Constraint refinement for online verifiable cross-layer system adaptation
Proceedings of the conference on Design, automation and test in Europe
Real-time deterministic chaos control by means of selected evolutionary techniques
Engineering Applications of Artificial Intelligence
Investigation on evolutionary computation techniques of a nonlinear system
Modelling and Simulation in Engineering
Maximizing availability of content in disruptive environments by cross-layer optimization
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Simulated annealing is a general optimisation algorithm, based on hill-climbing. As in hill-climbing, new candidate solutions are selected from the 'neighbourhood' of the current solution. 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 application where the number of fitness evaluations is limited. This paper explains how in such a case the use of a variable radius of this neighbourhood, refereed to as maximum step width, can increase the over-all performance of simulated annealing. A real-world example demonstrates the increased performance of the modified algorithm.