Az ezzel foglalkozó kínai cikkek egyikének abstractja:
Holographic research strategy (HRS) is a novel determinate optimization method. The principle of HRS is based on a special, two-dimensional presentation of a multidimensional space. This presentation was termed two-dimensional hologram. HRS translated the optimization operation in multidimensional space into finding better points in the neighborhood around the current best data points. In this way, HRS can find the global optimal parameters in all probability. However, HRS can't be applied to optimize continuous variables, it was only used in optimizing discrete systems. Therefore, it is necessary of improving HRS and ensuring the optimization algorithm can being applied in multidimensional continuous systems. Modified holographic research strategy (MHRS) was designed for the purpose. MHRS changed continuous variables into discrete variables in the searching region firstly, and then found the optimum in the discrete system. In order to reduce the deviation between the continuous system and the discrete system, MHRS adopted iterative algorithm to shrink the searching region gradually according to the location of the current optimal value. Furthermore, in order to improve the efficiency of HRS in searching for the global optimum, random mutation operator was added to the optimizing process. Ten-dimensional Rastrigin function was applied to testing MHRS, the results demonstrated that its global optimization performance is superior to one of eugenic evolution genetic algorithm (EGA). Further, MHRS was applied to estimate the kinetic model parameters of residue hydrofining. Satisfactory results were obtained.
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