In this paper, a multiobjective simulated annealing (MOSA) method is introduced and discussed with the multiobjective evolutionary algorithms (MOEAs). Though the simulated annealing is a very powerful search algorithm and has shown good results in various singleobjective optimization fields, it has been seldom used for the multiobjective optimization because it conventionally uses only one sear...

The goal of this Random Walks project is to code and experiment the Markov Chain Monte Carlo (MCMC) method for the problem of graph coloring. In this report, we present the plots of cost function H by varying the parameters like q (Number of colors that can be used in coloring) and c (Average node degree). The results are obtained by using simulated annealing scheme, where the temperature (inve...

In this paper, we propose sequential Monte Carlo simulated annealing (SMC-SA), a populationbased simulated annealing algorithm, for continuous global optimization. SMC-SA incorporates the sequential Monte Carlo method to track the converging sequence of Boltzmann distributions in simulated annealing, such that the empirical distribution will converge weakly to the uniform distribution on the se...

This paper presents a simulated annealing algorithm to minimize makespan of single machine scheduling problem with uniform parallel machines. The single machine scheduling problem with uniform parallel machines consists of n jobs, each with single operation, which are to be scheduled on m parallel machines with different speeds. Since, this scheduling problem is a combinatorial problem; usage o...

Parallel algorithms with quality equivalent to the simulated annealing placement algorithm for standard cells [23] are presented. The first, called heuristic spanning, creates parallelism by simultaneously investigating different areas of the plausible combinatorial search space. It is used to replace the high temperature portion of simulated annealing. The low temperature portion of Simulated ...

We present a parallel computational environment used to determine conductivity properties of human head tissues when the effects of skull inhomogeneities are modeled. The environment employs a parallel simulated annealing algorithm to overcome poor convergence rates of the simplex method for larger numbers of head tissues required for accurate modeling of electromagnetic dynamics of brain funct...

Stochastic optimization using simulated annealing has found wide applications in combinatorial optimization of NP-complete problems in recent years, especially in VLSI related problems. The Grid Connection Problem is a combinatorial optimization problem that is proven to be NP-complete. In this paper the simulated annealing algorithm has been applied to the Grid Connection Problem. We also prop...

The Methodology to Parallelize Simulated Annealing (MPSA) leads to massive parallelization by executing each temperature cycle of the Simulated Annealing (SA) algorithm in parallel. The initial solution for each internal cycle is set through a Monte Carlo random sampling to adjust the Boltzmann distribution at the cycle beginning. MPSA uses an asynchronous communication scheme and any implement...