The Annealing Algorithm pp 1-20 | Cite as

# The Annealing Algorithm: A Preview

Chapter

- 8 Citations
- 97 Downloads

## Abstract

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 while and r > 0 The formulation consists of a function to be minimized, the

*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$$ 2\pi rh + \pi {r^2} $$

$$ \pi {r^2}h = C $$

*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.## Keywords

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.

## Preview

Unable to display preview. Download preview PDF.

## Copyright information

© Kluwer Academic Publishers 1989