Optimization of decentralized information dissemination in quadrotor swarm using genetic algorithm

There is a glaring problem in communication systems when it comes to a decentralized robotic swarm. Since a decentralized swarm would limit the awareness of each agent to its immediate surroundings/neighbors, the exchange of information between agents may now prove to be challenging. An epidemic-based broadcasting technique is then presented to resolve the problem of end-to-end agent communication. This paper aims to optimize the information diffusion by means of implementing genetic algorithm to optimize the time it will take for each quadrotor individual to acquire the information coming from a single source (i.e. the quadrotor who first received the information from an external stimulus). The method by which this is done is epidemic in nature. Due to this, for each time there would be a signal broadcasting, the genetic algorithm would be run to determine the next ideal location of each individual. A genetic algorithm was looped several times to achieve the desired solution. The results showed that for each run of the GA, the number of quadrotors having received the information continually increased until the output converges to a fitness level. However this only worked under certain constraints that need to be weighed out properly. This includes the readjustment of the fitness and crossover functions. Also, the parameters of the GA must be well calibrated for proper output response.