Landscape Analysis using Simulated Annealing
Sergiu-Andrei Dinu · 2023
This study aims at obtaining a classification of the optimization landscapes for any function, via features of the footprints of simulated annealing (SA) runs on the respective function. Two types of classification are investigated: function-classification (classes labeled by the names of well-known test functions) and landscape-type-classification (classes labeled by characteristics of the landscape). The idea behind our approach is that an SA-controlled dynamic balance between exploration and exploitation produces during the runs, through the candidate solutions, probability distributions which capture relevant information on the search space landscape; we use three such distributions. In order to achieve comparable SA behaviour across multiple functions, a monotonic decrease of the expected worse-candidate acceptance probability is enforced, with its value reaching 0 exactly at the end of the optimization process. The present study empirically shows the viability of this feature identification technique for various classification tasks on unknown functions.