TY - JOUR
T1 - A multilevel genetic algorithm for the optimum design of structural control systems
AU - Li, Q. S.
AU - Liu, D. K.
AU - Leung, A. Y T
AU - Zhang, N.
AU - Luo, Q. Z.
PY - 2002/11/10
Y1 - 2002/11/10
N2 - A multilevel genetic algorithm (MLGA) is proposed in this paper for solving the kind of optimization problems which are multilevel structures in nature and have features of mixed-discrete design variables, multi-modal and non-continuous objective functions, etc. Firstly, the formulation of the mixed-discrete multilevel optimization problems is presented. Secondly, the architecture and implementation of MLGA are described. Thirdly, the algorithm is applied to two multilevel optimization problems. The first one is a three-level optimization problem in which the optimization of the number of actuators, the positions of actuators and the control parameters are considered in different levels. An actively controlled tall building subjected to strong wind action is considered to investigate the effectiveness of the proposed algorithm. The second application considers a combinatorial optimization problem in which the number and configuration of actuators are optimized simultaneously, an actively controlled building under earthquake excitations is adopted for this case study. Finally, some results and discussions about the application of the proposed algorithm are presented. Copyright © 2002 John Wiley & Sons, Ltd.
AB - A multilevel genetic algorithm (MLGA) is proposed in this paper for solving the kind of optimization problems which are multilevel structures in nature and have features of mixed-discrete design variables, multi-modal and non-continuous objective functions, etc. Firstly, the formulation of the mixed-discrete multilevel optimization problems is presented. Secondly, the architecture and implementation of MLGA are described. Thirdly, the algorithm is applied to two multilevel optimization problems. The first one is a three-level optimization problem in which the optimization of the number of actuators, the positions of actuators and the control parameters are considered in different levels. An actively controlled tall building subjected to strong wind action is considered to investigate the effectiveness of the proposed algorithm. The second application considers a combinatorial optimization problem in which the number and configuration of actuators are optimized simultaneously, an actively controlled building under earthquake excitations is adopted for this case study. Finally, some results and discussions about the application of the proposed algorithm are presented. Copyright © 2002 John Wiley & Sons, Ltd.
KW - Dynamic
KW - Genetic algorithm
KW - Optimization
KW - Structural control
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0037058315&origin=recordpage
U2 - 10.1002/nme.522
DO - 10.1002/nme.522
M3 - RGC 21 - Publication in refereed journal
SN - 0029-5981
VL - 55
SP - 817
EP - 834
JO - International Journal for Numerical Methods in Engineering
JF - International Journal for Numerical Methods in Engineering
IS - 7
ER -
