A new probabilistic transformation based on evolutionary algorithm for decision making

The study of alternative probabilistic transformation (PT) in DS theory has emerged recently as an interesting topic, especially in decision making applications. These recent studies have mainly focused on investigating various schemes for assigning both the mass of compound focal elements to each singleton in order to obtain Bayesian belief function for real-world decision making problems. In this paper, work by us also takes inspiration from both Bayesian transformation camps, with a novel evolutionary-based probabilistic transformation (EPT) to select the qualified Bayesian belief function with the maximum value of probabilistic information content (PIC) benefiting from the global optimizing capabilities of evolutionary algorithms. Verification of EPT is carried out by testing it on a set of numerical examples on 4D frames. On each problem instance, comparisons are made between the novel method and those existing approaches, which illustrate the superiority of the proposed method in this paper. Moreover, a simple constraint-handling strategy with EPT is proposed to tackle target type tracking (TTT) problem, simulation results of the constrained EPT on TTT problem prove the rationality of this modification.