  Multi-objective optimisation problems involve finding solutions with varying
trade-offs between multiple and often conflicting objectives. Ising machines
are physical devices that aim to find the absolute or approximate ground states
of an Ising model. To apply Ising machines to multi-objective problems, a
weighted sum objective function is used to convert multi-objective into
single-objective problems. However, deriving scalarisation weights that
archives evenly distributed solutions across the Pareto front is not trivial.
Previous work has shown that adaptive weights based on dichotomic search, and
one based on averages of previously explored weights can explore the Pareto
front quicker than uniformly generated weights. However, these adaptive methods
have only been applied to bi-objective problems in the past. In this work, we
extend the adaptive method based on averages in two ways: (i)~we extend the
adaptive method of deriving scalarisation weights for problems with two or more
objectives, and (ii)~we use an alternative measure of distance to improve
performance.
  We compare the proposed method with existing ones and show that it leads to
the best performance on multi-objective Unconstrained Binary Quadratic
Programming (mUBQP) instances with 3 and 4 objectives and that it is
competitive with the best one for instances with 2 objectives.
