This report proposes a double simulated annealing (DSA) for functional magnetic resonance image (fMRI) analysis. The first simulated annealing (SA) is used to disconnect the brain from the skull. The second SA is applied to locate the activation area of the fMRIs. The performance evaluation of this approach includes receiver-operating characteristic (ROC) analysis, similarity analysis, and comp...

Simulated annealing (SA) heuristics have been successfully applied on a variety of complex optimization problems. This paper presents a new hybrid SA approach for the permutation ¯ow-shop scheduling (FSS) problem. FSS is known to be NP-hard, and thus the right way to proceed is through the use of heuristics techniques. The proposed approach combines the characteristics of a canonical SA procedu...

This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments show the proposed QA algorithm finds better clustering assignments than SA. Furthermore, QA is as easy as SA to implement.

projects scheduling by the project portfolio selection, something that has its own complexity and its flexibility, can create different composition of the project portfolio. an integer programming model is formulated for the project portfolio selection and scheduling.two heuristic algorithms, genetic algorithm (ga) and simulated annealing (sa), are presented to solve the problem. results of cal...

It is a long time that the Simulated Annealing (SA) procedure is introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules start...

It is a long time that the Simulated Annealing (SA) procedure is introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules start...