Heuristics From Nature For Hard Combinatorial Optimization Problems

In this paper we try to describe the main characters of Heuristics "derived" from Nature, a border area between Operations Research and Artificial Intelligence, with applications to graph optimization problems. These algorithms take inspiration from physics, biology, social sciences, and use a certain amount of repeated trials, given by one or more "agents" operating with a mechanism of competition-cooperation. Two introductory sections, devoted respectively to a presentation of some general concepts and to a tentative classification of Heuristics from Nature open the work. The paper is then composed of six review sections: each of them concerns a heuristic and its application to an NP-hard combinatorial optimization problem. We consider the following topics: genetic algorithms with timetable problems, simulated annealing with dial-a-ride problems, sampling & clustering with communication spanning tree problems, tabu search with job-shop-scheduling problems, neural nets with location p..