The term metaheuristic was originally coined by Glover (1986). It is now is used to refer to developments of heuristic concepts that go beyond local search (LS) procedures as a means of seeking a global optimum, typically by analogy with natural (physical or biological) processes. Examples of metaheuristics include tabu search (see Section 7.2.2.3), simulated annealing (see Section 7.2.2.6), ant systems (see Section 7.2.2.8) and genetic algorithms (see Section 8.4). A series of bi-annual international conferences on metaheuristics have been held in various locations in recent years (see http://www.mic2007.ca/ for details of the 7th such conference). A number of metaheuristics can be examined and tested using the free educational program, VisualBots (www.visualbots.com), which is a basic simulation and modelling system written as a programmable ActiveX control for Excel (see, for example, the TSP project examples for implementations of simulated annealing, genetic algorithms and ant colony optimisation).
The analogy of many of these procedures with biological systems is often slightly tenuous, drawing ideas rather than detail from the way ants search for food or animal genetics help to create fitter offspring. Furthermore, in many instances the techniques are applied to static problems, whereas biological systems operate in a dynamic environment, for which sub-optimal behaviour that has in-built robustness and flexibility is often more important than temporary optimisation — finding and eating all one’s prey in the shortest possible time may exhaust their population to the point they cannot reproduce and provide you with yet more food! This observation provides not only a note of caution about such analogy-based procedures, but also one of optimism that they may prove particularly useful in dynamical systems — indeed this has been found to be the case in fields such as dynamic telecommunications routing and real-time traffic management.
