Usage of Comprehensive Learning Particle Swarm Optimization for Parameter Identification of Structural System

Abstract

This paper introduces a novel swarm intelligence based algorithm named comprehensive learning particle swarm optimization (CLPSO) to identify parameters of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. With the new strategy in this variant of particle swarm optimization (PSO), historical best information for all other particles is used to update a particle's velocity. This means that the particles have more exemplars to learn from, as well as have a larger potential space to fly, avoiding premature convergence. Simulation results for identifying the parameters of a five degree-of-freedom (DOF) structural system under conditions including limited output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness are presented to demonstrate improved estimation of these parameters by the CLPSO when compared with those obtained from standard PSO. In addition, the efficiency and applicability of the proposed method are experimentally examined by a twelve-story shear building shaking table model.

Modified Environmental Adaptation Method and its Application in Test Case Generation

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An Improved Comprehensive Learning - Particle Swarm Optimization - Extended Kalman Filtering Method for the Online High-Precision State of Charge and Model Parameter Co-Estimation of Lithium-Ion Batteries

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