C. A. Coello and M. S. Lechuga, “Mopso: A Proposal for Multiple Objective Particle Swarm Optimization,” IEEE Congress on Evolutionary Computation, Vol. 2, 2002, pp. 1051-1056.
http://ieeexplore.ieee.org/xpl/abstractCitations.jsp?tp=&arnumber=1004388&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1004388

has been cited by the following article:

• TITLE: A Robust Hybrid Multisource Data Fusion Approach for Vehicle Localization

  AUTHORS: Adda Redouane Ahmed Bacha, Dominique Gruyer, Alain Lambert

  KEYWORDS: Localization; Mobile Robotic; Kalman Filter; EKF; Particle Swarm Optimization; PSO; Particle Filter; Data Fusion; Vehicle Positioning; Navigation; GPS

  JOURNAL NAME: Positioning, Vol.4 No.4, November 13, 2013

  ABSTRACT: In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is developed addressing the challenge of managing reactivity and robustness during a real time ego-localization process. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. In these situations, the balance between reactivity and robustness concepts is crucial. The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO). It combines advantages coming from two filters: Particle Filter (PF) and Extended Kalman filter (EKF). The OKS is tested using real embedded sensors data collected in the Satory’s test tracks. The OKS is also compared with both the well-known EKF and the Particle Filters (PF). The results show the efficiency of the OKS for a high dynamic driving scenario with damaged and low quality GPS data.