Open AccessJournal Article
Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems
Zulkifli Md. Yusof,Zuwairie Ibrahim,Ismail Ibrahim,Kamil Zakwan Mohd Azmi,Nor Azlina Ab Aziz,Nor Hidayati Abd Aziz,Mohd Saberi Mohamad +6 more
TLDR
The proposed angle modulated SKF (AMSKF) is compared against two other discrete population-based optimization algorithms, namely, binary particle swarm optimization (BPSO) and binary gravitational search algorithm (BGSA), and it is found that the proposed AMSKF is as competitive as BGSA but the BPSO is superior to the both AMSKFs.Abstract:Â
Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve discrete optimization problems, the SKF algorithm is combined with an angle modulated approach. The performance of the proposed angle modulated SKF (AMSKF) is compared against two other discrete population-based optimization algorithms, namely, binary particle swarm optimization (BPSO) and binary gravitational search algorithm (BGSA). A set of traveling salesman problems are used to evaluate the performance of the proposed AMSKF. Based on the analysis of experimental results, we found that the proposed AMSKF is as competitive as BGSA but the BPSO is superior to the both AMSKF and BGSA.read more
Citations
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Journal ArticleDOI
Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.
TL;DR: The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.
Journal ArticleDOI
Adaptive Beamforming Algorithm Based on a Simulated Kalman Filter
Kelvin Lazarus,Nurul H. Noordin,Mohd Falfazli Mat Jusof,Zuwairie Ibrahim,Khairul Hamimah Abas +4 more
TL;DR: This research presents the first-time application ofSKF algorithm to adaptive beamforming, and a new modified version of the SKF algorithm named SKF with Modified Measurement (SKFMM) is introduced to further improve the exploration capabilities of SKf algorithm by modifying the measurement-update equation.
Proceedings ArticleDOI
An application of simulated Kalman filter optimization algorithm for parameter tuning in proportional-integral-derivative controllers for automatic voltage regulator system
Badaruddin Muhammad,Dwi Pebrianti,Normaniha Abdul Ghani,Nor Hidayati Abdul Aziz,Nor Azlina Ab Aziz,Mohd Saberi Mohamad,Mohd Ibrahim Shapiai,Zuwairie Ibrahim +7 more
TL;DR: This paper reports the first attempt to tune gain values in proportional-integral-derivative (PID) controllers using an optimizer called simulated Kalman filter (SKF) algorithm, a relatively new optimizer.
Parameter-less simulated kalman filter
TL;DR: Experimental results show that the parameter-less SKF managed to converge to near-optimal solution and performs as good as the original SKF algorithm.
Proceedings ArticleDOI
Feature Selection Using Binary Simulated Kalman Filter for Peak Classification of EEG Signals
Badaruddin Muhammad,Mohd Falfazli Mat Jusof,Mohd Ibrahim Shapiai,Asrul Adam,Zulkifli Md. Yusof,Kamil Zakwan Mohd Azmi,Nor Hidayati Abdul Aziz,Zuwairie Ibrahim,Norrima Mokhtar +8 more
TL;DR: Another extension of SKF algorithm, which is called binary SKF (BSKF) algorithm, is applied for the same feature selection problem and it is found that the BSKF algorithm performed slightly better than the AMSKF algorithms.
References
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Journal ArticleDOI
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