  This paper is concerned with a recently developed paradigm for
population-based optimization, termed particle filter optimization (PFO). This
paradigm is attractive in terms of coherence in theory and easiness in
mathematical analysis and interpretation. Current PFO algorithms only work for
single-objective optimization cases, while many real-life problems involve
multiple objectives to be optimized simultaneously. To this end, we make an
effort to extend the scope of application of the PFO paradigm to
multi-objective optimization (MOO) cases. An idea called path sampling is
adopted within the PFO scheme to balance the different objectives to be
optimized. The resulting algorithm is thus termed PFO with Path Sampling
(PFOPS). The validity of the presented algorithm is assessed based on three
benchmark MOO experiments, in which the shapes of the Pareto fronts are convex,
concave and discontinuous, respectively.
