Improved Heuristic Rules for the Price Algorithm of Global Optimization

• Autorzy: Keil F. J. , Stoyanov S. K. , Yordanov S. Y.
• Języki publikacji: EN

Abstrakty

EN

The present study is aiming at the determination of the influence of some new heuristic rules and parameters which are proposed to improve the convergence of the Price random search procedure for global optimization. The algorithms are tested for twenty complex problems with multimodal objective functions to get reliable statistical information about the probability to find the global optimum using several heuristic procedures. The following parameters have been investigated: the type of random generator (single or multiple), the number of randomly generated initial points; the distribution of initial points and the number of cluster points. New heuristic rules are proposed for the calculation of new points in the clusters and the selection of the cluster pole. Comparison of the new algorithms is made on the basis of convergence and average number of function evaluations for the problems solved. It is proved that the modified algorithms with new heuristic rules have a better convergence and are more efficient in respect of the average number of function evaluations. The proper number of points in the initial cluster is recommended.

Słowa kluczowe

EN

algorithm; heuristic rules; optimization; global optimization; Price algorithm; convergence; clusters; sub-clusters; adaptive random search; grid distribution; random distribution

PL

algorytm; metoda heurystyczna; konwergencja

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2007
• Tom: Vol. 15, nr 1
• Strony: 63--82
• Opis fizyczny: Bibliogr. 10 poz.

Twórcy

autor

Keil F. J.

autor

Stoyanov S. K.

autor

Yordanov S. Y.

• Hamburg University of Technology, 38 Eissendorfer Str., D-21071 Hamburg, Germany,,

Bibliografia

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