Evolutionary Recovery from Sensor Failure in a Trained Multi-objective Swarm

One of the benefits of using Combs disjunctive control in swarm intelligence is the ability of the swarm to continue to operate even when one or more of its sensors are broken. Instead of failing, the swarm continues to perform based on the inputs of the remaining sensors. In this project, a multi-objective scenario is designed for a swarm of agents to complete. Once optimized, the swarm is then modified by removing one of its sensors. Maintaining the original objective, the evolution process is continued to allow the swarm to compensate for the sensor failure. This paper describes the different emergent behaviors of the swarm after the post failure rules are optimized using an evolutionary learning algorithm.