Configuration Space Analysis for Optimization Problems

An interesting analogy between frustrated disordered systems studied in condensed matter physics and combinatorial optimization problems [1] has led to the use of simulated annealing (a stochastic algorithm based on the Monte Carlo method) to find approximate solutions to complex optimization problems. A common feature of these systems is the competition between objectives which favor different and incompatible types of ordering. Such “frustration” leads to the existence of a large number of nearly degenerate solutions which are not related by symmetry.