In this paper we describe a generic interface for simulated annealing programs using C++ and EcliPSe (for distributed versions). We have tested such interface by building software for six applications. We have run the simulations using the following approaches: Serial, Replication, Combined Chains, and a generic approach suggested by [1] that we refer as a Subchaining method. Results have been collected for all these approaches and new ideas and modifications of these methods have been discussed and tested. One of the main goals has been to reduce the programming load of the end user to the development of three functions. We have also used a design that minimizes the required modifications when going from the serial version to the distribut...
Simulated annealing is a general approach for approximately solving large combinatorial optimization...
This paper explores the use of simulated annealing (SA) for solving arbitrary combinatorial optimisa...
International audienceFinding the global minimum of a nonconvex optimization problem is a notoriousl...
Since its introduction as a generic heuristic for discrete optimisation in 1983, simulated annealing...
Simulated annealing has proven to be a good technique for solving hard combinatorial optimization p...
This book presents state of the art contributes to Simulated Annealing (SA) that is a well-known pro...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
An overview of physical annealing and simulated annealing methods is presented. The target audience ...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
Simulated annealing is an attractive, but expensive, heuristic for approximating the solution to com...
Abstract. Simulated annealing’s high computational intensity has stimulated researchers to experimen...
This work has attempted to exploit information sharing to improve the results of Adaptive Simulated ...
This chapter discusses simulated annealing and generalizations. The simulated annealing algorithm as...
Simulated Annealing (SA) is one of the oldest metaheuristics and has been adapted to solve many comb...
Abstract Simulated annealing is a popular local search meta-heuristic used to address discrete and, ...
Simulated annealing is a general approach for approximately solving large combinatorial optimization...
This paper explores the use of simulated annealing (SA) for solving arbitrary combinatorial optimisa...
International audienceFinding the global minimum of a nonconvex optimization problem is a notoriousl...
Since its introduction as a generic heuristic for discrete optimisation in 1983, simulated annealing...
Simulated annealing has proven to be a good technique for solving hard combinatorial optimization p...
This book presents state of the art contributes to Simulated Annealing (SA) that is a well-known pro...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
An overview of physical annealing and simulated annealing methods is presented. The target audience ...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
Simulated annealing is an attractive, but expensive, heuristic for approximating the solution to com...
Abstract. Simulated annealing’s high computational intensity has stimulated researchers to experimen...
This work has attempted to exploit information sharing to improve the results of Adaptive Simulated ...
This chapter discusses simulated annealing and generalizations. The simulated annealing algorithm as...
Simulated Annealing (SA) is one of the oldest metaheuristics and has been adapted to solve many comb...
Abstract Simulated annealing is a popular local search meta-heuristic used to address discrete and, ...
Simulated annealing is a general approach for approximately solving large combinatorial optimization...
This paper explores the use of simulated annealing (SA) for solving arbitrary combinatorial optimisa...
International audienceFinding the global minimum of a nonconvex optimization problem is a notoriousl...