1.

Aarts E., Korst J. (1989) Simulated Annealing and Boltzmann Machines. Wiley, New York

Google Scholar2.

Anily S., Federgruen A. (1987) Ergodicity in parametric nonstationary Markov chains: an application to simulated annealing methods. Operations Res. 35(6):867–874

Google Scholar3.

Anily S., Federgruen A. (1987) Simulated annealing methods with general acceptance probabilities. J.Appl. Prob. 24:657–667

CrossRefGoogle Scholar4.

Auslender A., Cominetti R., Maddou M. (1997) Asymptotic analysis for penalty and barrier methods in convex and linear programming. Math. Operations Res. 22:43–62

Google Scholar5.

Back T., Hoffmeister F., Schwefel H.-P. A survey of evolution strategies. In: Proceedings of the 4th Int’l Conference on Genetic Algorithms, pp 2–9. San Diego, CA, (1991)

6.

Bean J.C., Hadj-Alouane, A.B.: A dual genetic algorithm for bounded integer programs. In Technical Report TR 92-53, Department of Industrial and Operations Engineering, The University of Michigan (1992)

7.

Bertsekas D.P., Koksal A.E. (2000) Enhanced optimality conditions and exact penalty functions. Proceedings of Allerton Conference, Allerton, IL

Google Scholar8.

Bongartz I.,Conn A.R.,Gould N., Toint P.L. (1995) CUTE: Constrained and unconstrained testing environment. ACM Trans. Math Softw, 21(1):123–160

CrossRefGoogle Scholar9.

Chen, Y.X.: Solving nonlinear constrained optimization problems through constraint partitioning. Ph.D. thesis, Department of Computer Science, University of Illinois, Urbana, IL (2005)

10.

Corana A., Marchesi M., Martini C., Ridella S. (1987) Minimizing multimodal functions of continuous variables with the simulated annealing algorithm. ACM Trans. Math. Softw. 13(3):262–280

CrossRefGoogle Scholar11.

Evans J.P., Gould F.J., Tolle J.W. (1973) Exact penalty functions in nonlinear programming. Math. Program. 4:72–97

CrossRefGoogle Scholar12.

Fletcher R. (1970) A class of methods for nonlinear programming with termination and convergence properties. In: Abadie J.(eds) Integer and Nonlinear Programming. North-Holland, Amsterdam

Google Scholar13.

Fletcher R. An exact penalty function for nonlinear programming with inequalities. Technical Report 478, Atomic Energy Research Establishment, Harwell (1972)

14.

Fourer R., Gay D.M., Kernighan B.W. AMPL: A Modeling Language for Mathematical Programming. Brooks Cole Publishing Company (2002)

15.

Freidlin M.I., Wentzell A.D. (1984) Random Perturbations of Dynamical Systems. Springer, Berlin

Google Scholar16.

Gill P.E., Murray W., Saunders M. (2002) SNOPT: an SQP algorithm for large-scale constrained optimization. SIAM J. Optim. 12:979–1006

CrossRefGoogle Scholar17.

Homaifar A., Lai S.H-Y., Qi X. (1994) Constrained optimization via genetic algorithms. Simulation 62(4):242–254

Google Scholar18.

Joines J., Houck C.: On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with gas. In: Proceedings of the First IEEE Int’l Conf. on Evolutionary Computation, pp. 579–584. Orlando, FL (1994)

19.

Kirkpatrick S., Gelatt Jr., C.D., Vecchi M.P. (1983). Optimization by simulated annealing. Science. **220**(4598): 671–680

20.

Krishnan, S., Krishnamoorthy, S., Baumgartner, G., Lam, C.C., Ramanujam, J., Sadayappan, P., Choppella, V.: Efficient synthesis of out-of-core algorithms using a nonlinear optimization solver.Technical report, Department of Computer and Information Science, Ohio State University, Columbus, OH (2004)

21.

Kuri, A.: A universal electric genetic algorithm for constrained optimization. In: Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing, pp. 518–522. Aachen, Germany (1998)

22.

Luenberger D.G. (1984) Linear and Nonlinear Programming. Addison-Wesley, Reading, MA

Google Scholar23.

Mitra D., Romeo F., Vincentelli A.S. (1986) Convergence and finite-time behavior of simulated annealing. Adv. Appl. Prob. 18:747–771

CrossRefGoogle Scholar24.

Rardin R.L. (1998) Optimization in Operations Research. Prentice Hall, New York

Google Scholar25.

Trouve, A.: Rough large deviation estimates for the optimal convergence speed exponent of generalized simulated annealing algorithms. Technical report, LMENS-94-8, Ecole Normale , France (1994)

26.

Trouve A. (1996) Cycle decomposition and simulated annealing. SIAM J. Control Optim. 34(3):966–986

CrossRefGoogle Scholar27.

Wah B., Chen Y.X. (2006) Constraint partitioning in penalty formulations for solving temporal planning problems. Artif Intel 170(3):187–231

CrossRefGoogle Scholar28.

Wah, B.W., Chen, Y.X.: Solving large-scale nonlinear programming problems by constraint partitioning. In: Proceedigs of the Principles and Practice of Constraint Programming, LCNS-3709, pp. 697–711. Springer-Verlag, New York (2005)

29.

Wah, B.W., Wang, T.: Simulated annealing with asymptotic convergence for nonlinear constrained global optimization. In: Proceedings of the Principles and Practice of Constraint Programming, pp. 461–475. Springer-Verlag, New York (1999)

30.

Wah, B.W., Wu, Z.: The theory of discrete Lagrange multipliers for nonlinear discrete optimization. In: Proceedings of the Principles and Practice of Constraint Programming, pp. 28–42. Springer-Verlag, New York (1999)

31.

Wang, T.: Global Optimization for Constrained Nonlinear Programming. Ph.D. thesis, Department of Computer Science, University of Illinois, Urbana, IL (2000)

32.

Wu, Z.: The Theory and Applications of Nonlinear Constrained Optimization using Lagrange Multipliers. Ph.D. thesis, Department of Computer Science, University of Illinois, Urbana, IL (2001)

33.

Zangwill W.I. (1967) Nonlinear programming via penalty functions. Manag. Sci. 13:344–358

CrossRefGoogle Scholar