A multi-start global minimization algorithm with dynamic search trajectories
Journal of Optimization Theory and Applications
Random tunneling by means of acceptance-rejection sampling for global optimization
Journal of Optimization Theory and Applications
Global optimization
A Combined Global & Local Search (CGLS) Approach to Global Optimization
Journal of Global Optimization
Computers & Mathematics with Applications
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Two global optimization algorithms are presented. Both algorithms attempt to minimize an unconstrained objective function through the modeling of dynamic search trajectories. The first, namely the Snyman–Fatti algorithm, originated in the 1980's and still appears an effective global optimization algorithm. The second algorithm is currently under development, and is denoted the modified bouncing ball algorithm. For both algorithms, the search trajectories are modified to increase the likelihood of convergence to a low local minimum. Numerical results illustrate the effectiveness of both algorithms.