Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare, Akhilesh; Bhattacharjee, Sankha Subhra; Patidar, Rohan; George, Nithin V.

doi:10.1016/j.swevo.2016.06.007

Cited by 73 publications

(28 citation statements)

References 304 publications

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“…These include genetic algorithms (GA) (Affenzeller et al, 2009), related multiobjective approaches (Coello, 2006;Zhou et al, 2011), particle swarm optimization (PSO: Parsopoulos & Vrahatis, 2010) and the recently proposed differential evolution approach (Das & Suganthan, 2011). Gotmare, Bhattacharjee, Patidar & George (2016) discuss the suitability of GAs for various optimisation problems in system identification and filter design. Yao & Sethares (1994) and Nyarko & Scitovski (2004) provide similar in the context of parameter estimation more specifically.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

Montazeri

West

Monk

et al. 2016

International Journal of Control

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This article concerns the problem of dynamic modeling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model orientated research using the same machine, the article develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimize the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivization of an output error single performance index. The developed algorithm utilises a multi-objective Genetic Algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivization has improved convergence of the estimated parameters compared to the single objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

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Section: Introductionmentioning

confidence: 99%

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

Montazeri

West

Monk

et al. 2016

International Journal of Control

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“…Results of the genetic algorithm as it steps through each generation in optimizing a PID control variables. 7 An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control DOI: http://dx.doi.org /10.5772/intechopen.89637 Let us look again at the paper presented by Biesbroek [39], they look at a parameter space of only two in the application of optimizing the trajectory of a rocket such that a maximum horizontal distance, x, is achieved. The specific parameters are V m s ÀÁ & mk g ðÞ , where V = velocity in meters per second, and m = mass in kilograms.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Each particle's position within the search space (searching for the optimum value) is calculated with a corresponding velocity as defined by Eq. (7). v n t ðÞ¼ω Â v n t À 1 ðÞ þ a 1 r 1 p B n t À 1 ðÞ À x n t À 1 ðÞ þ a 2 r 2 g B n t À 1 ðÞ À x n t À 1 ðÞ (7) where v n is the particle velocity in the next time step, a 1 and a 2 are constant weights on the effect due to social evolution, p B n is the individual particle's best result so far, g B n is the population's best result so far, ω is the particle's inertia, r 1 and r 2 are random numbers normally distributed from 0 to 1 to keep the random nature in the algorithm.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Evolutionary algorithm use has been steadily increasing in the number of published papers corresponding to an increasing number of applications over the past 20 years [1][2][3][4][5][6][7][8]. Originating as an alternative to traditional mathematical optimization techniques, the techniques now span across almost every discipline to include data compression, traveling salesmen, image processing, and more importantly for spacecraft: control theory [9][10][11][12][13], system identification [14 -19], and trajectory optimization [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Cooper¹,

Smeresky²

2020

Advances in Spacecraft Attitude Control

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Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

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“…GWO, one of the recent nature-inspired algorithms, was applied successfully for the solutions of lot of engineering problems from Electrical Engineering to machine learning. Training multilayer percepteron [12], optimal reactive power dispatch [13], economic emission dispatch [14], classification [15], optimal power flow [16], hyperspectral band selection [17], energy loss minimization [18], system identification and filter design [19], feature selection [20] are some of the engineering applications using GWO.…”

Section: Introductionmentioning

confidence: 99%

A New Solution Approach for Non-Linear Equation Systems with Grey Wolf Optimizer

Erdoğmuş

2018

Sakarya University Journal of Computer and Information Sciences

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The aim of this study is to bring a new perspective for the solutions of non-linear equation systems. So this study handles the non-linear equation systems as a constrained optimization problem, while generally is handled unconstrained optimization problem or multi objective optimization problem. The object is to minimize the sum of the squares of nonlinear equations under the nonlinear equality constraints. A recently developed heuristic optimization algorithm called Grey Wolf Optimizer (GWO) is proposed for the solution of nonlinear equation systems. Two results were obtained. Firstly, it has been seen that GWO can be an alternative solution technique for the solution of nonlinear equation systems. Secondly, modelling the systems of nonlinear equations as constrained optimization gives better results.

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Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Cited by 73 publications

References 304 publications

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

A New Solution Approach for Non-Linear Equation Systems with Grey Wolf Optimizer

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