Bánhelyi, Balázs

Zombori, Dániel

Zombori Dániel

Bánhelyi Balázs

University of Szeged

Parallel implementation of a modular, population based globaloptimizer packageDániel Zombori, Balázs BánhelyiIn the world of optimization the problems are very different. Therefore it is impossible togive a single solution method to find the global optimum for all of them. The best way is tocreate a package of various optimization methods, which contains solvers for a wide range ofproblems. The other issue is that sometimes much CPU time is necessary to compute the objec-tive function in a point. For example, when the objective values come from simulations, whichis very common in physics problems. In this case the running time of the optimization methodis very high. A suitable solution for this is to compute the function evaluations simultaneously.In this case the algorithm have to handle the objective values simultaneously. Hence the trivialsimple deterministic methods is not fitting for this problem.In our presentation we parallelize the GLOBAL optimization method which is developedby in the Institute of Informatics, University of Szeged. GLOBAL is a stochastic technique thatis a sophisticated composition of sampling, clustering, and local search.The talk presents the architecture of the stochastic global optimization algorithm GLOBALand the single thread Java implementation. Then we present the parallel clustering methodwhich applied in our solution. After this, we show the sampling, clustering, and local searchmethods working in parallel. We applied a technique which is based on the priority of theearlier mentioned methods, and all threads determine alone the next methods to complete.Finally we illustrate the efficiency of our method on large scale popular test functions.65

Parallel implementation of a modular, population based global optimizer package

http://acta.bibl.u-szeged.hu/61743/1/cscs_2016_066.pdf

Parallel implementation of a modular, population based global optimizer package

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