Construction of large-scale global minimum concave quadratic test problems
Journal of Optimization Theory and Applications
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
New computer methods for global optimization
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Lipschitzian optimization without the Lipschitz constant
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Genetic algorithms: a powerful tool for large-scale nonlinear optimization problems
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Generating box-constrained optimization problems
ACM Transactions on Mathematical Software (TOMS)
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Simulated annealing algorithms for continuous global optimization: convergence conditions
Journal of Optimization Theory and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Convergence Criteria for Genetic Algorithms
SIAM Journal on Computing
Test Functions with Variable Attraction Regions for GlobalOptimization Problems
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Convergence of a Simulated Annealing Algorithm for Continuous Global Optimization
Journal of Global Optimization
Simulated Annealing: A Proof of Convergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convexification and Global Optimization in Continuous And
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A hybrid global optimization method: the multi-dimensional case
Journal of Computational and Applied Mathematics
ACM Transactions on Mathematical Software (TOMS)
A Hybrid Descent Method for Global Optimization
Journal of Global Optimization
Advances in Interval Methods for Deterministic Global Optimization in Chemical Engineering
Journal of Global Optimization
On the multilevel structure of global optimization problems
Computational Optimization and Applications
Objective Function Features Providing Barriers to Rapid Global Optimization
Journal of Global Optimization
A hybrid method for solving multi-objective global optimization problems
Journal of Global Optimization
A new class of test functions for global optimization
Journal of Global Optimization
Journal of Global Optimization
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Mathematical and Computer Modelling: An International Journal
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Inverse problems in geophysics are usually described as data misfit minimization problems, which are difficult to solve because of various mathematical features, such as multi-parameters, nonlinearity and ill-posedness. Local optimization based on function gradient can not guarantee to find out globally optimal solutions, unless a starting point is sufficiently close to the solution. Some global optimization methods based on stochastic searching mechanisms converge in the limit to a globally optimal solution with probability 1. However, finding the global optimum of a complex function is still a great challenge and practically impossible for some problems so far. This work develops a multiscale deterministic global optimization method which divides definition space into sub-domains. Each of these sub-domains contains the same local optimal solution. Local optimization methods and attraction field searching algorithms are combined to determine the attraction basin near the local solution at different function smoothness scales. With Multiscale Parameter Space Partition method, all attraction fields are to be determined after finite steps of parameter space partition, which can prevent redundant searching near the known local solutions. Numerical examples demonstrate the efficiency, global searching ability and stability of this method.