An information guided framework for simulated annealing

A framework for information-guided simulated annealing is presented in this paper. Information gathered during randomized exploration of the optimization domain is used as feedback with progressively increasing gain to drive the optimization procedure. Modeling of “information” can be performed in a variety of ways, with the ultimate objective of keeping track of the performance of the stochastic search procedure. A guided-annealing temperature is defined that incorporates information into the cooling schedule. The resulting algorithm has two phases: phase I performs (nearly) unrestricted exploration as a reconnaissance to survey the optimization domain, while phase II “re-heats” the optimization procedure and exploits information gathered during phase I. Phase I flows seamlessly into phase II via an information effectiveness parameter without need for user input. The algorithm presented in this paper improves the performance and success rate of the existing simulated annealing algorithms significantly. Results of are presented for a problem that is traditionally used in the literature to illustrate the shortcomings of simulated annealing and significant improvement is illustrated.