A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Abstract

Design of adaptive infinite impulse response (IIR) filter is the process of utilizing adaptive algorithm to iteratively determine the filter parameters to obtain an optimal model for the unknown plant based on minimizing the error cost function. However, the error cost surface of IIR filter is generally nonlinear, non-differentiable and multimodal. Hence, an efficient global optimization technique is required to minimize the error cost objective. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA) is proposed in this paper for IIR filter design. The proposed HPSO-GSA updates particle positions through obeying the influence of gravity acceleration in GSA and receiving direction of cognitive memory and social sharing information from PSO by means of coevolutionary strategy. The effect of key parameters on the performance of the proposed algorithm is firstly studied, and the proper parameters in HPSO-GSA are established using five benchmark plants along with the same-order model. The simulation studies have been performed for the performance comparison of eight algorithms such as PSO, GSA, QPSO, DPSO, FO-DPSO, GAPSO, PSOGSA and the proposed HPSO-GSA for unknown IIR system identification with the same-order and reduced-order filters. Simulation results show that the proposed algorithm has advantages over PSO, GSA and other PSO-based variants in terms of the convergence speed and the MSE levels.

Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

“…Among these works, studies on hybrid systems that combine skilled metaheuristic optimization algorithms to obtain good compromise between exploration and exploitation have gained extensive popularity. e most classical PSO variants have been reported in [14,15,23,[28][29][30][31][32][33][34][35][36]. Kao and Zahara [28] proposed the hybridization strategy of PSO and GA (GAPSO) for solving multimodal test functions.…”

“…Liu et al [14] proposed a novel hybrid algorithm named PSODE, where DE is incorporated to update the previous best positions of PSO particles to force them to jump out of local attractor in order to prevent stagnation of population. Besides GA and DE, PSO has been hybridized with extremal optimization (EO) [15], central force optimization (CFO) [23], estimation of distribution algorithm (EDA) [33], artificial immune system (AIS) [34], gravitational search algorithm [35], and teaching-learning-based optimization (TLBO) [36]. Overall, these PSO-based hybrid variants have been successfully utilized for solving global optimization problems.…”

A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

“…Although the GSA approach has demonstrated excellent performance when compared to other state-of-the-art optimization methods [25][26][27][28][29], it is known to suffer from several problems commonly found in such population-based approaches. In particular, the dependence on the fitness function for calculating the mass of search agents often causes GSA search speed to get deteriorated as agents become heavier, essentially manifesting a slow converge rate which worsens as the iterations increase.…”

A Novel Adaptive LMS Algorithm with Genetic Search Capabilities for System Identification of Adaptive FIR and IIR Filters

“…Adaptive system identification had a long history of many types of research ranged from the implementation of neural networks [5][6][7][8][9] to swarm optimization algorithms [10][11][12], reaching to the applications of LMS adaptation algorithm on IIR and FIR adaptive filters proposed by [13,14] with different techniques and applications [15][16][17][18][19]. Applications of Genetic Algorithm (GA) in system identification are studied in [20,21].…”