Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Var...
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Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage identification. In contrast, evolutionary algorithms (EAs) are a type of modern method for FE model updating. Structural damage identification using FE model updating by evolutionary algorithms is an active research focus in progress but lacking a comprehensive survey. In this situation, this study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based FE model updating. First, a theoretical background including the structural damage detection problem and the various types of FE model updating approaches is illustrated. Second, the various residuals between dynamic characteristics from FE model and the corresponding physical model, used for constructing the objective function for tracking damage, are summarized. Third, concerns regarding the selection of parameters for FE model updating are investigated. Fourth, the use of evolutionary algorithms to update FE models for damage detection is examined. Fifth, a case study comparing the applications of two single-objective EAs and one multi-objective EA for FE model updating-based damage detection is presented. Finally, possible research directions for utilizing evolutionary algorithm-based FE model updating to solve damage detection problems are recommended. This study should help researchers find crucial points for further exploring theories, methods, and technologies of evolutionary algorithm-based FE model updating for structural damage detection.
With the increasing uptake of electric vehicles (EVs), the need for efficient scheduling of EV charging is becoming increasingly important. A charging station operator needs to identify charging/discharging power of t...
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With the increasing uptake of electric vehicles (EVs), the need for efficient scheduling of EV charging is becoming increasingly important. A charging station operator needs to identify charging/discharging power of the client EVs over a time horizon while considering multiple objectives, such as operating costs and the peak power drawn from the grid. evolutionary algorithms (EAs) are a popular choice when faced with problems involving multiple objectives. However, since the objectives and constraints of this problem can be expressed using linear functions, it is also possible to come up with improvised multi-objective formulations which can be solved with exact techniques such as mixed-integer linear programming (MILP). With both approaches having their potential strengths and pitfalls, it is worth investigating their use to inform the algorithmic choices, which this study aims to address. In doing so, it makes a number of contributions to the topic, including extension of an existing EV charging problem to a multi-objective form;observing some interesting properties of the problem to improve both the MILP and EA solution approaches;and comparing the performance of MILP and EA. The study provides some useful insights into the problem, initial results and quantitative basis for selecting solution approaches, and highlights some areas of further development.
This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an evolutionary Algorithm (EA) based on multiple performan...
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This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production.
This paper investigates an optimized design of newly developed nonlinear controller called finite-time convergent controller to a third-order boiler-turbine dynamics. The third-order boiler-turbine dynamics only inclu...
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This paper investigates an optimized design of newly developed nonlinear controller called finite-time convergent controller to a third-order boiler-turbine dynamics. The third-order boiler-turbine dynamics only includes highly nonlinear and critical parameters of thermal plant like drum pressure, electric power and drum level;the design of controller is always a complicated task. The present work explores the possibility of application of newly developed finite-time convergent controller to a third-order boiler-turbine dynamics. The novelty of the newly developed finite-time convergent controller is complete utilization of system nonlinearities instead of cancelling or linearizing them. Also, the finite-time convergent controller ensures robustness and fast convergence. To achieve optimal performance, the tuning parameters involved in finite-time convergent controller have been optimized using evolutionary algorithm techniques. To validate the control performance of an optimized nonlinear controller design, simulations have been conducted using various evolutionary algorithm techniques and the results are reported as various case studies. To compare the performance of proposed optimized finite-time convergent controller, the fuzzy logic controller has also been designed using ANFIS for boiler-turbine system and the results are reported.
This study contributes a detailed assessment of how increasing problem sizes (measured in terms of the number of decision variables being considered) impacts the computational complexity of using multiple objective ev...
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This study contributes a detailed assessment of how increasing problem sizes (measured in terms of the number of decision variables being considered) impacts the computational complexity of using multiple objective evolutionary algorithms (MOEAs) to solve long-term groundwater monitoring (LTM) applications. The epsilon-dominance non-dominated sorted genetic algorithm II (epsilon-NSGAII), which has been shown to be an efficient and reliable MOEA, was chosen for the computational scaling study. Four design objectives were chosen for the analysis: (i) sampling cost, (ii) contaminant concentration estimation error, (iii) local uncertainty, and (iv) contaminant mass estimation error. The true Pareto-optimal solution set was generated for 18-25 well LTM test cases in order to provide for rigorous algorithm performance assessment for problems of increasing size. Results of the study indicate that the epsilon-NSGAII exhibits quadratic computational scaling with increasing LTM problem size. However, if the user is willing to accept an approximation to the Pareto-optimal solution set, F-dominance can be used to reduce the computational scaling of MOEAs to be linear with increasing problem sizes. This study provides a basis for advancing the size and scope of water resources problems that can be effectively solved using MOEAs. (C) 2006 Published by Elsevier Ltd.
This paper presents an adaptive selection scheme for use in evolutionary algorithms (EAs). The proposed algorithm adjusts the stochastic noise level in the determination of the mating pool in order to regulate the sel...
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This paper presents an adaptive selection scheme for use in evolutionary algorithms (EAs). The proposed algorithm adjusts the stochastic noise level in the determination of the mating pool in order to regulate the selection pressure. This eliminates the fitness scaling problem and allows optimization of the selection pressure throughout the learning phase, overcoming the major pitfalls of most popular EA selection procedures. Experimental evidence is given to prove the superior performance of the proposed technique compared with conventional EA procedures. The results also highlight how the application of windowing techniques to the roulette wheel procedure can increase the likelihood of premature convergence.
This article is concerned with the optimal use of metamodels in the context of multi-objective evolutionary algorithms which are based on computationally expensive function evaluations. The goal is to capture Pareto f...
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This article is concerned with the optimal use of metamodels in the context of multi-objective evolutionary algorithms which are based on computationally expensive function evaluations. The goal is to capture Pareto fronts of optimal solutions with the minimum computational cost. In each generation during the evolution, the metamodels act as filters that distinguish the most promising individuals, which will solely undergo exact and costly evaluations. By means of the so-called inexact pre-evaluation phase, based on continuously updated local metamodels, most of the non-promising individuals are put aside without aggravating the overall cost. The gain achieved through this technique is amazing in single-objective problems. However, with more than one objective, noticeable performance degradation occurs. This article scrutinizes the role of metamodels in multi-objective evolutionary algorithms and proposes ways to overcome expected weaknesses and improve their performance. Minimization of mathematical functions as well as aerodynamic shape optimization problems are used for demonstration purposes.
Numerous parallel and distributed evolutionary algorithms (PDEAs) and their implementations have been proposed and are available on the Web. A robust approach to make easier their code and design reuse is the framewor...
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Numerous parallel and distributed evolutionary algorithms (PDEAs) and their implementations have been proposed and are available on the Web. A robust approach to make easier their code and design reuse is the framework approach. In this paper, we present some existing frameworks for PDEAs and their development requirements, and propose a new C++ open source framework, named Parallel and distributed Evolving Objects (ParadisEO). ParadisEO is basically devoted to the reusable and flexible design of parallel and distributed metaheuristics, but we focus here only on PDEAs. Compared to other related frameworks, ParadisEO allows more reuse flexibility, and provides more implemented parallel and distributed models. Furthermore, these models can be exploited by the user in a transparent way, and deployed as well on shared memory multi-processors as on distributed memory machines. The architecture has been experimented on two real-world applications: the radio network design and the spectroscopic data mining. The experimental results demonstrate the efficiency and robustness of the different models. (C) 2004 Elsevier B.V. All rights reserved.
The general distillation sequence synthesis problem featuring the separation of multicomponent feed streams into multicomponent products is addressed. Potential flowsheets include stream bypassing and mixing and use s...
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The general distillation sequence synthesis problem featuring the separation of multicomponent feed streams into multicomponent products is addressed. Potential flowsheets include stream bypassing and mixing and use sharp separations as well as non-sharp splits where key component distribution is allowed. Compared to conventional sharp distillation sequence synthesis, this leads to a mixed-integer non-linear programming problem of increased complexity, including non-convexities as well as multi-modalities. Product specifications create additional constraints while simultaneously call for a rigorous modeling of the non-key distribution. A synthesis method is proposed that models the various flowsheet configurations with a new and flexible superstructure concept and connects the gradient-free optimization technique of application-orientedly developed evolutionary algorithms (EAs) to the rigorous modeling capabilities of the Aspen plus (TM) simulation system, thus enabling realistic process design and cost objective function calculation. The re-examination of two published examples illustrates the applicability and the potential of the approach. (C) 2007 Elsevier Ltd. All rights reserved.
The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algor...
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The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool.
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