Abstract
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.
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References
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.
D. Baker, A Surprising Simplicity to Protein Folding, Nature 405, 39–42, 2000.
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.
J. Brandao, A Tabu Search Algorithm for the Open Vehicle Routing Problem, European Journal of Operational Research 157(3), 552–564, 2004.
W. Ben-Ameur, Computing the Initial Temperature of Simulated Annealing , Computational Optimization and Applications 29(3), 369–385, 2004.
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.
Cambridge database: http://www-wales.ch.cam.ac.uk/CCD.html.
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.
A.R. Conn and N.I.M. Gould, Trust-Region Methods, SIAM Mathematical Optimization, 2000.
P.G. De Vries, Sampling for Forest Inventory, Springer, Berlin, 1986.
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.
E. Feinerman and M.S. Falkovitz, Optimal Scheduling of Nitrogen Fertilization and Irrigation, Water Resources Management 11(2), 101–117, 1997.
R. Fletcher, Practical Methods of Optimization, Wiley, New York, Second Edition, 1987.
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.
Z.W. Geem, J.H. Kim, and G.V. Loganathan, A New Heuristic Optimization Algorithm: Harmony Search , SIMULATIONS 76(2), 60–68, 2001.
F. Glover and F. Laguna, Tabu Search , Kluwer Academic Publishers, Dordrecht, 1997.
D.E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley, Reading, MA, 1989.
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.
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.
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.
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.
J. Kennedy and R. Eberhart, Particle Swarm Optimization, Proceedings IEEE International Conference on Neural Networks 4, Perth, WA, Australia, 1942–1948, 1995.
S. Kirkpatrick, C.D. Gelatt Jr., and M.P. Vecchi, Optimization by Simulated Annealing , Science 220(4598), 671–680, 1983.
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.
J.E. Lennard-Jones, Cohesion, Proceedings of the Physical Society 43, 461–482, 1931.
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.
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.
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.
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.
P.M. Morse, Diatomic Molecules According to the Wave Mechanics. II. Vibrational Levels, Physical Review 34, 57–64, 1929.
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.
A. Mucherino, O. Seref, and P.M. Pardalos, Simulating Protein Conformations: the Tube Model, working paper.
J.A. Northby, Structure and Binding of Lennard-Jones clusters: 13 ≤ N ≤ 147, Journal of Chemical Physics 87(10), 6166–6177, 1987.
P.M. Pardalos and H.E. Romeijn (eds.), Handbook of Global Optimization, Vol. 2, Kluwer Academic, Norwell, MA, 2002.
Protein Data Bank: http://www.rcsb.org/pdb/.
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.
J. Robinson and Y. Rahmat-Samii, Particle Swarm Optimization in Electromagnetics, IEEE Transations on Antennas and Propagation 52(2), 397–407, 2004.
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.
O. Seref, A. Mucherino, and P.M. Pardalos, Monkey Search : A Novel Meta-Heuristic Method, working paper.
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.
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.
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.
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.
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.
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Mucherino, A., Seref, O. (2009). Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods. In: Advances in Modeling Agricultural Systems. Springer Optimization and Its Applications, vol 25. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-75181-8_19
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