Two Asynchronous Parallel Algorithms for Function Optimization
Kang Li · Wuhan University Journal · 2002
Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the sub\|space search method(a general multi\|parent recombination strategy) with the population hill\|climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. In this paper the characteristics of the algorithm are given and some numerical experiments have been done for demostrating the efficiency of the algorithm. The Guo's algorithm has been parallelized as asynchronous parallel algorithms for suiting different parallel and distributed computing environments. The Bump problem as the numerical example is solved by a super parallel computer and some best results are obtained.