Multichannel deconvolution through simulated annealing

Identifying algorithmic vulnerabilities through simulated annealing

Real–time software systems with tight performance requirements are abundant. These systems frequently use many different algorithms and if these algorithms were to experience worst case behavior, the system may not be able to meet its performance requirements. Unfortunately, the inputs which would cause worst case behavior are often unknown, making it very difficult to defend against them. In t...

Composite Stock Cutting through Simulated Annealing†

This paper explores the use of Simulated Annealing as an optimization technique for the problem Composite Material Stock Cutting. The shapes are not constrained to be convex polygons or even regular shapes. However, due to the composite nature of the material, the orientation of the shapes on the stock is restricted. For placements of various shapes, we show how to determine a cost function, an...

Modified Sparse Multichannel Blind Deconvolution

A Mushy State Simulated Annealing

It is a long time that the Simulated Annealing (SA) procedure has been 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...

Simulated annealing

Metaheuristics are general problem solving algorithms which abstract from the actual problem description. Therefore they can be easily applied to many optimization problems. Simulated annealing is a simple and fast metaheuristic with an analogy to metal processing. As metal particles generate a solid and regular structure when cooling slowing simulated annealing seeks a low-energy solution avoi...