Title :
A Class of Continuously Differentiable Filled Functions for Global Optimization
Author_Institution :
Arkansas Univ., Little Rock
Abstract :
The filled function method is an approach to find global minima of multidimensional multimodal functions. This paper proposes a class of new filled functions that are continuously differentiable and do not include exponential terms. The performance of the new function in numerical experiments for a large set of testing functions up to 40 dimensions is quite satisfactory.
Keywords :
differentiation; optimisation; differentiable filled functions; global minima; global optimization; gradient methods; multidimensional multimodal functions; nonlinear programming; Clustering methods; Functional programming; Genetic algorithms; Gradient methods; Modeling; Multidimensional systems; Optimization methods; Simulated annealing; Testing; Tunneling; Filled function method (FFM); global optimization; gradient methods; nonlinear programming;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/TSMCA.2007.909554
