Bánhelyi, Balázs

Mester, Abigél

Mester Abigél

Bánhelyi Balázs

University of Szeged

Revision of local search methods in the GLOBAL optimizationalgorithmAbigél Mester, Balázs BánhelyiThere are many suitable global optimization methods to find the minimum of a nonlinearobjective function. In our presentation we improve the GLOBAL optimization method whichis developed byin the Institute of Informatics, University of Szeged. GLOBAL was used suc-cessfully for the solution of very complex optimization problems.GLOBAL is a stochastic technique that is a sophisticated composition of sampling, cluster-ing, and local search. It compares well with other global optimization software for the solutionof the low dimensional black-box-type problems (when only the objective function is available,while the derivatives should be approximated). It is usually very successful on problems wherethe relative size of the region of attraction of the global minimizer is not negligible.The performance of the GLOBAL depends mainly on the applied local search method ef-ficiency. In our presentation we try to improve the local search methods. First of all, wechanged the original Unirandi random walk technique, so the direction search and the linesearch method became independent modules. After this we recreated the Rosenbrock methodwith the same modular structure. Then we have developed two new different line search meth-ods based on polynomial interpolation schemes. In the presentation we show the efficiency ofthese method combinations, and the advantages of the modular structure.41

Revision of local search methods in the GLOBAL optimization algorithm

http://acta.bibl.u-szeged.hu/61727/1/cscs_2016_042.pdf

Revision of local search methods in the GLOBAL optimization algorithm

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