Parallel computing in global optimization

Handbook of Parallel Computing and Statistics

Volumn , Issue , 2005, Pages 225-258

  D’Apuzzo, Marco ^a   Marino, Marina ^b   Migdalas, Athanasios ^c   Pardalos, Panos M ^d   Toraldo, Gerardo ^b  

^a SECOND UNIVERSITY OF NAPLES   (Italy)

^b UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^c TECHNICAL UNIVERSITY OF CRETE   (Greece)

^d University of Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85009491395     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (184)

1. Törn, A. and Zilinskas, A. (1989). Global Optimization, Lecture Notes in Computer Science 350. Berlin: Springer-Verlag.
2. Pardalos, P.M., Phillips, A.T., and Rosen, J.B. (1992). Topics in Parallel Computing in Mathematical Programming. New York: Science Press.
3. Pardalos, P.M., Xue, G., and Shalloway, D. (1994). Optimization methods for computing global minima of nonconvex potential energy functions. Journal of Global Optimization 4: 117-133.
4. Xue, G. (1994). Molecular conformation on the CM-5 by parallel two-level simulated annealing. Journal of Global Optimization 4: 187-208.
5. Gu, J. (1995). Parallel algorithms for satisfiability (SAT) problem. In Pardalos, P.M., Resende, M.G.C., and Ramakrishnan, K.G., Eds., Parallel Processing of Discrete Optimization Problems. DIMACS Series in Discrete Mathematics and Theoretical Computer Sciences 22, New York: American Mathematical Society.
6. Horst, R. and Pardalos, P.M., Eds. (1995). Handbook of Global Optimization. Dordrecht: Kluwer Academic Publishers.
7. Horst, R., Pardalos, P.M., and Thoai, N.V. (1995). Introduction to Global Optimization. Dordrecht: Kluwer Academic Publishers.
8. Horst, R. and Tuy, H. (1996). Global Optimization-Deterministic Approaches, 3rd ed. Berlin: Springer-Verlag.
9. Holmqvist, K., Migdalas, A., and Pardalos, P.M. (1997). Parallel continuous non-convex optimization. In Migdalas, A., Pardalos, P.M., and Staroy, S., Eds., Parallel Computing in Optimization. Dordrecht: Kluwer Academic Publishers.
10. Abello, J., Butenko, S., Pardalos, P.M., and Resende, M.G.C. (2001). Finding independent sets in a graph using continuous multivariable polynomial formulations. Journal of Global Optimization 21: 111-137.
11. Zhigljavsky, A.A. (1991). Theory of Global Random Search. Dordrecht: Kluwer Academic Publishers.
12. Pardalos, P.M. and Schnitger, G. (1998). Checking local optimality in constrained quadratic programming is NP-hard. Operations Research Letters 7: 33-35.
13. Vavasis, S.A. (1995). Complexity issues in global optimization: a survey. In Horst, R., and Pardalos, P.M., Eds., Handbook of Global Optimization. Dordrecht: Kluwer Academic Publishers, pp. 27-41.
14. Floudas, C.A. and Pardalos, P.M., Eds. (1992). Recent Advances in Global Optimization. Princeton, NJ: Princeton University Press.
15. Floudas, C.A. and Pardalos, P.M., Eds. (1996). State of the Art in Global Optimization. Dordrecht: Kluwer Academic Publishers.
16. Grossmann, I.E., Ed. (1996). Global Optimization in Engineering Design. Dordrecht: Kluwer Academic Publishers.
17. Migdalas, A., Pardalos, P.M., and Storoy, S., Eds. (1997). Parallel Computing in Optimization. Dordrecht: Kluwer Academic Publishers.
18. Migdalas, A., Pardalos, P.M., and Varbrand, P., Eds. (2001). From Local to Global Optimization. Dordrecht: Kluwer Academic Publishers.
19. Pardalos, P.M., Migdalas, A., and Burkard, R.E. (2002). Discrete and Global Optimization. Singapore: World Scientific.
20. Pardalos, P.M. and Rosen, J.B. (1987). Global Optimization: Algorithms and Applications, Lecture Notes in Computer Science 268. Berlin: Springer-Verlag.
21. Pinter, J.D. (1996). Global Optimization in Action. Dordrecht: Kluwer Academic Publishers.
22. Konno, H., Thach, P.T., and Tuy, H. (1997). Optimization on Low Rank Nonconvex Structures. Dordrecht: Kluwer Academic Publishers.
23. Tuy, H. (1998). Convex Analysis and Global Optimization. Dordrecht: Kluwer Academic Publishers.
24. Androulakis, I.P., Maranas, C.D., and Floudas, C.A. (1995). a BB: a global optimization method for general constrained nonconvex problems. Journal of Global Optimization 7: 337-363.
25. Adjiman, C.S., Androulakis, I.P., and Floudas, C.A. (1996). A global optimization method, a BB, for process design. Computers and chemical Engineering Suppl. 20: S419-S424.
26. Tawarmalini, M. and Sahinidis, N.V. (1999). Global Optimization of Mixed Integer Nonlinear Programs: A Theoretical and Computational Study. Technical report, Department of Chemical Engineering, University of Illinois, Urbana-Champaign, IL.
27. Bomze, I.M. (2002). Branch-and-bound approaches to standard quadratic optimization problems. Journal of Global Optimization 22: 17-37.
28. http://archimedes.scs.uiuc.edu/baron/baron.html
29. Ratschek, H. and Rokne, J.G. (1988). New Computer Methods for Global Optimization. Chichester: Ellis Horwood.
30. Neumaier, A. (1990). Interval Methods for Systems of Equations. Cambridge, MA: Cambridge University Press.
31. Eriksson, J. (1991). Parallel Global Optimization Using Interval Analysis. Research report UMINF-91.17, Department of Computing Science, Institute of Information Processing, University of Umea, Ume.
32. Hansen, E.R. (1992). Global Optimization Using Interval Analysis. New York: Marcel Dekker.
33. Knüppel, O. (1993). BIAS - Basic Interval Arithmetic Subroutines. Berichte des Forschungsschwerpunktes Informations- und Kommunikationstechnik, Bericht 93.3, Technische Universitat, Hamburg, Germany.
34. Kniippel, O. (1993). PROFIL - Programmer’s Runtime Optimized Fast Interval Library. Berichte des Forschungsschwerpunktes Informations- und Kommunikationstechnik, Bericht 93.4, Technische Universitat, Hamburg, Germany.
35. Kniippel, O. (1993). PROFIL/BIAS Extensions. Berichte des Forschungsschwerpunktes Informationsund Kommunikationstechnik, Bericht 93.5, Technische Universitat, Hamburg, Germany.
36. Knhppel, O. (1993). A Multiple Precision Arithmetic for PROFIL. Berichte des Forschungsschwerpunktes Informations- und Kommunikationstechnik, Bericht 93.6, Technische Universitat, Hamburg, Germany.
37. Eriksson, J. and Lindström, P. (1995). A parallel interval method implementation for global optimization using dynamic load balancing. Reliable Computing 1: 77-91.
38. Holmqvist, K. and Migdalas, A. (1996). A C++ class library for interval arithmetic in global optimization. In Floudas, C.A. and Pardalos, P.M., Eds., State of the Art in Global Optimization. Dordrecht: Kluwer Academic Publishers.
39. Kearfott, R.B. (1996). Rigorous Global Search: Continuous Problems. Dordrecht: Kluwer Academic Publishers.
40. Kearfott, R.B. and Kreinovich, V., Eds., Applications of Interval Computations. Dordrecht: Kluwer Academic Publishers.
41. Neumaier, A. (2002). Grand Challenges and Scientific Standards in Interval Analysis. Technical note, Institut für Mathematik, Universitat Wien, Austria.
42. Leclerc, A.P. (1992). Efficient and Reliable Global Optimization. Ph.D. thesis, Graduate School, Ohio State University.
43. Mockus, J. (1989). Bayesian Approach to Global Optimization. Dordrecht: Kluwer Academic Publishers.
44. Dixon, L.C.W. and Jha, M. (1993). Parallel algorithms for global optimization. Journal of Optimization Theory and Application 79: 385-395.
45. He, L. and Polak, E. (1993). Multistart method with estimation scheme for global satisfycing problems. Journal of Global Optimization 3: 139-156.
46. Holmqvist, K., Migdalas, A., and Pardalos, P.M. (1997). Parallelized heuristics for combinatorial search. In Migdalas, A., Pardalos, P.M., Staroy, S., Eds., Parallel Computing in Optimization. Dordrecht: Kluwer Academic Publishers.
47. Mockus, J., Eddy, W., Mockus, A., Mockus, L., and Reklaitis, G. (1997). Bayesian Heuristic Approach to Discrete and Global Optimization. Dordrecht: Kluwer Academic Publishers.
48. Velazquez, L., Phillips, G.N., Jr., Tapia, R.A., and Zhang, Y. (1999). Selective Search for Global Optimization of Zero or Small Residual Least-Squares Problems: A Numerical Study. Technical report, September 1999, Revised May 2000, Francis Bitter Magnet Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
49. de Angelis, P.L., Bomze, I.M., and Toraldo, G. (2004). Ellipsoidal approach to box-constrained quadratic problems. Journal of Global Optimization 28(1): 1-15
50. Boender, C.G., Rinnoy Kan, A.H.G., Stougie, L., and Timmer, G.T. (1982). A stochastic method for global optimization. Mathematical Programming 22: 125-140.
51. Rinnooy Kan, A.H.G., and Timmer, G.T. (1987). Stochastic global optimization methods. Part I: Clustering methods; Part II: Multi level methods. Mathematical Programming 39: 27-78.
52. Bertocchi, M. (1990). A parallel algorithm for global optimization. Optimization 21: 379-386.
53. Byrd, R.H., Dert, C.L., Rinnooy Kan, A.H.G., and Schnabel, R.B. (1990). Concurrent stochastic methods for global optimization. Mathematical Programming 46: 1-29.
54. Betro, B. (1991). Bayesian methods in global optimization. Journal of Global Optimization 1: 1-14.
55. Schoen, F. (1991). Stochastic techniques for global optimization: a survey of recent advances. Journal of Global Optimization 1: 207-228.
56. Smith, S.L. and Schnabel, R.B. (1992). Dynamic Scheduling Strategies for an Adaptive, Asynchronous Parallel Global Optimization Algorithms. Research report CU-CS-652-93, Department of Computer Science, University of Colorado at Boulder, Campus Box 430, Boulder, Colorado.
57. Byrd, R.H., Eskow, E., Schnabel, R.B., and Smith, S.L. (1993). Parallel global optimization: numerical methods, dynamic scheduling methods, and application to molecular configuration. In Ford, B. and Fincham, A., Eds., Parallel Computation. Oxford: Oxford University Press, pp. 187-207.
58. Byrd, R.H., Derby, T., Eskow, E., Oldenkamp, K.P.B., and Schnabel, R.B. (1994). A new stochas-tic/perturbation method for large-scale global optimization and its application to water cluster problems. In Hager, W., Hearn, D., and Pardalos, P.M., Eds., Large-Scale Optimization: State of the Art. Dordrecht: Kluwer Academic Publishers, pp. 71-84.
59. Ritter, K. and Schaffler, S. (1994). A stochastic method for constraint global optimization. SIAM Journal on Optimization 4: 894-904.
60. Schoen, F. (1994). On an new stochastic global optimization algorithm based on censored observations. Journal of Global Optimization 4: 17-35.
61. Boender, C.G.E. and Romeijn, H.E. (1995). Stochastic methods. In Horst, R. and Pardalos, P.M., Eds., Handbook of Global Optimization. Dordrecht: Kluwer Academic Publishers, pp. 829-869.
62. Chamberlain, R.D., Edelman, M.N., Franklin, M.A., and Witte, E.E. (1988). Simulated annealing on a multiprocessor. In Proceedings of 1988 IEEE International Conference on Computer Design, pp. 540-544.
63. Aarts, E. and Korst, J. (1990). Simulated Annealing and Boltzmann Machines. Chichester: John Wiley &Sons.
64. Dekker, A. and Aarts, E. (1991). Global optimization and simulated annealing. Mathematical Programming 50: 367-393.
65. Azencott, R. (1992). Simulated Annealing - Parallelization Techniques. New York: John Wiley &Sons.
66. Desai, R. and Patil, R. (1996). SALO: combining simulated annealing and local optimization for efficient global optimization. In Proceedings of the Ninth Florida AI Research Symposium (FLAIRS-96). Key West, FL, pp. 233-237.
67. Crainic, T.G. and Toulouse, M. (1997). Parallel Metaheuristics. Technical report, Centre de recherche sur les transports, Universite de Montreal, Montreal, Quebec, Canada.
68. Tomassini, M. (1993). The parallel genetic cellular automata: application to global function optimization. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms. Wien: Springer-Verlag.
69. Michalewicz, Z. (1994). Genetic Algorithms + Data Structures = Evolution Programs, Second, Extended Edition. Berlin: Springer-Verlag.
70. Alander, J.T. (1995). Proceedings of the First Nordic Workshop on Genetic Algorithms and their Applications. Vaasa: Vaasan yliopisto.
71. Pardalos, P.M., Pitsoulis, L., Mavridou, T., and Resende, M.G.C. (1995). Parallel search for combinatorial optimization: genetic algorithms, simulated annealing, tabu search and GRASP. In Ferreira, A. and Rolim, J., Eds., Proceedings of Workshop on Parallel Algorithms for Irregularly Structured Problems, Lecture notes in Computer Science 980. Berlin: Springer-Verlag, pp. 317-331.
72. Osyczka, A. (2002). Evolutionary Algorithms for Single and Multicriteria Design Optimization. Heidelberg: Physica-Verlag.
73. Pardalos, P.M., Pitsoulis, L. and Resende, M.G.C. (1995). A parallel GRASP implementation for the quadratic assignment problem. In Ferreira, A. and Rolim, J., Eds., Solving Irregular Problems in Parallel - State of the Art. Dordrecht: Kluwer Academic Publishers.
74. Glover, F. (1989). Tabu search - Part I. ORSA Journal on Computing 1: 190-206.
75. Glover, F. (1990). Tabu search - Part II. ORSA Journal on Computing 2: 4-31.
76. Glover, F., Taillard, E., and De Werra, D. (1993). A user’s guide to tabu search. In Glover, F., Laguna, M., Taillard, E., and De Werra, D., Eds., Tabu Search. Annals of Operations Research 41. J.C. Baltzer AG, Basel, Switzerland, pp. 2-28.
77. Battiti, R. and Tecchiolli, G. (1994). The Continuous Reactive Tabu Search: Blending Combinatorial Optimization and Stochastic Search for Global Optimization. Preprint UTM 432, Dipartimento Di Matematica Universita di Trento, Trento, Italy.
78. Cvijovic, D., and Klinowski, J. (1995). Tabu search: an approach to the multiple minima problem. Science 267: 664-666.
79. Crainic, T.G., Toulouse, M., and Gendreau, M. (1997). Towards a taxonomy of parallel tabu search algorithms. INFORMS Journal on Computing 9: 61-72.
80. http://plato.la.asu.edu/gom.html
81. http://www.mat.univie.ac.at/~neum/glopt/software_g.html
82. Pinter, J.D. (1996). Continuous global optimization software: a brief review. Optima 52: 1-8.
83. Trienekens, H.W.J.M. (1990). Parallel Branch and Bound Algorithms. Ph.D. thesis, Department of Computer Science, Erasmus University, Rotterdam, The Netherlands.
84. Kronsjo, L., and Shumsheruddin, D., Eds., Advances in Parallel Algorithms. London: Blackwell Scientific Publications.
85. de Bruin, A., Kindervater, G.A.P., and Trienekens, H.W.J.M. (1995). Asynchronous Parallel Branch and Bound and Anomalies. Lyon, France, September 4-6.
86. Clausen, J. (1997). Parallel branch and bound - principles and personal experiences. In Migdalas, A., Pardalos, P.M., and Varbrand, P., Eds., From Local to Global Optimization. Dordrecht: Kluwer Academic Publishers, pp. 239-267.
87. http://liinwww.ira.uka.de/bibliography/Parallel/par.branch.and.bound.html
88. Tschoke S., and Polzer, T. (1999). Portable Parallel Branch-and-Bound Library: PPBB-Lib, User Manual, Library Version 2.0. Department of Computer Science, University of Paderborn, D-33095 Paderborn, Germany.
89. http://www.uni-paderborn.de/~ppbb-lib
90. Gendron, B. and Crainic, T.G. (1994). Parallel branch-and-bound algorithms: survey and synthesis. Operations Research 42: 1042-1066.
91. Henriksen, T. and Madsen, T. (1992). Use of a Depth-First Strategy in Parallel Global Optimization. Research report 92-10, Technical University of Denmark, Lyngby, Denmark.
92. Moore, R.E., Hansen, E., and Leclerc, A. (1992). Rigorous methods for global optimization. In Floudas, C.A. and Pardalos, P.M., Eds., Recent Advances in Global Optimization. Princeton, JN: Princeton University Press.
93. Leclerc, A.P. (1993). Parallel interval global optimization and its implementation in C++. Interval Computations 3: 148-163.
94. Berner, S. (1996). Parallel methods for verified global optimization - practice and theory. Journal of Global Optimization 9: 1-22.
95. Gau, C.Y. and Stadtherr, M.A. (1999). A systematic analysis of dynamic load balancing strategies for parallel interval analysis. In AIChE Annual Meeting. Dallas, TX, October 31-November 5.
96. Gau, C.Y. and Stadtherr, M.A. (2000). Trends in parallel computing for process engineering. In AspenWorld2000. Orlando, FL, February 6-11.
97. Gau, C.Y. and Stadtherr, M.A. (2000). Parallel branch-and-bound for chemical engineering applications: load balancing and scheduling issues. In VECPAR2000. Porto, Portugal, June 21-23.
98. http://happy.dt.uh.edu/~ParaGlobSol.html
99. Phillips, A.T. and Rosen, J.B. (1989). Guaranteed e-approximate solution for indefinite quadratic global minimization. Naval Research Logistics Quarterly 37: 499-514.
100. Pardalos, P.M. and Rosen, J.B., Eds. (1990). Computational Methods in Global Optimization. Annals of Operations Research 25.
101. Phillips, A.T. and Rosen, J.B. (1990). A parallel algorithm for partially separable non-convex global minimization. In Pardalos, P.M., and Rosen, J.B., Eds., Computational Methods in Global Optimization25. J.C. Baltzer AG, Basel, Switzerland.
102. Boffey, T.B. and Saeidi, P. (1996). A parallel branch-and-bound method for a network design problem. Belgian Journal of Operations Research 32: 69-83.
103. Goux, J.P. and Leyffer, S. (2001). Solving large MINLPs on computational grids. University of Dundee Report NA200.
104. Anstreicher, K., Brixius, N., Linderoth, J., and Goux, J.P. (2002). Solving large quadratic assignment problems on computational grids. Mathematical Programming Series B 91: 563-588.
105. Goux, J.P., Linderoth, J., and Yoder, M. (2002). Metacomputing and the master/worker paradigm. Preprint MCS/ANL P792/0200, Computer Science Division, Argonne National Laboratory, USA.
106. Livny, M., Basney, J., Raman, R., and Tannebaum, T. (1997). Mechanisms for High-throughput Computing. Speedup 11.
107. Goux, J.P., Kulkarni, L., Linderoth, J., and Yoder, M. (2000). An enabling framework for master-worker applications on computational grid. Cluster Computing 43-50.
108. Pardalos, P.M., Resende, M.G.C., and Ramakrishnan, K.G., Eds. (1995). Parallel Processing of Discrete Optimization Problems. DIMACS Series in Discrete Mathematics and Theoretical Computer Science: 22. New York American Mathematical Society.
109. Zhang, Y., Tapia, R.A., and Velazquez, L. (1999). On Convergence of Minimization Methods: Attraction, Repulsion and Selection. Technical report TR 99-12, Department of Computational and Applied Mathematics, Rice University, Housto, TX.
110. Goffe, B. (1994). Global optimization of statistical functions with simulated annealing. Journal of Econometrics 30: 65-100.
111. Castellanos, L. and Gomez, S. (1998). Tunnel: A Fortran Subroutine for Global Optimization with Bounds on the Variables using the Tunneling Method. Technical report, IIMAS-UNAM.
112. Levy, A.V. and Gomez, S. (1985). The tunneling method applied to global optimization. In Boggs, P.T., Byrd, R.H., and Schnabel, R.B., Eds., Numerical Optimization. Philadelphia: SIAM, pp. 213-244.
113. Levy, A.V. and Montalvo, A. (1985). The tunneling algorithm for the global minimisation of functions. SIAM Journal of Scientific and Statistical Computing 6(1):15-29.
114. Gomez, S., Del Castillo, N., Castellanos, L., and Solano, J. (2003). The parallel tunneling method. Journal of Parallel Computing 9: 523-533.
115. Gomez, S. and Romero, D. (1994). Two global methods for molecular geometry optimization. Progress in Mathematics. Vol. 121. Boston: Birkhauser, pp. 503-509.
116. Gomez, S., Gosselin, O., and Barker, J. (2001). Gradient-based history-matching with a global optimization method. Society of Petroleum Engineering Journal 200-208.
117. Nichita, D.V., Gomez, S., and Luna, E. (2002). Multiphase equilibria calculation by direct minimization of Gibbs free energy with a global optimization method. In AICHE Spring Meeting Proceedings. Computers and Chemical Engineering.
118. Nichita, D.V., Gomez, S., and Luna, E. (2002). Phase stability analysis with cubic equations of state using a global optimization method. Fluid Phase Equilibria 194-197: 411-437.
119. Gomez, S., Castellanos, L., and Del Castillo, N. (2003). Users Manuals for the Sequential Tunneling Methods, TunTGNS and TunLBFS. Serie Manuales, IIMAS-UNAM, No. 12.
120. Gomez, S., Castellanos, L., and Del Castillo, N. (2003). Users Manuals for the Parallel Tunneling Methods, TunTGNP and TunLBFP. Serie Manuales, IIMAS-UNAM, No. 13.
121. Minoux, M. (1976). Multiflots de Cout Minimal Avec Fonctions de Cout Concaves. Annals of Telecommunication 31(3-4): 77-92.
122. Yaged, B. Jr. (1971). Minimum cost routing for static network models. Networks 1: 139-172.
123. Li, Z., Pardalos, P.M., and Levine, S.H. (1992). Space-covering approach and modified Frank-Wolfe algorithm for optimal nuclear reactor reload design. In Floudas, C.A., and Pardalos, P.M., Eds., Recent Advances in Global Optimization. Princeton, NJ: Princeton University Press.
124. Ten Eikelder, H.M.M., Verhoeven, M.G.A., Vossen, T.W.M., and Aarts, E.H.L. (1996). A probabilistic analysis of local search. In Osman, I.H., and Kelly, J.P., Eds., Meta-Heuristics: Theory and Applications. Boston: Kluwer Academic Publishers, pp. 605-618.
125. Evtushenko, Y.G. (1985). Numerical Optimization Techniques. New York. Optimization Software.
126. Kim, D. and Pardalos, P.M. (2000). Dynamic slope scaling and trust interval techniques for solving concave piecewise linear network flow problems. Networks 35: 216-222.
127. Phillips, A.T. and Rosen, J.B. (1988). A parallel algorithm for the linear complementarity problem. Annals of Operations Research 14: 77-104.
128. Mockus, J. (1994). Application of Bayesian approach to numerical methods of global and stochastic optimization. Journal of Global Optimization 4: 347-365.
129. Mockus, J. (2002). Bayesian heuristic approach to global optimization and examples. Journal of Global Optimization 22: 191-203.
130. Phillips, A.T., Rosen, J.B., and van Vliet, M. (1992). A parallel stochastic method for solving linearly constrained concave global minimization problems. Journal of Global Optimization 2: 243-258.
131. Byrd, R.H., Derby, T., Eskow, E., Oldenkamp, K.P.B., and Schnabel, R.B. (1994). A new stochas-tic/perturbation method for large-scale global optimization and its application to water cluster problems. In Hager, W., Hearn, D., and Pardalos, P.M., Eds., Large-scale Optimization: State of the Art. Dordrecht: Kluwer Academic Publishers, pp. 71-84.
132. Byrd, R.H., Eskow, E., van der Hoek, A., Schnabel, R.B., and Oldenkamp, K.P.B. (1995). A parallel global optimization method for solving molecular cluster and polymer conformation problems. In Bailey, D.H. et al., Eds., Proceedings of the Seventh SIAM Conference on Parallel Processing for Scientific Computing, Philadelphia: SIAM, pp. 72-77.
133. Byrd, R.H., Eskow, E., van der Hoek, A., Schnabel, R.B., Shao, C.S., and Zou, Z. (1995). Global optimization methods for protein folding problems. In Pardalos, P.M., Shalloway, D., and Xue, G., Eds., Proceedings of the DIMACS Workshop on Global Minimization ofNonconvex Energy Functions: Molecular Conformation and Protein Folding. New York: American Mathematical Society.
134. Byrd, R.H., Eskow, E., and Schnabel, R.B. (1999). A Large-Scale Stochastic-Perturbation Global Optimization Method for Molecular Cluster Problems. Research report, Department of Computer Science, University of Colorado Boulder, CO.
135. Smith, S.L. and Schnabel, R.B. (1992). Centralized and distributed dynamic scheduling for adaptive parallel algorithms. In Mehrotra, P., Saltz, J., and Voigt, R., Eds., Unstructured Scientific Computation on Scalable Multiprocessors. Cambridge, MA: MIT Press, pp. 301-321.
136. Smith, S.L., Eskow, E., and Schnabel, R.B. (1990). Adaptive, asynchronous stochastic global optimization for sequential and parallel computation. In Coleman, T. and Li, Y., Eds., Large Scale Numerical Optimization. Philadelphia: SIAM, pp. 207-227.
137. Byrd, R.H., Eskow, E., and Schnabel, R.B. (1992). A New Large-Scale Global Optimization Method and its Application to Lennard-Jones Problems. Technical report CU-CS-630-92, Department of Computer Science, University of Colorado, Boulder, CO.
138. Byrd, R.H., Derby, T., Eskow, E., Oldenkamp, K., Schnabel, R.B., and Triantafillou, C. (1993). Parallel global optimization methods for molecular configuration problems. In Proceedings of the Sixth SIAM Conference of Parallel Processing for Scientific Computation. Philadelphia: SIAM, pp. 165-169.
139. Feo, T.A. and Resende, M.G.C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization 6: 109-133.
140. Festa, P. and Resende, M.G.C. (2001). GRASP: an annoted bibliography. In Hansen, P. and Ribeiro, C.C., Eds., Essays and Surveys on Metaheuristics. Dordrecht: Kluwer Academic Publishers.
141. Resende, M.C.C., Pardalos, P.M., and Eksioglu, S.D. (2001). Parallel metaheuristics for combinatorial optimization. In Correa, R. et al., Eds., Advanced Algorithmic Techniques for Parallel Computation with Applications. Dordrecht: Kluwer Academic Publishers.
142. Holmqvist, K., Migdalas, A., and Pardalos, P.M. (1998). A GRASP algorithm for the single source uncapacitated minimum concave-cost network flow problem. DIMACS Series in Discrete Mathematics and Theoretical Computer Science 40: 131-142.
143. Aiex, R., Pardalos, P.M., Resende, M., and Toraldo, G. (2005). GRASP with path relinking for the three-index assignment problem. INFORMS Journal on Computing, 17(2): 224-247
144. Feo, T.A., Resende, M.G.C., and Smith, S.H. (1994). A greedy randomized adaptive search procedure for maximum independent set. Operations Research 42: 860-878.
145. Murphey, R.A., Pardalos, P.M., and Pitsoulis, L. (1998). A parallel GRASP for the data association multidimensional assignment problem. In Parallel Processing ofDiscrete Problems. IMA Volumes in Mathematics and its Applications 106. Heidelberg: Springer-Verlag, pp. 159-180.
146. Ingber, L. (1993). Simulated annealing: practice versus theory. Journal of Mathematical and Computer Modelling 18: 29-57.
147. Romeijn, H.E. and Smith, R.L. (1994). Simulated annealing for constrained global optimization. Journal ofGlobal Optimization 5: 101-126.
148. Pardalos, P.M., Xue, G., and Panagiotopoulos, P.D. (1995). Parallel algorithms for global optimization. In Ferreira, A. and Pardalos, P.M., Eds., Solving Irregular Problems in Parallel: State of the Art. Berlin: Springer-Verlag.
149. Glover, F., Laguna, M., Taillard, E., De Werra, D., Eds., (1993). Tabu Search. Annals of Operations Research 41.
150. Gergel, V.P., Sergeyev, Ya.D., and Strongin, R.G. (1992). A parallel global optimization method and its implementation on a transputer system. Optimization 26: 261-275.
151. Strongin, R.G. and Sergeyev, Y.D. (1992). Global multidimensional optimization on parallel computer. Parallel Computing 18(11): 1259-1273.
152. Ghannadan, S., Migdalas, A., Tuy, H., and Varbrand, P. (1994). Heuristics based on tabu search and lagrangean relaxation for the concave production-transportation problem. Studies in Regional and Urban Planning 3: 127-140.
153. Ghannadan, S., Migdalas, A., Tuy, H., and Varbrand, P. (1996). Tabu meta-heuristic based on local search for the concave production-transportation Problem. Studies in Location Analysis. Special Issue Edited by C. R. Reeves. 8: 33-47.
154. Crainic, T.G., Toulouse, M., and Gendreau, M. (1995). Synchronous tabu search parallelization strategies for multicommodity location-allocation with balancing requirements. OR Spectrum 17: 113-123.
155. Battiti, R. and Tecchiolli, G. (1992). Parallel biased search for combinatorial optimization: genetic algorithms and tabu. Microprocessors and Microsystems 16: 351-367.
156. Price, W.L. (1978). A controlled random search procedure for global optimization. In Dixon, L.C.W. and Szego, G.P., Eds., Towards Global Optimization2. Amsterdam: North-Holland, pp. 71-84.
157. Price, W.L. (1979). A controlled random search procedure for global optimization. The Computer Journal 20: 367-370.
158. Price, W.L. (1983). Global optimization algorithms for a CAD workstation. Journal of Optimization Theory and Applications 55: 133-146.
159. Ali, M.M. and Storey, C. (1994). Modified controlled random search algorithms. International Journal of Computational Mathematics 53: 229-235.
160. Ali, M.M., Torn, A., and Viitanen, S. (1997). A numerical comparison of some modified controlled random search algorithms. Journal of Global Optimization 11: 377-385.
161. Mohan, C. and Shanker, K. (1988). A numerical study of some modified versions of controlled random search method for global optimization. International Journal ofComputer Mathematics 23: 325-341.
162. Mohan, C. and Shanker, K. (1994). A controlled random search technique for global optimization using quadratic approximation. Asia-Pacific Journal of Operational Research 11: 93-101.
163. Woodhams, F.W.D. and Price, W.L. (1988). Optimizing accelerator for CAD workstation. IEEE Proceedings, Part E 135: 214-221.
164. Garcia, I., Ortigosa, P.M., Casado, L.G., Herman, G.T., Matej, S. (1995). A parallel implementation of the controlled random search algorithm to optimize an algorithm reconstruction from projections. In Third Workshop on Global Optimization. Szeged, Hungary, December, pp. 28-32.
165. Garcia, I. and Herman, G.T. (1996). Global optimization by parallel constrained biased random search. In Floudas, C.A. and Pardalos, P.M., Eds., State of the Art in Global Optimization: Computational Methods and Applications. Dordrecht: Kluwer Academic Publishers, pp. 433-455.
166. Garcia, I., Ortigosa, P.M., Casado, L.G., Herman, G.T., and Matej, S. (1997). Multi-dimensional optimization in image reconstruction from projections. In Bomze, L.M., Csendes, T., Horst, R., and Pardalos, P.M., Eds., Developments in Global Optimization. Dordrecht: Kluwer Academic Publishers, pp. 289-300.
167. McKeown, J.J. (1980). Aspects of parallel computations in numerical optimization. In Arcetti, F. and Cugiarni, M., Eds., Numerical Techniques for Stochastic Systems, pp. 297-327.
168. Schwefel, H.P. (1981). Numerical Optimization for Computer Models. Chichester: John Wiley &Sons.
169. Back, T., Hoffmeister, F., and Schwefel, H.P. (1991). A survey of evolution strategies. In Proceedings of the Fourth International Conference on Genetic Algorithms. Los Altos, CA: Morgan Kaufmann.
170. Schwefel, H.P. and Manner, R., Eds. (1991). Parallel Problem Solving from Nature. Lecture notes in Computer Science 496. Berlin: Springer-Verlag.
171. Schaffer, J., Ed. (1989). Proceedings of the Third International Conference on Genetic Algorithms. Los Altos, CA: Morgan Kaufmann Publishers.
172. Fogerty, T.C. and Haung, R. (1991). Implementing the genetic algorithm on transputer based parallel systems. In Schwefel, H.P. and Manner, R., Eds., Parallel Problem Solving from Nature. Lecture notes in Computer Science 496. Berlin: Springer-Verlag, pp. 145-149.
173. Abramson, D. and Abela, J. (1992). A parallel genetic algorithm for solving the school timetabling problem. In Gupta, G. and Keen, C. Eds., 15th Australian Computer Science Conference. Department of Computer Science, University of Tasmania, Australia, pp. 1-11.
174. Cui, J. and Fogarty, T.C. (1992). Optimization by using a parallel genetic algorithm on a transputer computing surface. In Valero, M. et al., Eds., Parallel Computing and Transputer Applications. Amsterdam: IOS Press, pp. 246-254.
175. Abramson, D., Millis, G., and Perkins, S. (1993). Parallelization of a genetic algorithm for the computation of efficient train schedules. In Arnold, D. et al., Eds., Proceedings of the 1993 Parallel Computing and Transputers Conference. IOS Press, pp. 139-149.
176. Hauser, R. and Manner, R. (1994). Implementation of standard genetic algorithms on MIMD machines. In Parallel Problem Solving from Nature III. Lecture notes in Computer Science 866. Berlin: SpringerVerlag, pp. 504-514.
177. Cohoon, J.P., Hedge, S.U., Martin, W.N., and Richards, D. (1987). Punctuated equilibria: a parallel genetic algorithm. In Grefenstette, J.J., Ed., Proceedings of the Second International Conference on Genetic Algorithms. Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 148-154.
178. Tanese, R. (1987). Parallel genetic algorithm for a hypercube. In Grefensette, J.J., Ed., Proceedings of the Second International Conference on Genetic Algorithms. Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 177-183.
179. Whitley, D. (1989). GENITOR II: A distributed genetic algorithm. Journal of Experimental and Theoretical Artificial Intelligence 2: 189-214.
180. Starkweather, T., Whitney, D. and Mathias, K. (1991). Optimization using distributed genetic algorithms. In Schwefel, H.P. and Manner, R., Eds., Parallel Problem Solving from Nature. Lecture notes in Computer Science 496. Berlin: Springer-Verlag, pp. 176-185.
181. Belding, T. (1995). The distributed genetic algorithms revised. In Eshelman, L.J., Eds., Proceedings of the Sixth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann, pp. 114-121.
182. Miihlenbein, H. (1989). Parallel genetic algorithms, population genetics and combinatorial optimization. In Schaffer, J., Ed., Proceedings of the Third International Conference on Genetic Algorithms. Los Altos, CA: Morgan Kaufmann Publishers, pp. 416-421.
183. Miihlenbein, H., Schomisch, M., and Born, J. (1991). The parallel genetic algorithm as function optimizer. Parallel Computing 7: 619-632.
184. Schnabel, R.B. (1995). A view of the limitations, opportunities, and challenges in parallel nonlinear optimization. Parallel Computing 21(6): 875-905.