Stochastic combinatorial optimization techniques, such as simulated annealing and genetic algorithms, have become increasingly important in design automation as the size of design problems have grown and the design objectives have become increasingly complex. However, stochastic algorithms are often slow since a large number of random design perturbations are required to achieve an acceptable r...

Nowadays, there are many optimization problems where exact methods do not exist or where deterministic methods are computationally too complex to implement. Simulated annealing may be the answer for these cases. It is not greedy in the sense that it is not fool with false minima, and is pretty easy to implement. Furthermore, because it does not require a mathematical model, it can be used to so...

Formation of nano/ultrafine grain structure in AISI 304L austenitic stainless steel through the martensite reversion treatment was studied. The solution annealed specimens were cold rolled up to 95 % thickness reduction at -15°C. The cold-rolled specimens were subjected to reversion annealing treatment at the temperature range of 700-1000 °C for 10–150 min. Microstructural evolutions were analy...

This paper proposes a new algorithm of a simulated annealing (SA): Parallel Simulated Annealing using Genetic Crossover (PSA/GAc). The proposed algorithm consists of several processes, and in each process SA is operated. The genetic crossover is used to exchange information between solutions at fixed intervals. While SA requires high computational costs, particularly in continuous problems, thi...

In this study, the effects of nitrogen addition on the production of ultrafine/nano grained AISI 201L stainless steel by advanced thermo-mechanical process were investigated. Cast samples were first homogenized at 1200 °C for 4 h, hot-rolled at 1100 °C and finally, solution-annealed at 1150 °C for 2.5 h. Unidirectional and cross multipass cold rolling at 25 °C was carried out to 90% reduction i...

Simulated annealing (SA) algorithms can be modeled as time-inhomogeneous Markov chains. Much work on the convergence rate of simulated annealing algorithms has been well-studied. In this paper, we propose an adiabatic framework for studying simulated annealing algorithm behavior. Specifically, we focus on the problem of simulated annealing algorithms that start from an initial temperature T0 an...

In this study, a simulated annealing approach is developed to find optimal police patrol district design using a discreteevent simulation to evaluate the performances of districting plans, such as average response time and workload variation among districts. Similar to the solution neighborhood definition in an existing simulated annealing approach, the new solution (districting plan) in each i...

Quantum annealing is a generic algorithm using quantum-mechanical fluctuations to search for the solution of an optimization problem. The present paper first reviews the fundamentals of quantum annealing and then reports on preliminary results for an alternative method. The review part includes the relationship of quantum annealing with classical simulated annealing. We next propose a novel qua...

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...

The accurate and automatic segmentation of tissue regions in cervigram images can aid in the identification and classification of precancerous regions. We implement and analyze four GPU (Graphics Processing Unit) based clustering algorithms: K-means, mean shift, deterministic annealing, and spatially coherent deterministic annealing. From our results, we propose a novel parallel algorithm using...