Editing Metaheuristic (section)

=== Single-solution vs. population-based ===
Another classification dimension is single solution vs [[Population model (evolutionary algorithm)|population-based]] searches.<ref name="blum03metaheuristics" /><ref name="talbi09metaheuristics" /> Single solution approaches focus on modifying and improving a single candidate solution; single solution metaheuristics include [[simulated annealing]], [[iterated local search]], [[Variable Neighborhood Search|variable neighborhood search]], and [[Guided Local Search|guided local search]].<ref name="talbi09metaheuristics" /> Population-based approaches maintain and improve multiple candidate solutions, often using population characteristics to guide the search; population based metaheuristics include [[evolutionary computation]] and [[particle swarm optimization]].<ref name="talbi09metaheuristics" /> Another category of metaheuristics  is [[Swarm intelligence]] which is a collective behavior of decentralized, self-organized agents in a population or swarm. [[Ant colony optimization]],<ref name="M. Dorigo, Optimization, Learning and Natural Algorithms">M. Dorigo, ''Optimization, Learning and Natural Algorithms'', PhD thesis, Politecnico di Milano, Italie, 1992.</ref> [[particle swarm optimization]],<ref name="talbi09metaheuristics" /> [[social cognitive optimization]] and bacterial foraging algorithm<ref name=":0" /> are examples of this category.