A Particle Filter based Multi-Objective Optimization Algorithm: PFOPS
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This letter is concerned with a recently developed paradigm of population-based optimization, termed particle filter optimization (PFO). In contrast with the commonly used meta-heuristics based methods, the PFO paradigm is attractive in terms of coherence in theory and easiness in mathematical analysis and interpretation. However, 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). Experimental results show that the proposed algorithm works consistently well for three different types of MOO problems, which are characterized by an associated convex, concave and discontinuous Pareto front, respectively.
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