Two local search methods for protein folding simulation in the HP and the MJ lattice models

Abstract

We present experimental results on benchmark problems for two local search procedures that utilise the pull-move set: (i) simulated annealing with logarithmic cooling schedule and (ii) guided local search that traverses the energy landscape with greedy steps towards (potential) local minima followed by upwards steps to a certain level of the objective function. The latter method returns optimum values on established 2D and 3D HP benchmark problems faster than logarithmic simulated annealing (LSA), however, it performs worse on five benchmarks designed for the Miyazawa-Jernigan energy function, where LSA reaches optimum solutions on all five benchmarks. Moreover, the number of function evaluations executed by LSA is significantly smaller than the corresponding number for Monte Carlo simulations with kink-jump moves. © Springer-Verlag Berlin Heidelberg 2008.