An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification

Tian, Tian; Liu, Changyu; Guo, Qi; Yuan, Yi; Li, Wei; Yan, Qingdong

doi:10.3390/en11010095

Cited by 55 publications

(21 citation statements)

References 18 publications

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“…The unknown parameters are obtained along with the iterations of the intelligent optimization algorithms. The parameter identification methods for HTGS or PTGS based on intelligent optimization algorithms have received extensive attention in recent years [13].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm

et al. 2018

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Accurate parameter identification of pump turbine governing system (PTGS) is of great importance to the precise modeling of pumped storage unit. As PTGS is characterized by uncertainties and strong nonlinear characteristics, it is difficult to identify its parameters. To solve the parameter identification problem for PTGS, an improved backtracking search algorithm (IBSA) is proposed by combining the original BSA with the orthogonal initialization technique, the chaotic local search operator, the elastic boundary processing strategy and the adaptive mutation scale factor. The proposed IBSA algorithm for parameter identification of PTGS was applied on an illustrative example to demonstrate its accuracy and efficiency. The simulation results have shown that IBSA performed better compared with the particle swarm optimization, the gravitational search algorithm and the original BSA in regard to solution quality and parameter identification accuracy.

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Section: Introductionmentioning

confidence: 99%

Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm

et al. 2018

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“…A type-l and the type-2 fuzzy set are characterized by a 2-D and 3-D membership function respectively [14]. Mirjalili et al [15] investigated the ant lion optimization (ALO) algorithm. This algorithm has been efficiently solved optimization problems with minimum parameters and execution time.…”

Section: Related Existing Workmentioning

confidence: 99%

Rapidly-implementable optimizely-sizable fuzzy controller architectures: A performance analysis for semiconductor packaging two axes table

Karam¹,

Ghintab²,

Hasan³

2019

PEN

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The tendency of miniaturizing semiconductor products towards nano-size transistor in integrated chips has motivated this work on the semiconductor package. Consequently, Four Fuzzy PID controller architectures based on type 2 FLC are developed; the Interval Type-2 Fuzzy Logic PID, IT2FLC PID MOALO-based, IT2FLC PI-PD, and IT2FLC PI-PD MOALO controllers. These architectures are improved to overcome the inherent nonlinearity in X-Y table models and capacitate the uncertainties of the parameters and the disturbances. Both controllers are designed to improve the desired position specification at minimum settling time (Ts), rise time (Tr), overshoot through minimization of oscillation and friction rejection during tracking the desired position trajectory. The ant lion optimization (ALO) algorithm has been efficiently solved optimization problems with minimum parameters and execution time. Hence, Multi-Objective Ant Lion Optimizer (MOALO) has been implemented to size the gains of the proposed controllers to get the desired position trajectory according to the required specification. A comparison with a related existing work shows minimal numerical values of improved transient specification response of Tr, Mp% and Ts for the MOALO-Based developed IT2 FLC PID and IT2 FLC PI-PD architectures. Observation of a higher Maximum Percentage of Enhancement settling time is noticed in both axes within the IT2FLC PI-PD architecture. Accordingly, transient performances of the four architectures have been significantly improved. The improvement is noticeable within the response of IT2FLC PI-PD architecture. The Maximum Percentage of Enhancement in the X-axis and Y-axis has been improved more than eight-fold and six-fold respectively using IT2FLC PI-PD architecture.

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“…Ali uses the ALO to find the optimal positions and determine the sizing of Distributed Generation (DG) in various distribution systems [42]. However, ALO has a drawback of early prematurity and local optimum, especially in solving complicated problems [43]. To solving this problem, different methods have been combined with ALO to enhance the performance.…”

Section: Introductionmentioning

confidence: 99%

“…To solving this problem, different methods have been combined with ALO to enhance the performance. Tian applied PSO after the solution space has been searched by ALO, which can combine the search ability of ALO and PSO [43]. Zhongqiang Wu applied chaotic sequence for generating the initial position of population (ants and antlions) to increase the uniformity and ergodicity [44].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

Zhang

Zhao

et al. 2019

Mathematical Problems in Engineering

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For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.

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An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification

Cited by 55 publications

References 18 publications

Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm

Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm

Rapidly-implementable optimizely-sizable fuzzy controller architectures: A performance analysis for semiconductor packaging two axes table

Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

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