Generalized particle swarm optimization algorithm - Theoretical and empirical analysis with application in fault detection

Kanović, Željko; Rapaić, Milan R.; Jeličić, Zoran D.

doi:10.1016/j.amc.2011.05.013

Cited by 38 publications

(15 citation statements)

References 28 publications

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“…The process model under consideration is given by (1). In simulations we use parameter values obtained by identifying a concrete physical crane model, to be described in the following section.…”

Section: Simulation Resultsmentioning

confidence: 99%

Examples of robust controller design

Buljevic

Miletic²,

Mitrović

et al. 2020

Serb J Electr Eng

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In this paper two modern controllers are presented. These controllers should eliminate disturbance effect, handle process variations and process uncertainties. First controller that is considered is conventional Proportional-Integral (PI) controller and second one is Fractional PI (FOPI) controller. After testing both controllers on a series of simulations, performance of both control algorithms were applied on a real 3D crane system.

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Section: Simulation Resultsmentioning

confidence: 99%

Examples of robust controller design

Buljevic

Miletic²,

Mitrović

et al. 2020

Serb J Electr Eng

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“…There are numerous variants of the original PSO algorithm reported in the literature. Here, the Generalized PSO (GPSO) algorithm is used (see [8] and further developments in [9]). GPSO enables tight and precise control over the convergence properties of the particle swarm, which are of paramount importance for the performance of the optimizer [10].…”

Section: B Generalized Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…Following the recommendations of [9], the value of ρ is linearly changed from 0.95 to 0.6, and c is varied from 0.8 to 0.2; moreover, ζ is randomly chosen in each iteration, by sampling it from a uniform distribution over [-0.9, 0.2].…”

Section: B Generalized Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Parameter estimation in non-linear models of pressure dynamics in CNG injection systems

Lino

Maione

Kapetina

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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Common rail injection systems in innovative gas engines require an accurate control of the rail pressure dynamics. This task is, in fact, combined with a precise setting of the opening/closing time intervals of electro-injectors to achieve a satisfactory metering of the ratio between gas and air, then a good performance that reduces pollution and consumption. To achieve a trade-off between model accuracy and control simplicity, an important issue is to optimize the model of the pressure dynamics in the main accumulation volumes. This paper reports preliminary results in estimating the optimal parameters of a non-linear model by two different techniques. First delays in the main variables are estimated by a particle swarm optimization. Then, time-variability of model parameters is investigated.

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“…D. Programs Foundation of Ministry of Education of China (grant number #20090201110027) for solving the problem with high-dimensional search space. Various heuristic approaches, including Genetic algorithm (GA) [3,4], Ant colony optimization (ACO) [5,6], Particle swarm optimization (PSO) [7,8], and Gravitational search algorithm (GSA) [9], etc, have been proposed and widely applied in many areas. These algorithms are typically motivated by biological process or physical phenomenon and they are population-based stochastic optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

A hybrid PSO-GSA strategy for high-dimensional optimization and microarray data clustering

Sun

Peng

2014

2014 IEEE International Conference on Information and Automation (ICIA)

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High-dimensional data analysis and its great chances of overfitting result in great challenges for constructing efficient models in practical applications. To overcome these problems swarm intelligence algorithms can be utilized. However, the balance between global and local search throughout the course of a run is critical to the success of an intelligence optimization algorithm. Moreover, almost all the available algorithms are still having issues like premature convergence to local optimum and slow convergence rate, especially in high-dimensional space. As motivated above, a new hybrid optimization algorithm integrating particle swarm optimization(PSO) with gravitational search algorithm(GSA) is presented (denoted as PSOGSA). Based on the analysis of the compensatory advantages of the PSO and the GSA, in this paper, we integrate the ability of exploitation in PSO with the ability of exploration in GSA to update velocity equations. To update position equations a mobility factor is used which is guided by diversity of population to improve the final accuracy and the convergence speed of the PSOGSA. We also apply proposed algorithm to the cluster analysis of microarray data. Experiments are conducted on six benchmark test functions, four artificial data sets and three microarray data sets, and the results demonstrate that the proposed algorithm possess better robustness.

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Generalized particle swarm optimization algorithm - Theoretical and empirical analysis with application in fault detection

Cited by 38 publications

References 28 publications

Examples of robust controller design

Examples of robust controller design

Parameter estimation in non-linear models of pressure dynamics in CNG injection systems

A hybrid PSO-GSA strategy for high-dimensional optimization and microarray data clustering

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