Comparative Study on Multi-Objective Genetic Algorithms for Seismic Response Controls of Structures

Abstract

This chapter introduces three new multi-objective genetic algorithms (MOGAs) for minimum distributions 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.