Abstract:
The demand for increased automation of industrial processes generates control problems
that are dynamic, multi-objective and noisy at the same time. The primary
hypothesis underlying this research is that dynamic evolutionary methods could be
used to address dynamic control problems where con
icting control criteria are necessary.
The aim of this research is to develop a framework for on-line optimisation
of dynamic problems that is capable of a) representing problems in a quantitative
way, b) identifying optimal solutions using multi-objective evolutionary algorithms,
and c) automatically selecting an optimal solution among alternatives.
A literature review identi es key problems in the area of dynamic multi-objective
optimisation, discusses the on-line decision making aspect, analyses existing Multi-
Objective Evolutionary Algorithms (MOEA) applications and identi es research
gap. Dynamic evolutionary multi-objective search and on-line a posteriori decision
maker are integrated into an evolutionary multi-objective controller that uses an
internal process model to evaluate the tness of solutions.
Using a benchmark multi-objective optimisation problem, the MOEA ability
to track the moving optima is examined with di erent parameter values, namely,
length of pre-execution, frequency of change, length of prediction interval and static
mutation rate. A dynamic MOEA with restricted elitism is suggested for noisy
environments.To address the on-line decision making aspect of the dynamic multi-objective
optimisation, a novel method for constructing game trees for real-valued multiobjective
problems is presented. A novel decision making algorithm based on game
trees is proposed along with a baseline random decision maker.
The proposed evolutionary multi-objective controller is systematically analysed
using an inverted pendulum problem and its performance is compared to Proportional{
Integral{Derivative (PID) and nonlinear Model Predictive Control (MPC) approaches.
Finally, the proposed control approach is integrated into a multi-agent framework
for coordinated control of multiple entities and validated using a case study of a
tra c scheduling problem.
