Searching for optimal configurations by simulated tunneling

Optimization theory deals with algorithms finding the lowest cost (energy) configuration in a minimal number of steps. When the cost function has many local minima, the deterministic algorithms become easily trapped in suboptimal solutions. The simulated annealing method tries to overcome this difficulty by introducting thermal noise in the problem. Here we explore the possibility of implementing search procedures analogous to the quantum tunneling effect. The suggested dynamics is a guided diffusion process of an interactingpopulation of configurations. Different dynamical aspects of the search process are formulated first in a simple one-dimensional tight-binding model with a hierarchical potential. The new algorithm is then applied to the Traveling Salesman Problem. It is demonstrated that the use of interacting, evolving populations of tours representing our wave packet leads to systematic improvements and possibly, to the optimal tour. In addition, the structure of the cost function landscape for a given instance becomes locally accessible. The performance of the algorithm and its implications for parallel computing and genetic programming are briefly discussed.