Particle swarm optimization with quantum infusion for system identification

Luitel, Bipul; Venayagamoorthy, G.K.

doi:10.1016/j.engappai.2010.01.022

Cited by 107 publications

(56 citation statements)

References 20 publications

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“…Among them, many approaches and strategies are proposed to enhance the performance of the PSO by the integration of some skilled operators or adjusting inertia weight in system identification. Fang et al [36], Luitel et al [37] and Sun et al [38], adopting different fusion strategies, have introduced quantumbehaved theory into PSO algorithm to enhance optimization performance for IIR digital filters, respectively. Another strategy is introduced by adjusting the inertia weight and acceleration coefficients to improve the searching ability of the PSO for digital IIR filter design [39,40].…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Wang

2014

Nonlinear Dyn

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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.

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Section: Introductionmentioning

confidence: 99%

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

Jiang

Wang

2014

Nonlinear Dyn

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“…• Resources active (7), (9) and reactive (8), (10) power generation limits in each period , respectively for DG units (7), (8) and upstream suppliers (9), (10). The maximum active power that can be reduced in each consumer, in each step, is assured by (11), (12), (13).…”

Section: Energy Resource Scheduling Formulationmentioning

confidence: 99%

Modified Particle Swarm Optimization applied to integrated demand response and DG resources scheduling

Faria

Soares

Vale

et al. 2014

2014 IEEE PES T&D Conference and Exposition

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Abstract-The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities.The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.Index Terms-Demand response, energy resource management, particle swarm optimization, virtual power player.

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“…In [10][11][12], convergence analysis and other varients of QPSO have been presented. As an efficient algorithm, QPSO has been applied to many optimization problems, such as system identification [13], non-linear programming problems [14], power system [15], etc. Although Coelho et al proposed a quantum-inspired HQPSO using the harmonic oscillator potential well to solve economic dispatch problems [16], Sun and Lu applied QPSO to ED problems [15], and Chakraborty et al presented a hybrid QPSO to solve the ED problems [17], to the best of our knowledge, it has not been used yet to solve ED problems with multiple fuel options.…”

Section: Introductionmentioning

confidence: 99%

An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects

et al. 2012

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Quantum-behaved particle swarm optimization (QPSO) is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO) and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED) problems with valve-point effects and multiple fuel options. To show the performance of the proposed SQPSO, it is tested on five standard benchmark functions and two ED benchmark problems, including a 40-unit ED problem with valve-point effects and a 10-unit ED problem with multiple fuel options. The results are compared with differential evolution (DE), particle swarm optimization (PSO) and basic QPSO, as well as a number of other methods reported in the literature in terms of solution quality, convergence speed and robustness. The simulation results confirm that the proposed SQPSO is effective and reliable for both function optimization and ED problems.

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Particle swarm optimization with quantum infusion for system identification

Cited by 107 publications

References 20 publications

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

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

Modified Particle Swarm Optimization applied to integrated demand response and DG resources scheduling

An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects

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