Energy landscape paving as a perfect optimization approach under detrended fluctuation analysis

Global optimization (GO) is one of the key numerical tools in computational physics. Among the GO algorithms the ones originating in statistical physics are particularly powerful. Recently an adaptive scheme was developed to increase the efficiency of one of these algorithms (stochastic tunneling). This scheme is based on the time-series of minima tested and the respective detrended fluctuation analysis (DFA). We here present a study on another GO methodology (energy landscape paving), which in itself is adaptive, and show that its performance is optimal under the DFA analysis. We give arguments to explain this fact.