An overview of physical annealing and simulated annealing methods is presented. The target audience includes computational analysts and software engineers who are considering simulated annealing for the solution of a combinatorical optimization problem. Several important implementation issues are discussed including decompositions for parallel programming. Special emphasis is given to the often...

Genetic algorithm is widely used in optimization problems for its excellent global search capabilities and highly parallel processing capabilities; but, it converges prematurely and has a poor local optimization capability in actual operation. Simulated annealing algorithm can avoid the search process falling into local optimum. A hybrid genetic algorithm based on simulated annealing is designe...

Capabilities of enhanced simulated-annealing-based algorithms in solving process planning problems in reconfigurable manufacturing are investigated. The algorithms are enhanced by combining variants of the simulated annealing technique with other algorithm concepts such as (i) knowledge exploitation and (ii) parallelism. Four configurations of simulated annealing algorithms are devised and enga...

We present a parallel simulated annealing algorithm for crystallization of N-body systems. The implementation uses systolic simulated annealing with additional parallel cost function evaluation. We have a hybrid topology which consists of a systolic ring and attached to each ring processor a tree of processors. Our research topic is crystallization of particles on a spherical surface. Some resu...

The approach of reverse engineering gene networks by fitting models has been highly successful, but increasing model complexity means that more powerful global optimisation techniques are required for model fitting; for this, we require faster parallel algorithms. I examine the modeling of the gap gene network in Drosophila, for which a gene network model, in the form of a set of differential e...

Many significant engineering and scientific problems involve optimization of some criteria over a combinatorial configuration space. The two methods most often used to solve these problems effectively—simulated annealing (SA) and genetic algorithms (GA)—do not easily lend themselves to massive parallel implementations. Simulated annealing is a naturally serial algorithm, while GA involves a sel...

Solving the hard Satisfiability Problem is time consuming even for modest-sized problem instances. Solving the Random L-SAT Problem is especially difficult due to the ratio of clauses to variables. This report presents a parallel synchronous simulated annealing method for solving the Random L-SAT Problem on a large-scale distributed-memory multiprocessor. In particular, we use a parallel synchr...

Solving the hard Satissability Problem is time consuming even for modest-sized problem instances. Solving the Random L-SAT Problem is especially diicult due to the ratio of clauses to variables. This report presents a parallel synchronous simulated annealing method for solving the Random L-SAT Problem on a large-scale distributed-memory multiprocessor. In particular, we use a parallel synchrono...