Online Performance Measures for Metaheuristic Optimization

摘要: (Global) optimization is one of the fundamental challenges in scientific computing. Frequently, encounters objective functions or search space topologies that do not fulfill necessary requirements for well understood and efficient procedures like, e.g., linear programming. This methodological gap filled by metaheuristic approaches. Their dynamics high dimensional spaces complicated at present. In particular, choice parameters driving a demanding task. this contribution we show how insight from time series analysis help to investigate – on pure empirical basis schemes. Rather than deriving analytical results convergence behavior, ex ante, propose online observation progress. To end, use Detrended Fluctuation Analysis method metaheuristics as stochastic processes. We apply proposed two different metaheuristic, namely differential evolution basin hopping.