Evolutionary and bioinspired algorithms for optimization
Throughout the second half of the last century, a set of metaheuristics inspired by evolution and collective intelligence appeared that have had transcendent repercussions in the field of optimization; they are evolutionary algorithms, ant colony optimization (ACO) and particle swarm optimization (PSO). The success of these bioinspired optimization techniques has led, at present, to a growing interest in discovering other strategies inherent in living beings whose computational implementation can bring benefits in the field of optimization and, therefore, a great wave of new approaches is being produced that constitute what we could call the second generation of bioinspired algorithms. They include artificial bee colony (ABC), biogeography-based optimization, and bacterial foraging optimization.
The objective of the line “#CI – Evolutionary and bioinspired algorithms for optimization” is the study and development of bioinspired algorithms for: (1) Improve their behavior when they tackle optimization problems in continuous domains and (2) Apply and adapt them to solve specific combinatorial optimization problems, such as those related to interventions in complex networks, optimal organization of hospitals, etc.
Responsible: Manuel Lozano Márquez