To solve real-world discrete optimization problems approximately metaheuristics such as simulated annealing and other local search methods are commonly used. For large instances o...
The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reve...
In this paper a combined use of reinforcement learning and simulated annealing is treated. Most of the simulated annealing methods suggest using heuristic temperature bounds as the...
Simulated annealing is a general optimisation algorithm, based on hill-climbing. As in hill-climbing, new candidate solutions are selected from the ‘neighbourhood’ of the curre...
Lars Nolle, Alec Goodyear, Adrian A. Hopgood, Phil...
This paper presents a new heuristic algorithm for the graph coloring problem based on a combination of genetic algorithms and simulated annealing. Our algorithm exploits a novel cr...
Dimitris Fotakis, Spiridon D. Likothanassis, Stama...
Automatic design of software architecture by use of genetic algorithms has already been shown to be feasible. A natural problem is to augment – if not replace – genetic algori...
To truly exploit FPGAs for rapid turn-around development and prototyping, placement times must be reduced to seconds; latebound, reconfigurable computing applications may demand p...
We describe two Go programs,  ¢¡¤£¦¥ and  ¢¡¤§¨£ , developed by a Monte-Carlo approach that is simpler than Bruegmann’s (1993) approach. Our method is based on Abra...
Abstract- Simulated annealing (SA) is an effective general heuristic method for solving many combinatorial optimization problems. This paper deals with two problems in SA. One is ...
This paper introduces a new evaluation function, called δ, for the Bandwidth Minimization Problem for Graphs (BMPG). Compared with the classical β evaluation function used, our Î...
Eduardo Rodriguez-Tello, Jin-Kao Hao, Jose Torres-...