On a new Stepwidth Adaptation Scheme for Simulated Annealing and its Practical Application

Abstract

Simulated Annealing is a general optimisation algorithm, based on hill-climbing. As in hill-climbing, new candidate solutions are selected from the ‘neighbourhood’ of a current solution and then processed. For continuous parameter optimisation , it is practically impossible to choose direct neighbours, because of the vast number of points in the search space. In this case, it is necessary to choose new candidate solutions from a wider neighbourhood, i.e. from some distance of the current solution, for performance reasons. The right choice of this distance is often crucial for the success of the algorithm, especially in real-world applications where the number of fitness evaluations is limited. This paper introduces a new neighbourhood selection scheme for Simulated Annealing, based on a simplified measurement of the mean distance between particles of the population. This new algorithm is refereed to as Differential Annealing. The performance of the new algorithm is compared with standard Simulated Annealing and Stepwidth Adapting Simulated Annealing for on-line Langmuir probe tuning.