Metaheuristics – Global Methods

Ștefănoiu, Dan; Borne, Pierre; Popescu, Dumitru; Filip, Florin Gheorghe; Kamel, Abdelkader El

doi:10.1002/9781118648766.ch2

Cited by 3 publications

(8 citation statements)

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“…The full identification of ARX-ARMA models (27) and (31) requires solving a granular optimization problem [ 25 ], as the structural indices are unknown. In order to find the optimal structure of ARX-ARMA models, two approaches are considered, as explained next.…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, in order to solve the SNR maximization problem, exact optimization methods [ 27 ] are useless. Hopefully, this type of problems can be solved by means of evolutionary computing techniques, referred to as metaheuristics [ 24 , 25 ]. Such a metaheuristic optimization technique is based on the classical Hill Climbing Algorithm (HCA).…”

Section: Methodsmentioning

confidence: 99%

“…In the original version, the HCA was introduced as a local metaheuristic [ 24 , 25 ]. This means the search for the optimum is only performed in a reduced subset of search space, where the global optimum is very likely to lie.…”

Section: Methodsmentioning

confidence: 99%

“…Since the SNR usually exhibits a fractal shape (in context of data acquired from physical processes), the optimal structure of each model is determined by means of a metaheuristic. In this respect, the paper introduces an advanced version of the classical Hill Climbing Algorithm ( HCA ), by using groups of climbers [ 24 , 25 ]. Although, nowadays, there is a trend in SI to adhere to non-conventional models (e.g., Long Short Term Memory (LSTM) in [ 26 ]), the performed simulations prove that the classical ARMAX model returns good results as well, especially in the context of a spline model.…”

Section: Introductionmentioning

confidence: 99%

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Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data

Ștefănoiu

Culita

2020

Sensors

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This article introduces the spline approximation concept, in the context of system identification, aiming to obtain useful autoregressive models of reduced order. Models with a small number of poles are extremely useful in real time control applications, since the corresponding regulators are easier to design and implement. The main goal here is to compare the identification models complexity when using two types of experimental data: raw (affected by noises mainly produced by sensors) and smoothed. The smoothing of raw data is performed through a least squares optimal stochastic cubic spline model. The consecutive data points necessary to build each polynomial of spline model are adaptively selected, depending on the raw data behavior. In order to estimate the best identification model (of ARMAX class), two optimization strategies are considered: a two-step one (which provides first an optimal useful model and then an optimal noise model) and a global one (which builds the optimal useful and noise models at once). The criteria to optimize rely on the signal‑to‑noise ratio, estimated both for identification and validation data. Since the optimization criteria usually are irregular in nature, a metaheuristic (namely the advanced hill climbing algorithm) is employed to search for the model optimal structure. The case study described in the end of the article is concerned with a real plant with nonlinear behavior, which provides noisy acquired data. The simulation results prove that, when using smoothed data, the optimal useful models have significantly less poles than when using raw data, which justifies building cubic spline approximation models prior to autoregressive identification.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data

Ștefănoiu

Culita

2020

Sensors

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“…Reliable techniques are necessary to manage construction project information flows as well as to reduce uncertainties and risks when solving technological and economic challenges in construction with effective decision support [2,12,24]. Analysing possibilities of application of automated design and information management [17], the need for delivering mutual information in a timely manner between all the responsible and competent participants, data archiving and working in a common area with a unified computer-aided design system tools is emphasized [1,26,27].…”

Section: Introductionmentioning

confidence: 99%

Application of MCDM and BIM for Evaluation of Asset Redevelopment Solutions

Pavlovskis¹,

Antuchevičienė²,

Migilinskas³

2016

SIC

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Abstract:The current paper analyses the application of Multiple Criteria Decision Making (MCDM) and Building Information Modelling (BIM) techniques for integrated information management when assessing redevelopment solutions of former industrial buildings with emphasis on sustainable development. The theoretical approach of complex decisionmaking model for asset redevelopment is proposed and practical case study of old equipment factory redevelopment is presented. Sixteen criteria combined in three groups as economic, environmental and technological indicators are analysed both with ranking of possible redevelopment alternatives of the building using Weighted Aggregated Sum Product Assessment method with grey attributes scores (WASPAS-G). The selection of the most rational redevelopment solution for analysed projects is also supported by BIM techniques and allows implementation of whole project life-cycle management strategy in real life.Keywords: information management, sustainable asset redevelopment, MCDM, WASPAS-G, BIM, building life cycle. 293useful for handling numerous information and for decision support [11,28,29]. Uncertainty of information and risk level are the largest in the beginning of construction project, while their influence decline to an acceptable level in the elaboration of the information in the course of the project, i.e. adjusting the data over the entire building lifecycle. Therefore, the most important is to manage and control the information at the beginning of a project and to select the most effective solution.Accordingly, the aim of the current research is to suggest application of MCDM and BIM techniques for integrated information management, when selecting and implementing the most effective sustainable asset redevelopment solution. Integrated decisionmaking model, emphasizing interconnections between the techniques, is presented in Chapter 2. Due to high level of uncertainty, using Weighted Aggregated Sum Product Assessment method with grey numbers is suggested. A case study of abandoned industrial building redevelopment, involving 3D modelling and multiple criteria evaluation, is presented in Chapter 3.The outcomes of the study revealed that the suggested information management and decision-making model is a reliable technique for project selection and management in uncertain environment. The objectives of the study were effectively attained by determining the priority order of the analysed asset redevelopment solutions and making it possible to implement the selected project using Digital Construction techniques. Methodology for Evaluation of Asset Redevelopment Solutions Asset management decision-making using MCDM and BIM techniquesThis specific subtopic of the implementation of new Digital Construction techniques (like geospatial GIS, 3D Laser Scan and BIM) was found as new research field partially analysed in some scientific works [1,15]. The best practice, knowledge, experience and "knowhow" [26,27] leads world to new approach of connecting traditional survey methods with ...

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Main Metaheuristics Used for the Optimization of the Control of the Complex Systems

Borne

Gharbi

2019

Computational Intelligence and Optimization Methods for Control Engineering

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Metaheuristics – Global Methods

Cited by 3 publications

References 0 publications

Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data

Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data

Application of MCDM and BIM for Evaluation of Asset Redevelopment Solutions

Main Metaheuristics Used for the Optimization of the Control of the Complex Systems

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