State-space search strategies gleaned from animal behavior: a traveling salesman experiment

Abstract.

A widespread search strategy employed by predators in both vertebrate and invertebrate phyla is the well-known area-restricted search strategy. The generality, simplicity, and effectiveness of this strategy have made it emerge many times during the course of natural selection. In this work, an artificial intelligence state-space search procedure is developed using search guidelines gleaned from the foraging behavior of predators. This procedure, which we call predatory search, has been implemented on a NP-Hard combinatorial problem: the traveling salesman problem. Numerical results are presented for a limited set of benchmark problems, and area-restricted search seems to be effective: We have been able to find the optimal solution to, among others, a 400-city Manhattan problem.