Simulated Annealing

Abstract

Many problems in engineering, planning and manufacturing can be modeled as that of minimizing or maximizing a cost function over a finite set of discrete variables. This class of so-called combinatorial optimization problems has received much attention over the last two decades and major achievements have been made in its analysis ( Papadimitriou and Steiglitz, 1982 ). One of these achievements is the separation of this class into two subclasses. The first one contains the problems that can be solved efficiently, i.e. problems for which algorithms are known that solve each instance to optimality in polynomial time. Examples are linear programming, matching and network problems. The second subclass contains the problems that are notoriously hard, formally referred to as NP-hard (see Chapter 11 for more details). For an NP-hard algorithm it is generally believed that no algorithm exists that solves each instance in polynomial time. Consequently, there are instances that require superpolynomial or exponential time to be solved to optimality. Many known problems belong to this class and probably the best known example is the traveling salesman problem. The above-mentioned distinction is supported by a general framework in computer science called complexity theory; for a detailed introduction and an extensive listing of provably hard problems see Garey and Johnson (1979) and Ausiello et al. (1999). 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.