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

Azmi, Kamil Zakwan Mohd; Ibrahim, Zuwairie; Pebrianti, Dwi; Jusof, Mohd Falfazli Mat; Aziz, Nor Hidayati Abdul; Aziz, Nor Azlina Ab.

doi:10.15282/mekatronika.v1i1.157

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…In this algorithm, the formation of the opposite population uses the best-so-far solution as the center between the estimate population and the opposition estimate population. 12…”

Section: Opposition-based Learningmentioning

confidence: 99%

A brief review of simulated Kalman Filter Algorithm – variants and applications

et al. 2021

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Simulated Kalman Filter (SKF) solves optimization problems by finding the estimate of the optimum solution. As a multi-agent algorithm, every agent in the population acts as a Kalman filter by using a standard Kalman filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. This paper presented an overview of the research progress in SKF from the day it was introduced until the present day, discussing the progress, improvements, modifications, and applications of SKF. The fundamental and standard algorithm were first introduced. Then, the work on the algorithm improvements was surveyed. Finally, the remaining unresolved problems and some directions of SKF research were discussed. We reviewed 57 SKF papers. 16 of them on fundamental improvements, 9 on extension of the algorithm to discrete problems and 25 on their applications. Researchers have worked on ideas to improve exploration capability to prevent premature convergence by trying prediction operators, opposition-based learning, and different iteration strategies. There were also attempts to hybridize SKF with other famous algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Sine Cosine Algorithm (SCA) to improve its performance. Lastly, a single-agent variant of SKF and a multi-objective SKF were introduced. SKF algorithms and its variants have been implemented in at least nine areas of applications: drill path optimization, airport gate allocation problem (AGAP), assembly sequence planning (ASP), system identification, feature selection, image template matching, controller tuning, wireless sensor network, and engineering design problem. The literature reviewed solely depended on the keyword search that contained the terms simulated Kalman filter from December 2015 to the present date. This is the first review paper on SKF. It is hoped that this survey would be beneficial for the researchers of this area and attracting interest towards the algorithm.

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“…In this algorithm, the formation of the opposite population uses the best-so-far solution as the center between the estimate population and the opposition estimate population. 12…”

Section: Opposition-based Learningmentioning

confidence: 99%

A brief review of simulated Kalman Filter Algorithm – variants and applications

et al. 2021

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“…Hybridization of SKF with particle swarm optimization (PSO), gravitational search algorithm (GSA), and opposition-based learning [11][12][13][14][15][16] have also been proposed for better performance. Other variants called parameter-less SKF and randomized SKF algorithms were proposed in [17][18]. The SKF has also been applied for real world problems like the adaptive beamforming in wireless cellular communication [19][20][21][22], airport gate allocation problem [23][24], feature selection of EEG signal [25][26], system identification [27][28], image processing [29][30], assembly sequence planning [31], controller tuning [32], and PCB drill path optimization [33][34].…”

Section: Introductionmentioning

confidence: 99%

Fitness-evaluated Adaptive Switching Simulated Kalman Filter Algorithm with Randomness

Aziz¹,

Rahman²,

Aziz³

2019

MEKATRONIKA

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The original Simulated Kalman Filter (SKF) is an optimizer that employs synchronous update mechanism. The agents in SKF update their solutions after all fitness calculations, prediction process, and measurement process are completed. An alternative to synchronous update is asynchronous update. In asynchronous update, only one agent does fitness calculation, prediction, measurement, and estimation processes at one time. A recent study shows that the asynchronous SKF outperforms synchronous SKF. In this study, synchronous and asynchronous mechanisms are combined in SKF. At first, the SKF starts with either synchronous or asynchronous update. By evaluating the fitness, if no improved solution is found, the SKF changes its update mechanism. The decision to switch from synchronous to asynchronous or vice versa is made randomly. Using the CEC2014 benchmark test suite, experimental results indicate that the proposed adaptive switching SKF randomness outperforms the original SKF algorithm.

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“…The SKF also has been extended for binary optimization problems [7] and combinatorial optimization problems [8][9][10]. Hybridization of SKF with particle swarm optimization (PSO), gravitational search algorithm (GSA), and opposition-based learning [11][12][13][14][15][16][17] have also been proposed for better performance. Other variants called parameter-less SKF and randomized SKF algorithms were proposed in [18][19].…”

Section: Introductionmentioning

confidence: 99%

A Tutorial on Population-based Simulated Kalman Filter

Aziz¹,

Aziz²,

Muhammad³

et al. 2019

MEKATRONIKA

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Simulated Kalman Filter (SKF) is an estimation-based optimization algorithm which is established based on the Kalman filtering framework. Even since the SKF algorithm is introduced in 2015, there is no tutorial been published on SKF. One may find that the equations and flowchart of the algorithm is not easy to understand. Hence, this paper provides a tutorial on SKF algorithm that emphasizes on a numerical example for easy and intuitive explanations. This tutorial would be important to those who work on the fundamentals and applications of SKF as well as to students who are new to optimization research.

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

Cited by 6 publications

References 26 publications

A brief review of simulated Kalman Filter Algorithm – variants and applications

A brief review of simulated Kalman Filter Algorithm – variants and applications

Fitness-evaluated Adaptive Switching Simulated Kalman Filter Algorithm with Randomness

A Tutorial on Population-based Simulated Kalman Filter

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