  In multiobjective optimization, the result of an optimization algorithm is a
set of efficient solutions from which the decision maker selects one. It is
common that not all the efficient solutions can be computed in a short time and
the search algorithm has to be stopped prematurely to analyze the solutions
found so far. A set of efficient solutions that are well-spread in the
objective space is preferred to provide the decision maker with a great variety
of solutions. However, just a few exact algorithms in the literature exist with
the ability to provide such a well-spread set of solutions at any moment: we
call them anytime algorithms. We propose a new exact anytime algorithm for
multiobjective combinatorial optimization combining three novel ideas to
enhance the anytime behavior. We compare the proposed algorithm with those in
the state-of-the-art for anytime multiobjective combinatorial optimization
using a set of 480 instances from different well-known benchmarks and four
different performance measures: the overall non-dominated vector generation
ratio, the hypervolume, the general spread and the additive epsilon indicator.
A comprehensive experimental study reveals that our proposal outperforms the
previous algorithms in most of the instances.
