational Levels, Physical Review 34, 57–64, 1929.CrossRefGoogle Scholar
  31. 31.
    A. Mucherino and O. Seref, Monkey Search: A Novel Meta-Heuristic Search for Global Optimization, AIP Conference Proceedings 953, Data Mining, System Analysis and Optimization in Biomedicine, 162–173, 2007.Google Scholar
  32. 32.
    A. Mucherino, O. Seref, and P.M. Pardalos, Simulating Protein Conformations: the Tube Model, working paper.Google Scholar
  33. 33.
    J.A. Northby, Structure and Binding of Lennard-Jones clusters: 13 ≤ N ≤ 147, Journal of Chemical Physics 87(10), 6166–6177, 1987.CrossRefGoogle Scholar
  34. 34.
    P.M. Pardalos and H.E. Romeijn (eds.), Handbook of Global Optimization, Vol. 2, Kluwer Academic, Norwell, MA, 2002.zbMATHGoogle Scholar
  35. 35.
    Protein Data Bank:
  36. 36.
    B. Raoult, J. Farges, M.F. De Feraudy, and G. Torchet, Comparison between Icosahedral, Decahedral and Crystalline Lennard-Jones Models Containing 500 to 6000 Atoms, Philosophical Magazine B60, 881–906, 1989.Google Scholar
  37. 37.
    J. Robinson and Y. Rahmat-Samii, Particle Swarm Optimization in Electromagnetics, IEEE Transations on Antennas and Propagation 52(2), 397–407, 2004.MathSciNetCrossRefGoogle Scholar
  38. 38.
    C.T. Scott and M. Kohl, A Method of Comparing Sampling Designs Alternatives for Extensive Inventories, Mitteilungen der Eidgenossischen Forschungsanstalt fur Wald. Schnee and Landschaft 68(1), 3–62, 1993.Google Scholar
  39. 39.
    O. Seref, A. Mucherino, and P.M. Pardalos, Monkey Search: A Novel Meta-Heuristic Method, working paper.Google Scholar
  40. 40.
    A. Shmygelska and H.H. Hoos, An Ant Colony Optimisation Algorithm for the 2D and 3D Hydrophobic Polar Protein Folding Problem, BMC Bioinformatics 6, 30, 2005.CrossRefGoogle Scholar
  41. 41.
    R. Storn and K. Price, Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization 11(4), 341–359, 1997.MathSciNetzbMATHCrossRefGoogle Scholar
  42. 42.
    Y. Xiang, H. Jiang, W. Cai, and X. Shao, An Efficient Method Based on Lattice Construction and the Genetic Algorithm for Optimization of Large Lennard-Jones Clusters, J. Physical Chemistry 108(16), 3586– 3592, 2004.Google Scholar
  43. 43.
    X. Zhang, and T. Li, Improved Particle Swarm Optimization Algorithm for 2D Protein Folding Prediction, ICBBE 2007: The 1st International Conference on Bioinformatics and Biomedical Engineering, 53–56, 2007.Google Scholar
  44. 44.
    T. Zhou, W.-J. Bai, L. Cheng, and B.-H. Wang, Continuous Extremal Optimization for Lennard Jones Clusters, Physical Review E72, 016702, 1–5, 2005.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Center for Applied OptimizationUniversity of FloridaGainesvilleUSA

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