Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Global optimization and simulated annealing
Mathematical Programming: Series A and B
Simulated annealing: an initial application in econometrics
Computer Science in Economics and Management
Tabu search for nonlinear and parametric optimization (with links to genetic algorithms)
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Automatic Determination of an Initial Trust Region in Nonlinear Programming
SIAM Journal on Scientific Computing
Computers and Operations Research
Journal of Optimization Theory and Applications
Simulated annealing algorithms for continuous global optimization: convergence conditions
Journal of Optimization Theory and Applications
Trust-region methods
Global Optimization with Polynomials and the Problem of Moments
SIAM Journal on Optimization
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
GloptiPoly: Global optimization over polynomials with Matlab and SeDuMi
ACM Transactions on Mathematical Software (TOMS)
Advances in Interval Methods for Deterministic Global Optimization in Chemical Engineering
Journal of Global Optimization
Ant colony optimization theory: a survey
Theoretical Computer Science
A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
Variable space search for graph coloring
Discrete Applied Mathematics
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gaussian variable neighborhood search for continuous optimization
Computers and Operations Research
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We propose a new heuristic for nonlinear global optimization combining a variable neighborhood search framework with a modified trust-region algorithm as local search. The proposed method presents the capability to prematurely interrupt the local search if the iterates are converging to a local minimum that has already been visited or if they are reaching an area where no significant improvement can be expected. The neighborhoods, as well as the neighbors selection procedure, are exploiting the curvature of the objective function. Numerical tests are performed on a set of unconstrained nonlinear problems from the literature. Results illustrate that the new method significantly outperforms existing heuristics from the literature in terms of success rate, CPU time, and number of function evaluations.