| Abstract: | Simulated annealing is a randomized algorithm proposed for finding a global optimum in large problems where a target function may have many local extrema. This article considers a modification of the simulated annealing algorithm that turns it into a deterministic technique. Instead of carrying out a stochastic jump based on the annealing update density, the update density is used to select a fixed number of candidate parameter vectors which are all fed to the next iteration of the algorithm. The selection criterion involves not only the update density height, but also information about the origin of the candidate vector. Thus, each iteration produces a cooperative collection of parameter vectors in hope of exploring the parameter space in search of the optimum more thoroughly than the regular annealing. The technique is shown to outperform regular annealing on the problem of restoration of lattice images consisting of simple-shaped objects. |
