An Opposition-based Simulated Kalman Filter algorithm for adaptive beamforming

Lazarus, Kelvin; Noordin, Nurul H.; Ibrahim, Zuwairie; Jusof, Mohd Falfazli Mat; Faudzi, Ahmad Afif Mohd; Subari, Norazian; Azmi, Kamil Zakwan Mohd

doi:10.1109/icasi.2017.7988354

Cited by 21 publications

(10 citation statements)

References 8 publications

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“…The SKF has also been applied to solve engineering problems. For example, the SKF have employed as feature selector [30][31][32][33], algorithms in adaptive beamforming [34][35][36][37], routing algorithm in manufacturing process [38][39][40] and airport gate allocation [41], tuning algorithm in control engineering [42][43][44][45], and matching algorithm in image processing [46][47][48].…”

Section: Simulated Kalman Filtermentioning

confidence: 99%

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Azmi¹,

Ibrahim²,

Pebrianti³

et al. 2019

mekatronika

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Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, current optimum opposition-based learning (COOBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this paper intends to improve the exploration capability of SKF through the application of COOBL. The COOBL is employed after the estimation process of SKF. Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions indicate that current optimum opposition-based simulated Kalman filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all.

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Section: Simulated Kalman Filtermentioning

confidence: 99%

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Azmi¹,

Ibrahim²,

Pebrianti³

et al. 2019

mekatronika

View full text Add to dashboard Cite

show abstract

“…Other variants called parameter-less SKF and randomized SKF algorithms were proposed in [19][20]. The SKF has also been applied for real world problems like the adaptive beamforming in wireless cellular communication [21][22][23][24], airport gate allocation problem [25][26], feature selection of EEG signal [27][28], system identification [29][30], image processing [31][32], controller tuning [33], and PCB drill path optimization [34][35].…”

Section: Introductionmentioning

confidence: 99%

Single-agent Finite Impulse Response Optimizer vs Simulated Kalman Filter Optimizer

Rahman¹,

Aziz²,

Aziz³

2019

MEKATRONIKA

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Single-agent Finite Impulse Response Optimizer (SAFIRO) is a new estimation-based optimization algorithm which mimics the work procedure of the ultimate unbiased finite impulse response (UFIR) filter. In a real UFIR filter, the horizon length, N, plays an important role to obtain the optimal estimation. In SAFIRO, N represents the repetition number of estimation part that needs to be done in find-ing an optimal solution. On the other hand, Simulated Kalman Filter (SKF) is also an estimation- based optimization algorithm inspired by the estimation capability of Kalman filtering. In literature, substantial amount of works has been devoted to SKF, both in applied research and fundamental enhancements. Thus, in this paper, a performance comparison of both SAFIRO and SKF is presented. It is found that the SAFIRO outperforms the SKF significantly.

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“…Other variants called parameter-less SKF and randomized SKF algorithms were proposed in [16][17]. The SKF has also been applied for real world problems like the adaptive beamforming in wireless cellular communication [18][19][20][21], airport gate allocation problem [22][23], feature selection of EEG signal [24][25], system identification [26][27], image processing [28][29], controller tuning [30], and printed circuit board (PCB) drill path optimization [31][32].…”

Section: Introductionmentioning

confidence: 99%

Solving Assembly Sequence Planning Using Distance Evaluated Simulated Kalman Filter

Mustafa¹,

Ibrahim²,

Yusof³

et al. 2019

MEKATRONIKA

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This paper presents an implementation of simulated Kalman filter (SKF) algorithm for optimizing an assembly sequence planning (ASP) problem. The SKF search strategy contains three simple steps; predict-measure-estimate. The main objective of the ASP is to determine the sequence of component installation to shorten assembly time or save assembly costs. Initially, permutation sequence is generated to represent each agent. Each agent is then subjected to a precedence matrix constraint to produce feasible assembly sequence. In this paper, the distance evaluated SKF (DESKF) is proposed for solving ASP problem. The performance of the proposed DESKF is compared against previous works in solving ASP by applying BGSA, BPSO, and MSPSO. Using a case study of ASP, the results show that DESKF outperformed all the algorithms in obtaining the best solution.

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An Opposition-based Simulated Kalman Filter algorithm for adaptive beamforming

Cited by 21 publications

References 8 publications

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Single-agent Finite Impulse Response Optimizer vs Simulated Kalman Filter Optimizer

Solving Assembly Sequence Planning Using Distance Evaluated Simulated Kalman Filter

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