Cloud theory-based simulated annealing algorithm and application

Abstract

Using the randomness and stable tendency of a Y condition normal cloud generator, a cloud theory-based simulated annealing algorithm (CSA) is originally proposed, whose characteristic is approximately continuous decrease in temperature and implied “Backfire & Re-Annealing”. It fits the annealing process of solid matter in nature much better, overcomes the traditional simulated annealing algorithm (SA)'s disadvantages, which are slow searching speed and being trapped by local minimum easily, then enhances the veracity of final solution and reduces the time cost of the optimization process simultaneously. Theory analysis proves that CSA is convergent and typical function optimization experiments show that CSA is superior to SA in terms of convergence speed, searching ability and robustness. The result of the application using CSA for multiple observers sitting problem (MOST) in visibility-based terrain reasoning (VBTR) also declares the new algorithm's usefulness and effectiveness adequately.