Keynote speech II evolutionary multi-objective optimization in engineering and prognostic applications

Multi-objective optimization is widely found in many fields, such as logistics, economics, engineering, or whenever optimal decisions need to be made in the presence of trade-offs. The problem is challenging because it involves the simultaneous optimization of several conflicting objectives in the Pareto optimal sense and requires researchers to address many issues that are unique to MO problems. This talk will provide an overview of evolutionary computation for multi-objective optimization (EMO). It will then present various applications of EMO for solving engineering problems particularly in the area of robust prognostic. As one of the key enablers of condition based maintenance, prognostic involves the core task of determining the remaining useful life (RUL) of the system. This talk will discuss the use of neural network ensembles to improve the prediction accuracy of RUL estimation as well as the use of EMO to optimize the ensemble hyper-parameters in order to achieve the trade-off between accuracy and diversity of deep neural networks as ensemble members. A case study involving the estimation of RUL for turbofan engines will also be presented in the talk.