Evolutionary and bioinspired algorithms for optimization
In the last fifty years, the field of optimization has been revolutionized by the arrival of new algorithms, called meta-heristics, inspired by nature, evolution, and animal behaviors, including the collective intelligence of gregarious animals. Examples of such algorithms would be Genetic Algorithms, inspired by the animal evolution, Ant Colony-based Optimization (ACO) algorithms, or Particle Cloud Optimization (PSO) algorithms, based on the movement of birds. The success of these bio-inspired optimization techniques has produce, nowadays, an increasing interest in discovering other strategies inspired by nature whose computational implementation can bring benefits in the field of optimization. For that reason recently there have been proposed a wave of new approaches that we could call second-generation bio-inspired algorithms. These include algorithms on artificial bee colonies (ABC), bacterial foraging optimization algorithms, …
The objective of the line “#CI – Evolutionary and bio-inspired algorithms for optimization” is the study and development of bio-inspired algorithms to: (1) improve their behavior against optimization problems in continuous domains and (2) apply and adapt them to solve concrete combinatorial optimization problems, such as those related to interventions in complex networks, optimal organization of hospitals, etc.
Contact: Daniel Molina Cabrera
