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A Hybrid Genetic Algorithm for Nonconvex Function Minimization

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Abstract

In this paper, we consider the problem of minimizing a function in severalvariables which could be multimodal and may possess discontinuities. A newalgorithm for the problem based on the genetic technique is developed. Thealgorithm is hybrid in nature in the sense that it utilizes the genetictechnique to generate search directions, which are used in an optimizationscheme and is thus different from any other methods in the literature.The algorithm has been tested on the Rosenbrock valley functions in 2 and 4dimensions, and multimodal functions in 2 and 4 dimensions, which are of ahigh degree of difficulty. The results are compared with the Adaptive RandomSearch, and Simulated Annealing algorithms. The performance of the algorithmis also compared to recent global algorithms in terms of the number offunctional evaluations needed to obtain a global minimum and results show thatthe proposed algorithm is better than these algorithms on a set of standardtest problems. It seems that the proposed algorithm is efficient and robust.

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Authors and Affiliations

  1. Data Processing Center, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    M. F. Hussain

  2. Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    K. S. Al-Sultan

Authors
  1. M. F. Hussain
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  2. K. S. Al-Sultan
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Hussain, M.F., Al-Sultan, K.S. A Hybrid Genetic Algorithm for Nonconvex Function Minimization. Journal of Global Optimization 11, 313–324 (1997). https://doi.org/10.1023/A:1008290611151

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