Geology Reference
In-Depth Information
Chapter 14
Comparative Study on Multi-
Objective Genetic Algorithms
for Seismic Response
Controls of Structures
Young-Jin Cha
The City College of New York, USA
Yeesock Kim
Worcester Polytechnic Institute, USA
ABSTRACT
This chapter introduces three new multi-objective genetic algorithms (MOGAs) for minimum distribu-
tions of both actuators and sensors within seismically excited large-scale civil structures such that the
structural responses are also minimized. The first MOGA is developed through the integration of Implicit
Redundant Representation (IRR), Genetic Algorithm (GA), and Non-dominated sorting GA 2 (NSGA2):
NS2-IRR GA. The second one is proposed by combining the best features of both IRR GA and Strength
Pareto Evolutionary Algorithm (SPEA2): SP2-IRR GA. Lastly, Gene Manipulation GA (GMGA) is
developed based on novel recombination and mutation mechanism. To demonstrate the effectiveness
of the proposed three algorithms, two full-scale twenty-story buildings under seismic excitations are
investigated. The performances of the three new algorithms are compared with the ones of the ASCE
benchmark control system while the uncontrolled structural responses are used as a baseline. It is shown
that the performances of the proposed algorithms are slightly better than those of the benchmark control
system. In addition, GMGA outperforms the other genetic algorithms.
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