  Value-alignment in normative multi-agent systems is used to promote a certain
value and to ensure the consistent behavior of agents in autonomous intelligent
systems with human values. However, the current literature is limited to
incorporation of effective norms for single value alignment with no
consideration of agents' heterogeneity and the requirement of simultaneous
promotion and alignment of multiple values. This research proposes a
multi-value promotion model that uses multi-objective evolutionary algorithms
to produce the optimum parametric set of norms that is aligned with multiple
simultaneous values of heterogeneous agents and the system. To understand
various aspects of this complex problem, several evolutionary algorithms were
used to find a set of optimised norm parameters considering two toy tax
scenarios with two and five values are considered. The results are analysed
from different perspectives to show the impact of a selected evolutionary
algorithm on the solution, and the importance of understanding the relation
between values when prioritising them.
