Multi-objective Optimization of Structural Active Control System Using a New Hybrid Swarm Algorithm

PAN Zhaodong, TAN Ping, ZHOU Fulin

Abstract

This paper proposes a new multi-objective hybrid swarm optimization method for active control system based on particle swarm algorithm and differential evolution algorithm, in which the parameters of controller, and the number of and allocation of actuator are synchronously optimized. The basic idea is as follows: The different algorithms are used to complete the evolution of corresponding population, the non-dominated solution set is achieved based on the dealer principle, and the leader selection based on boundary point geometry center is adopted. Meanwhile, the simulated annealing algorithm is used for the secondary local search, the two indexes reflecting the structural vibration control effect and performance of control strategy are used as the optimization objective function. Finally, a ASCE 9-story benchmark model is used as a numerical example to validate the effectiveness of the proposed method. Compared with the conventional MODE, MOPSO, and MOHA algorithm, the MOHO-SA algorithm has better convergence curve and distribution of the pareto solution sets.

 

 

Keywords: active control,  hybrid swarm algorithm,  two level search,  multi-objective optimization,  dealer principle,  geometric center leader selection


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References


LIU Fuqiang. ZHANG Lingnii. Advances in optimal placement of actuators and sensors [J]. Advances in Mechanics. 2000, 30(4) =506-516. (In Chinese)

CHEN G S. BRUNO R J. SALAMA M. Optimal placement of active/passive members in truss structures using simulated annealing [J]. A1A A Journal. 1991 .29(8): 1327-1334.

LIU X. BEGG D W. MATRAVERS D R. Optimal topology / actuator placement design of structures using SA [J]. Journal of Aerospace Engineering. 1997. 10C3): 119 — 125.

ABDULLAH M M. RICHARDSON A, HAN1FJ. Placement of sensors/actuators on civil structures using genetic algorithms [J]. Earthquake Engineering & Structural Dynamics. 2001. 30(8): 1167-1184.

LIU D K. YANG Y L. LI Q S. Optimum positioning of actuators in tall buildings using genetic algorithm [J]. Computers & Structures. 2003. 81(32) :2823~2827.

Huiyong, JIANG Jian, ZHANG Ling. Optimal placement of MRFD using improved genetic algorithms[J]. Engineering Mechanics, 2001. 21(2)s 145 —151. (In Chinese)

LI Q S. LIU D K, TANG J. et al. Combinatorial optimal design of number and positions of actuators in actively controlled structures using genetic algorithm [j]. Journal of Sound &-Vibration. 2004, 270(4/5):611-624.

TAN P. DYKE S J. RICHARDSON A. et al. Integrated device placement and control design in civil structures using genetic algorithms [J]. Journal of Structural Engineering. 2005, 131(10): 1489—1496.

WU Lianghong. WANG Yaonan. YUAN Xiaofang. et al. Research on differential evolution algorithm for mops[J]. Journal of Hunan University: Natural Sciences. 2009.36(2):53 — 57. (In Chinese)

HUANG Wei. XU Jian. ZHU Dayong.et al. Parameters optimization on particle swarm of vibration isolation system based optimization (PSO) algorithm [J]. Journal of Hunan University: Natural Sciences. 2014 .41 (11) :58 — 66. (In Chinese)

TAN Ping. NING Xiangliang. BU Guoxiong. et al. Integrated design and multobjective optimization method for active control system [j]. Journal of Vibration Engineering. 2009.22 (6); 638 — 644. (In Chinese)

MERZ P. FRE1SLEBEN B. Fitness landscapes, memetic algorithms. and greedy operators for graph bipartitioning [J]. Evolutionary Computation. 2000, 8( 1);61 — 91.


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