System Identification of Dampers Using Chaotic Accelerated Particle Swarm Optimization

Abstract

\n\nDifferent chaotic APSO-based algorithms are developed to deal with high non-linear\noptimization problems. Then, considering the difficulty of the problem, an adaptation of these algorithms\nis presented to enhance the algorithm.\n\n\n\nParticle swarm optimization (PSO) is a population-based stochastic optimization technique\nsuitable for global optimization with no need for direct evaluation of gradients. The method\nmimics the social behavior of flocks of birds and swarms of insects and satisfies the five axioms of\nswarm intelligence, namely proximity, quality, diverse response, stability, and adaptability. There are\nsome advantages to using the PSO consisting of easy implementation and a smaller number of parameters\nto be adjusted; however, it is known that the original PSO had difficulties in controlling the balance\nbetween exploration and exploitation. In order to improve this character of the PSO, recently, an\nimproved PSO algorithm, called the accelerated PSO (APSO), was proposed, and preliminary studies\nshow that the APSO can perform superiorly.\n\n\n\nThis paper presents several chaos-enhanced accelerated particle swarm optimization methods\nfor high non-linear optimization problems.\n\n\n\nSome modifications to the APSO-based algorithms are performed to enhance their performance.\nThen, the algorithms are employed to find the optimal parameters of the various types of hysteretic\nBouc-Wen models. The problems are solved by the standard PSO, APSO, different CAPSO,\nand adaptive CAPSO, and the results provide the most useful method. The sub-optimization mechanism\nis added to these methods to enhance the performance of the algorithm.\n\n\n\nSeven different chaotic maps have been investigated to tune the main parameter of the APSO.\nThe main advantage of the CAPSO is that there is a fewer number of parameters compared with other\nPSO variants. In CAPSO, there is only one parameter to be tuned using chaos theory.\n\n\n\nTo adapt the new algorithm for susceptible parameter identification algorithm, two series\nof Bouc-Wen model parameters containing standard and modified Bouc-Wen models are used. Performances\nare assessed on the basis of the best fitness values and the statistical results of the new approaches\nfrom 20 runs with different seeds. Simulation results show that the CAPSO method with\nGauss/mouse, Liebovitch, Tent, and Sinusoidal maps performs satisfactorily.\n