ACM Transactions on Mathematical Software (TOMS)
A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Cycle Decompositions and Simulated Annealing
SIAM Journal on Control and Optimization
An SQP method for general nonlinear programs using only equality constrained subproblems
Mathematical Programming: Series A and B
A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems
Journal of Global Optimization
Simulated Annealing with Asymptotic Convergence for Nonlinear Constrained Global Optimization
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
The Theory of Discrete Lagrange Multipliers for Nonlinear Discrete Optimization
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Global optimization of nonconvex nonlinear programs using parallel branch and bound
Global optimization of nonconvex nonlinear programs using parallel branch and bound
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
A dual sequence simulated annealing algorithm for constrained optimization
MATH'06 Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS
Solving nonlinearly constrained global optimization problem via an auxiliary function method
Journal of Computational and Applied Mathematics
Proceedings of the 2010 ACM Symposium on Applied Computing
Computers and Operations Research
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This paper improves constrained simulated annealing (CSA), a discrete global minimization algorithm with asymptotic convergence to discrete constrained global minima with probability one. The algorithm is based on the necessary and sufficient conditions for discrete constrained local minima in the theory of discrete Lagrange multipliers. We extend CSA to solve nonlinear continuous constrained optimization problems whose variables take continuous values. We evaluate many heuristics, such as dynamic neighborhoods, gradual resolution of nonlinear equality constraints and reannealing, in order to greatly improve the efficiency of solving continuous problems. We report much better solutions than the best-known solutions in the literature on two sets of continuous optimization benchmarks.