In this and two companion papers, we report on an extended empirical study of the simulated annealing approach to combinatorial optimization proposed by S. Kirkpatrick et al. That study investigated how best to adapt simulated annealing to particular problems and compared its performance to that of more traditional algorithms. This paper (Part I) discusses annealing and our parameterized generi...

The Metropolis algorithm is simulated annealing with a fixed temperature. Surprisingly enough, many problems cannot be solved more efficiently by simulated annealing than by the Metropolis algorithm with the best temperature. The problem of finding a natural example (artificial examples are known) where simulated annealing outperforms the Metropolis algorithm for all temperatures has been discu...

In this paper, we demonstrate the ease in which an adaptive simulated annealing algorithm can be designed. Specifically, we use the adaptive annealing schedule known as the modified Lam schedule to apply simulated annealing to the weighted tardiness scheduling problem with sequence-dependent setups. The modified Lam annealing schedule adjusts the temperature to track the theoretical optimal rat...

nowadays organization especially r&d; centers are dealing with project portfolio selection decisions under uncertainty. moreover in the most of the past research, project portfolio selection and scheduling are often considered to be independent problem. this leads to insufficient result in real world. so in this research simultaneous project portfolio selection and scheduling problem is modelin...

In this paper, an improved Simulated Annealing algorithm for Protein Folding Problem (PFP) is presented. This algorithm called Cluster Perturbation Simulated Annealing (CPSA) is based on a brand new scheme to generate new solutions using a cluster perturbation. The algorithm is divided into two phases: Cluster Perturbation Phase and the Reheat Phase. The first phase obtains a good solution in a...

This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into subpopulations, and in each subpopulation the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each subpopulation are migrated to neighbo...

in this paper, the researchers have investigated a concatenated robot move (crm) sequence problem and minimal part set (mps) schedule problem with different setup times for two-machine robotic cell. they have focused on simultaneous solving of crm sequence and mps schedule problems with different loading and unloading times. they have applied a simulated annealing (sa) algorithm to provide a go...

clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. the aim of clustering is to collect data points. in this paper, a cultural algorithm (ca) is presented to optimize partition with n objects into k clusters. the ca is one of the effective methods for searching into the problem space in order to find a n...

We applied simulated annealing algorithm to nurse scheduling problem. For time complexity problem of simulated annealing, we suggested an efficient transition rule using cost matrix for simulated annealing. The experimental results showed that the suggested method generated a nurse scheduling faster in time and better in quality compared to traditional simulated annealing.