DESIGN METHODOLOGIES AND ARCHITECTURES OF HARDWARE - BASED EVOLUTIONARY ALGORITHMS FOR AEROSPACE OPTIMISATION APPLICATIONS ON FPGAS Jonathan

The motivation for this thesis stems from the interest to address the computational time complexity of evolutionary computation techniques. Investigations into parallel computing concepts through digital hardware-based designs are carried out for improving computation run-time and meeting constraints of highly automated aircraft systems. Evolutionary algorithm (EA) is an effective evolutionary computation technique that is widely used in many fields of research and development. Fundamentally, EA is a generic population-based metaheuristic optimisation algorithm that employs features inspired by biological evolution. The practical applications of EAs are limited by the heavy computational overhead that arises from the complexity of real-world scenarios, especially when applied to aerospace optimisation problems. EAs are therefore rarely used as an on-board optimisation method for unmanned aerial vehicles (UAVs) or highly automated aircraft systems where flight computer processor power is limited. A few of the common ways to address this issue is to simplify the optimisation problem, run an EA offline or use a compromised algorithm in place of an EA. The key to realising the full potential of EAs lies in addressing the algorithm design from a lower level. Although EAs were originally designed and intended to run sequentially, they opportunistically have inherent parallelism potentials that are attributed to their population-based characteristics and the low dependency of