What are metaheuristics, genetic algorithms and ant colony optimization?
Many complex combinatorial and numerical optimization problems arise in human activities, such as economics (e.g. portfolio selection), industry (e.g. scheduling or logistics), or engineering (e.g. network routing), among many others.
To get optimal solutions for these kinds of problems in reasonable time using classical algorithmic techniques is quite difficult. This fact has favored the successful development of different approximate algorithm methodologies in the last two decades (called metaheuristics). They are able to quickly provide high quality solutions.
Metaheuristics are particularly suited to solve these problems due to their flexibility, their iterative nature, and their capability to simultaneously optimize several conflicting criteria. They constitute a very diverse family of optimization algorithms including methods such as simulated annealing, tabu search, multi-start methods, iterated local search, variable neighborhood search, GRASP etc. The most successful are biologically inspired like the Genetic Algorithms, and the Ant Colony Optimization (ACO).