Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives

Zvoianu, Alexandru-Ciprian; Bramerdorfer, Gerd; Lughofer, Edwin; Silber, Siegfried; Amrhein, Wolfgang; Klement, Erich Peter

doi:10.1016/j.engappai.2013.06.002

Cited by 77 publications

(25 citation statements)

References 27 publications

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“…An application of differential evolution to constrained combinatorial problems is shown in [29]. A multi-objective genetic algorithm has been used to optimise electrical drives [30]. A gravitational search is conducted to optimise a fuzzy servo controller in [31].…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data.A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained.The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are Preprint submitted to Applied Soft Computing September 18, 2015 discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation. A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle Swarm Optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation.

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Section: Introductionmentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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“…Techniques include: fitness inheritance [10,16]; co-evolved fitness predictors [17]; fuzzy matching against an archive [18]; artificial neural networks [19][20][21]; linear and polynomial regression [22,23]; Gaussian processes or Kriging [24,25] and probabilistic distributions [26].…”

Section: Related Workmentioning

confidence: 99%

Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Brownlee

Wright

2015

Applied Soft Computing

106

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a b s t r a c tReducing building energy demand is a crucial part of the global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with NonDominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance.

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“…Finally, the classification stage is proposed to be solved by a hierarchical neural network. In Zavoianu et al (2013), MLP networks have been extensively employed in optimization of motors drives.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Bearing fault identification of three-phase induction motors bases on two current sensor strategy

et al. 2016

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Three-phase induction motors are the most commonly used devices for electromechanical energy conversion. This study proposes an alternative approach for identifying bearing faults in induction motors, using two current sensors and a pattern classifier, based on artificial neural networks. To validate the methodology, results are given from experiments carried out on a test bench where the motors operate with different types of bearing faults, under varying conditions of load torque and voltage unbalance. This paper also provides the comparative performance of neural network and random forest classifiers. This study also presents an analysis of the current signals in the time domain, applied to different neural structures.

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Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives

Cited by 77 publications

References 27 publications

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Bearing fault identification of three-phase induction motors bases on two current sensor strategy

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