Simulated annealing, a methodology for solving combinatorial optimization problems, is a very computationally expensive algorithm, and as such, numerous researchers have undertaken efforts to parallelize it. In this paper, we investigate three of these parallel simulated annealing strategies when applied to standard cell placement, specifically the TimberWolfSC placement tool. We have examined ...

A general methodology for damper placement in spaceborne interferometers is introduced. The process consists of combined structural/optical/damper modeling, 3. 1, cost selection, and genetic-algorithm or simulated annealing optimization. Combined structural/optical modeling allows the ability to quantitatively predict the effect of mechanical disturbances on optical performance metrics. 3. 1, c...

Multi-constrained quality-of-service routing (QoSR) is to find a feasible path that satisfies multiple constraints simultaneously, as an NPC problem, which is also a big challenge for the upcoming next-generation networks. In this paper, we propose SA_MCP, a novel heuristic algorithm, by applying simulated annealing to Dijkstra's algorithm. This algorithm first uses a nonlinear energy function ...

It is difficult to determine the appropriate temperature parameters which control the acceptance probability in Simulated Annealing, which is a typical meta-heuristic method in the optimization methods. In this paper, we propose a new simulated annealing method that determines the maximum temperature adaptively. The proposed method is base on an important temperature where optimum solutions wer...

A suite of parallel algorithms for ordering DNA sequences (termed PARODS) is presented. The algorithms in PARODS are based on an earlier serial algorithm, ODS, which is a physical mapping algorithm based on simulated annealing. Parallel algorithms for simulated annealing based on Markov chain decomposition are proposed and applied to the problem of physical mapping. Perturbation methods and pro...

Adaptive simulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using other previous simulated annealing algorithms. The author’s ASA code has been publicly available for over two years. During this time the author has volunteered to help people via e-mail, and the feedback obtained has been...

Stochastic global optimization is a very important subject, that has applications in virtually all areas of science and technology. Therefore there is nothing more opportune than writing a book about a successful and mature algorithm that turned out to be a good tool in solving difficult problems. Here we present some techniques for solving several problems by means of Fuzzy Adaptive Simulated ...