A parallel algorithm for global optimization problems in a distributed computing environment

The problem of finding a global minimum of a real function on a set S of Rn occurs in many real world problems. Since its computational complexity is exponential, its solution can be a very expensive computational task. In this paper, we introduce a parallel algorithm that exploits the latest computers in the market equipped with more than one processor, and used in clusters of computers. The algorithm belongs to the improvement of local minima algorithm family, and carries on local minimum searches iteratively but trying not to find an already found local optimizer. Numerical experiments have been carried out on two computers equipped with four and six processors; fourteen configurations of the computing resources have been investigated. To evaluate the algorithm performances the speedup and the efficiency are reported for each configuration

Un nuovo metodo per l'ottimizzazione globale

A large number of algorithms introduced in the literature to find the global minimum of a real function relay on the search of local minima. The multistart and tunneling methods produce well known procedure. A crucial point of these algorithms is to establish whether to perform or not a new local search. In this work, after a brief description of well known global optimization methods, we consider a new technique to handle this matter. We choose to carry on a local search according to a probability D that is calculated so as to minimize the average number evals of function evaluations needed to get a new local minimum. The values required to calculate evals are estimated from the history of the algorithm at the running time. The algorithm has been tested with sample problems usually used by researches and the outcome is compared with recently published results