A Hybrid Multi-Objective Evolutionary Algorithm Using an Inverse Neural Network for Aircraft Control System Design

Abstract

This study introduces a hybrid multiobjective evolutionary algorithm (MOEA) 
for the optimization of aircraft control system design. The strategy suggested 
here is composed mainly of two stages. The first stage consists of training an 
Artificial Neural Network (ANN) with objective values as inputs and decision 
variables as outputs to model an approximation of the inverse of the objective 
function used. The second stage consists of a local improvement phase in objective 
space preserving objectives relationships, and a mapping process to decision variables 
using the trained ANN. Both the hybrid MOEA and the original MOEA were applied to 
an aircraft control system design application for assessment.