The Annealing Algorithm: A Preview

  • R. H. J. M. Otten
  • L. P. P. P. van Ginneken
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 72)


Many problems that arise in practice are concerned with finding a good or even the best configuration or parameter set from a large set of feasible options. Feasible means that the option has to satisfy a number of rigid requirements. An example of such an optimization problem is to find a minimum weight cylindrical can that must hold a given quantity of liquid. Assuming the weight of the empty can to be proportional to its area, the problem can be formulated as finding the dimensions of a cylinder with a volume c, and the smallest possible perimeter: minimize
$$ 2\pi rh + \pi {r^2} $$
$$ \pi {r^2}h = C $$
and r > 0 The formulation consists of a function to be minimized, the objective function, and a number of constraints. These constraints specify the set of feasible options, in this case the parameter pairs (r,h) that represent cylinders with radius r, height h, and volume c. Feasibility requirements are not always given as explicit constraints. In this example the volume constraint could have been substituted into the objective function.


Assignment Problem Score Function Travel Salesman Problem Outer Loop Feasible Option 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • R. H. J. M. Otten
    • 1
  • L. P. P. P. van Ginneken
    • 2
  1. 1.Delft University of TechnologyThe Netherlands
  2. 2.Eindhoven University of TechnologyThe Netherlands

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