New computer methods for global optimization
New computer methods for global optimization
A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Nonlinear optimization: complexity issues
Nonlinear optimization: complexity issues
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
A Global Optimization Algorithm using Lagrangian Underestimates and the Interval Newton Method
Journal of Global Optimization
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Interval Branch and Bound with Local Sampling for Constrained Global Optimization
Journal of Global Optimization
Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization
Journal of Global Optimization
Efficient interval partitioning for constrained global optimization
Journal of Global Optimization
Introduction to Interval Analysis
Introduction to Interval Analysis
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
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We present a global optimization algorithm of the interval type that does not require a lot of memory and treats standard constraints. The algorithm is shown to be able to find one globally optimal solution under certain conditions. It has been tested with many examples with various degrees of complexity and a large variety of dimensions ranging from 1 to 2,000 merely in a basic personal computer. The extensive numerical experiments have indicated that the algorithm would have a good chance to successfully find a good approximation of a globally optimal solution. More importantly, it finds such a solution much more quickly and using much less memory space than a conventional interval method. The new algorithm is also compared with several noninterval global optimization methods in our numerical experiments, again showing its clear superiority in most cases.