A class of filled functions for finding global minimizers of several variables
Journal of Optimization Theory and Applications
A multi-start global minimization algorithm with dynamic search trajectories
Journal of Optimization Theory and Applications
A filled function method for finding a global minimizer of a function of several variables
Mathematical Programming: Series A and B
Diffusion equation method of global minimization performance for standard test functions
Journal of Optimization Theory and Applications
Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) for fast global optimization
Journal of Optimization Theory and Applications
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Journal of Global Optimization
A dynamic convexized method for nonconvex mixed integer nonlinear programming
Computers and Operations Research
Max-k-Cut by the Discrete Dynamic Convexized Method
INFORMS Journal on Computing
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We consider the box constrained continuous global minimization problem. We present an auxiliary function T(x, k, p), which has the same global minimizers as the problem if p is large enough. The minimization of T(x, k, p) can escape successfully from a previously converged local minimizer by taking the value of k increasingly. We propose an algorithm to find a global minimizer of the box constrained continuous global minimization problem by minimizing T(x, k, p) dynamically. Numerical experiments on two sets of standard testing problems show that the algorithm is effective, and is competent with some well known global minimization methods.