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Šiame darbe pasiūlytas naujas deterministinis globalios optimizacijos algoritmas, skirtas juodos dėžės funkcijoms, kurioms galioja Lipšico sąlyga, bet Lipšico konstanta nežinoma. Algoritmo pradinėje stadijoje leistinoji sritis yra padalinama simpleksais. Kiekvienoje algoritmo iteracijoje visiems simpleksams randami Lipšico konstantos įverčiai ir galimos mažiausios funkcijos reikšmės simplekse įverčiai; perspektyviausi simpleksai yra atrenkami ir padalinami. Galimai mažiausiai funkcijos reikšmei simplekse rasti sprendžiamas vidinis optimizavimo uždavinys, norint kurį išspręsti buvo pasiūlytas vidinis algoritmas. Eksperimentai atlikti su dviejų ir trijų dimensijų optimizavimo uždaviniais, panaudojant 400 testinių funkcijų, sugeneruotų su GKLS funkcijų generatoriumi. Rezultatai parodė, kad sudėtingi uždaviniai su pasiūlytuoju algoritmu išsprendžiami su mažesniu funkcijos įvertinimų skaičiumi negu su kitais alternatyviais algoritmais.

In this paper, a technology enabling the optimization of the topology of truss or frame structures with genetic algorithms is presented. It has been shown that due to a huge number of possible variants the global solution of similar problems with exhaustive search algorithms is feasible only for systems possessing small numbers of d.o.f. s (usually until 10 nodes). These problems can be solved in a reasonable time by genetic algorithms. The modified genetic algorithm for optimization of topology of truss systems is suggested, where the repair of the genotype, instead of some constraint is used. The solution of numerical examples with original software illustrates the efficiency of proposed technology; the global solutions are obtained in all cases. [ABSTRACT FROM AUTHOR]

Experimental sciences collect large amounts of data. Different techniques are available for information elicitation from data. Frequently statistical analysis should be combined with the experience and intuition of researchers. Human heuristic abilities are developed and oriented to patterns in space of dimensionality up to 3. Multidimensional scaling (MDS) addresses the problem how objects represented by proximity data can be represented by points in low dimensional space. MDS methods are implemented as the optimization of a stress function measuring fit of the proximity data by the distances between the respective points. Since the optimization problem is multimodal, a global optimization method should be used. In the present paper a combination of an evolutionary metaheuristic algorithm with a local search algorithm is used. The experimental results show the influence of metrics defining distances in the considered spaces on the results of multidimensional scaling. Data sets with known and unknown structure and different dimensionality (up to 512 variables) have been visualized. [ABSTRACT FROM AUTHOR]