Multiple sequence alignment has been a useful method in the study of molecular evolution and sequence-structure relationships. This paper presents a new method for multiple sequence alignment based on simulated annealing technique. Dynamic programming has been widely used to find an optimal alignment. However, dynamic programming has several limitations to obtain optimal alignment. It requires ...

Tabu Search is a simple combinatorial optimization strategy that has been applied with great success in applications ranging from graph coloring to scheduling and space planning. This paper describes the application of the Tabu Searchh1] heuristic to the circuit partitioning problem. Results obtained indicate that in most cases Tabu Search yields netlist partitions with 10% fewer cut nets than ...

In this paper, we propose a population-based optimization algorithm, Sequential Monte Carlo Simulated Annealing (SMC-SA), for continuous global optimization. SMC-SA incorporates the sequential Monte Carlo method to track the converging sequence of Boltzmann distributions in simulated annealing. We prove an upper bound on the difference between the empirical distribution yielded by SMC-SA and th...

This research presents a new application of the cloud theory-based simulated annealing algorithm to solve mixed model assembly line sequencing problems where line stoppage cost is expected to be optimized. This objective is highly significant in mixed model assembly line sequencing problems based on just-in-time production system. Moreover, this type of problem is NP-hard and solving this probl...

This paper introduces and analyzes a parallel method of simulated annealing. Borrowing from genetic algorithms, an eeective combination of simulated annealing and genetic algorithms, called parallel recombinative simulated annealing, is developed. This new algorithm strives to retain the desirable asymptotic convergence properties of simulated annealing, while adding the populations approach an...

In this paper, we present a new approach of hybrid simulated annealing method for minimizing multimodel functions called the simulated annealing heuristic pattern search (SAHPS) method. Two subsidiary methods are proposed to achieve the final form of the global search method SAHPS. First, we introduce the approximate descent direction (ADD) method, which is a derivative-free procedure with high...

Two well known stochastic optimization algorithms, simulated annealing and genetic algorithm are compared when using a sample to minimize an objective function which is the expectation of a random variable. Since they lead to minimum depending on the sample, a weighted version of simulated annealing is proposed in order to reduce this kind of overt bias. The algorithms are implemented on an opt...

Relations of simulated annealing and quantum annealing are studied by a mapping from the transition matrix of classical Markovian dynamics of the Ising model to a quantum Hamiltonian and vice versa. It is shown that these two operators, the transition matrix and the Hamiltonian, share the eigenvalue spectrum. Thus, if simulated annealing with slow temperature change does not encounter a difficu...

In this note, we describe a method of simulated annealing for producing k-anonymity. For analytic purposes, there is no reason to expect that the method will be superior or worse than the method of applying genetic algorithms used by Iyengar (2002). The main appeal of simulated annealing is the amount of control it allows of the microaggregation process. The k-anonymity problem is known to be N...