A Hybrid Descent Method for Global Optimization

Authors:
K. F. C. Yiu;Y. Liu;K. L. Teo
Affiliations:
Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong;Department of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong (e-mail: cedric_yiu@hkucc.hku.hk);Department of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong (e-mail: cedric_yiu@hkucc.hku.hk)
Venue:
Journal of Global Optimization
Year:
2004

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Abstract

In this paper, a hybrid descent method, consisting of a simulated annealing algorithm and a gradient-based method, is proposed. The simulated annealing algorithm is used to locate descent points for previously converged local minima. The combined method has the descent property and the convergence is monotonic. To demonstrate the effectiveness of the proposed hybrid descent method, several multi-dimensional non-convex optimization problems are solved. Numerical examples show that global minimum can be sought via this hybrid descent method.