Control and System Identification via Swarm and Evolutionary Algorithms

Tayebeh Mostajabi, Javad Poshtan Abstract— A central topic of swarm intelligence is the investigation of different types of emergent collective behaviors in swarms. This article focus on the swarm intelligence applications in control and system identification. Particle swarm optimization (PSO), a novel population based stochastic optimizer with fast convergence speed and simple implementation and genetic algorithm, have been successfully applied to solve system identification optimization problems. In addition, PSO and ant colony optimization (ACO) have been applied as a navigation algorithm in swarm robots. Some of the recently proposed swarm based metaheuristics such as bacterial foraging optimization algorithm (BFOA), wasp optimization algorithm (WOA), bee optimization algorithm (BOA) and Physarum Solver will need further investigation to assess their potential for generating state-of-the-art algorithms that are useful for this area.