Dynamic Neighbourhood Size in Simulated Annealing

Simulated annealing has been shown to be a powerful stochastic method of tackling hard combinatorial optimisation problems, but it demands a vast amount of computation time to arrive at a good approximate solution. A lot of research has been done on the cooling schedule of simulated annealing to speed up its convergence, but only limited attention has been paid to the impact of the neighbourhood size on the performance of simulated annealing. It has been shown that the performance of simulated annealing can be improved by adopting a suitable neighbourhood size. However, previous studies usually assumed that the neighbourhood size was fixed during search after decided at the beginning. This paper presents a simulated annealing algorithm with a dynamic neighbourhood size which depends on the current “temperature” value during search. A method of dynamically deciding the neighbourhood size by approximating a continuous probability distribution is given. Four continuous probability distributions are used in our experiments to generate neighbourhood sizes dynamically, and the results are compared.