New computer methods for global optimization
New computer methods for global optimization
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simulated Annealing: Searching for an Optimal Temperature Schedule
SIAM Journal on Optimization
Numerical solution for bounding feasible point sets
Journal of Computational and Applied Mathematics
A hybrid global optimization method: the multi-dimensional case
Journal of Computational and Applied Mathematics
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We propose a hybrid global optimization method for nonlinear inverse problems. The method consists of two components: local optimizers and feasible point finders. Local optimizers have been well developed in the literature and can reliably attain the local optimal solution. The feasible point finder proposed here is equivalent to finding the zero points of a one-dimensional function. It warrants that local optimizers either obtain a better solution in the next iteration or produce a global optimal solution. The algorithm by assembling these two components has been proved to converge globally and is able to find all the global optimal solutions. The method has been demonstrated to perform excellently with an example having more than 1 750 000 local minima over [-106, 107].