For np-hard combinatorial optimization problems that frequently arise in production, logistics, and transportation exact solution methods are only applicable up to a certain problem size. On the other hand, simple constructive heuristics and improvement heuristics can be used to obtain good solutions quickly. These heuristics are typically tailored to a certain application area and are likely to get stuck in a local optimum. Since a few decades researchers have tried to approach the global optimal solution by proposing some general purpose metaheuristics.
Many such metaheuristics have been developed. Some are nature inspired, some are population based, and most of them are stochastic. Some are constructive; others are based on local search. The talk will briefly mention the historical development and give an overview of the most popular and successful metaheuristics. It will be pointed out that the key success factor is to strike a proper balance between diversification (i.e. coverage of all regions of the search space) and intensification (i.e. more aggressive search for a close local optimum).