A Particle Filter based Multi-Objective Optimization Algorithm: PFOPS
This work addresses the limitation of existing PFO algorithms to single-objective cases, enabling application to real-world MOO problems, though it is an incremental extension of the PFO paradigm.
The authors extended the particle filter optimization (PFO) paradigm to handle multi-objective optimization (MOO) problems by incorporating path sampling, resulting in the PFOPS algorithm, which was validated on three benchmark experiments with convex, concave, and discontinuous Pareto fronts.
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.