Esquivel, S.C. and Coello, C.A.C. (2003) On the Use of Particle Swarm Optimization with Multimodal Functions. IEEE Congress on Evolutionary Computation, Canberra, 8-12 December 2003, 1130-1136.
http://dx.doi.org/10.1109/cec.2003.1299795

has been cited by the following article:

• TITLE: OKPS: A Reactive/Cooperative Multi-Sensors Data Fusion Approach Designed for Robust Vehicle Localization

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

  KEYWORDS: Localization, Mobile Robotic, Extended Kalman Filter, Particle Swarm Optimization, Particle Filter, Data Fusion, Vehicle Positioning, GPS

  JOURNAL NAME: Positioning, Vol.7 No.1, December 31, 2015

  ABSTRACT: This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.