Heuristics from nature for hard combinatorial optimization problems

This paper tries 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 Heuritics 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 optimziation problem. The paper considers the following topics: genetic algorithms with timetable problems, simulated annealing with dial-a-ride problems, sampling and clustering with communication spanning tree problems, tabu search with job-shop-scheduling problems, neural nets with location problems, ant system with travelling salesman and quadratic assignment problems.