Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures

  • Authors:

  • Young-Jin Cha;Anil K. Agrawal;Yeesock Kim;Anne M. Raich

  • Affiliations:

  • Civil Engineering, The City College of the City University of New York, New York, NY, USA;Civil Engineering, The City College of the City University of New York, New York, NY, USA;Civil and Environmental Engineering, Worcester Polytechnic Institute, Worcester, MA, USA;Civil and Environmental Engineering, Lafayette College, Easton, PA, USA

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

This paper proposes a multi-objective genetic algorithm (MOGA) for optimal placements of control devices and sensors in seismically excited civil structures through the integration of an implicit redundant representation genetic algorithm with a strength Pareto evolutionary algorithm 2. Not only are the total number and locations of control devices and sensors optimized, but dynamic responses of structures are also minimized as objective functions in the multi-objective formulation, i.e., both cost and seismic response control performance are simultaneously considered in structural control system design. The linear quadratic Gaussian control algorithm, hydraulic actuators and accelerometers are used for synthesis of active structural control systems on large civil structures. Three and twenty-story benchmark building structures are considered to demonstrate the performance of the proposed MOGA. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement of actuators and sensors in seismically excited large buildings such that the performance on dynamic responses is also satisfied.