Multi-objective quick group search optimizer and its application in model updating

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (20) : 120-128.

Multi-objective quick group search optimizer and its application in model updating

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Abstract

Model updating is a typical multi-objective optimization problems. Comparing with other traditional swarm intelligent algorithms, the group search optimizer(GSO) algorithm has outstanding performance on solving complex optimization problems. However, it is not free from the problems such as entrapped by the local optima and premature convergence when it was utilized in multi-objective optimization. In this paper, a novel multi-objective fast group search optimizer named MQGSO is proposed for solving these problems. To simplify the computation, the LPS method was introduced in updating of producers. Meanwhile, PSO evolutionary strategy is adopted in updating of scroungers to improve the convergence accuracy. Moreover, to restricting with local optima, a crossover and mutation operation was introduced to increase the diversity of scroungers in every iteration. In addition, mutation probability, which can increase the randomicity of rangers, is introduced to enhance the global searching capability. Seven multi-objective minimization benchmark functions and a model updating case of the launch platform are used to evaluate the proposed MQGSO against MPSO and MBFO algorithms. The calculation results show that the MQGSO has a preferable convergence rate and accuracy, and it is an effective method for multi-objective optimization.

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multi-objective optimization / group search optimizer(GSO) / Pareto optimal solution / model updating / objective function

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Multi-objective quick group search optimizer and its application in model updating . Multi-objective quick group search optimizer and its application in model updating[J]. Journal of Vibration and Shock, 2015, 34(20): 120-128

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