A Combined Global & Local Search (CGLS) Approach to Global Optimization
Journal of Global Optimization
Random search optimization approach for highly multi-modal nonlinear problems
Advances in Engineering Software
Computational Optimization and Applications
Sprouting search-an algorithmic framework for asynchronous parallel unconstrained optimization
Optimization Methods & Software
Random search optimization approach for highly multi-modal nonlinear problems
Advances in Engineering Software
Age reading of cod otoliths based on image morphing, filtering and Fourier analysis
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
Explicit gradient information in multiobjective optimization
Operations Research Letters
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This paper presents sequential and parallel derivative-free algorithms for finding a local minimum of smooth and nonsmooth functions of practical interest. It is proved that, under mild assumptions, a sufficient decrease condition holds for a nonsmooth function. Based on this property, the algorithms explore a set of search directions and move to a point with a sufficiently lower functional value. If the function is strictly differentiable at its limit points, a (sub)sequence of points generated by the algorithm converges to a first-order stationary point ($\nabla\!f(x) = 0$). If the function is convex around its limit points, convergence (of a subsequence) to a point with nonnegative directional derivatives on a set of search directions is ensured. Preliminary numerical results on sequential algorithms show that they compare favorably with the recently introduced pattern search methods.