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Damage identification in three-dimensional structures using single-objective evolutionary algorithms and finite element model updating: evaluation and comparison

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ENGINEERING OPTIMIZATION 2018年第10期50卷 1695-1714页

作者： Alkayem, Nizar Faisal Cao, Maosen Hohai Univ Dept Engn Mech Nanjing Jiangsu Peoples R China

This study presents a methodology which integrates single-objective evolutionary algorithms (EAs) and finite element (FE) model updating for damage inference in three-dimensional (3D) structures. First, original well-known EAs, namely the genetic algorithm, differential evolution (DE) and particle swarm optimization (PSO), are combined with FE model updating for detecting damage in a 3D four-storey modular structure and their performances are compared. Next, to obtain more accurate results, hybrid Levy flights-DE and hybrid artificial bee colony-PSO are developed for enhancing damage identification. With each method, the objective function composed of modal strain energy and mode shape residuals, taken from the FE model of the intact structure and the simulated damage responses, is initially created. Then, the performance of each algorithm combined with FE model updating for damage detection is assessed in terms of three characteristics: consistency, computational cost and accuracy, and the best performing algorithm is recommended.

关键词： evolutionary algorithms single-objective optimization finite element model updating structural damage detection

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Preference-Inspired Co-evolutionary algorithms With Local PCA Oriented Goal Vectors for Many-Objective Optimization

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IEEE ACCESS 2018年 6卷 68701-68715页

作者： Shu, Zhe Wang, Weiping Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China

It remains a challenge to identify a satisfactory set of tradeoff solutions for many-objective optimization problems that have more than three objectives. Coevolving the solutions with preference is becoming increasingly popular due to the enhanced local search capability, which makes it suitable for solving many-objective optimization problems. The framework of preference-inspired co-evolutionary algorithms (PICEAs) is suitable for obtaining promising performance for such problems, and the PICEA with goal vectors (PICEA-g) has achieved good performance in many applications. In this paper, an improved PICEA-g is proposed to further resolve this long-standing problem. The local principal component analysis operator is used as a controller to further expand the ability of the PICEA-g algorithm and enhance the convergence of PICEA-g. The proposed algorithm was evaluated using several widely used benchmark test suites that had 3-15 objectives and made a systematic comparison with five state-of-the-art multi-objective evolutionary algorithms. The resulting substantial amount of experimental results revealed that the algorithm we proposed could have good performance on most of the test suites assessed in our research, and it performs very well compared with other many-objective optimization algorithms. In addition, a sensitivity test was carried out to explore the impact of a key parameter in the algorithm we proposed in this study.

关键词： evolutionary algorithms many-objective optimization oriented goal vectors co-evolutionary computation

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Modelling evolutionary algorithms with Stochastic Differential Equations

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evolutionary COMPUTATION 2018年第4期26卷 657-686页

作者： Heredia, Jorge Perez Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogues of the additive and multiplicative drift theorems from runtime analysis. In addition, we derive a new more general multiplicative drift theorem that also covers non-elitist EAs. This theorem simultaneously allows for positive and negative results, providing information on the algorithm's progress even when the problem cannot be optimised efficiently. Finally, we provide results for some well-known heuristics namely Random Walk (RW), Random Local Search (RLS), the (1+1) EA, the Metropolis Algorithm (MA), and the Strong Selection Weak Mutation (SSWM) algorithm.

关键词： Theory stochastic differential equations drift fixed budget evolutionary algorithms non-elitism metropolis SSWM random walk local search (1+1) EA modelling

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InDM2: Interactive Dynamic Multi-Objective Decision Making Using evolutionary algorithms

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SWARM AND evolutionary COMPUTATION 2018年 40卷 184-195页

作者： Nebro, Antonio J. Ruiz, Ana B. Barba-Gonzalez, Cristobal Garcia-Nieto, Jose Luque, Mariano Aldana-Montes, Jose F. Univ Malaga Dept Lenguajes & Ciencias Comp Ada Byron Res Bldg E-29071 Malaga Spain Univ Malaga Dept Appl Econ Math Campus El Ejido E-29071 Malaga Spain

Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of streaming data sources in the context of Big Data applications. However, approaches combining dynamic multi objective optimization with preference articulation are still scarce. In this paper, we propose a new dynamic multi-objective optimization algorithm called InDM2 that allows the preferences of the decision maker (DM) to be incorporated into the search process. When solving a dynamic multi-objective optimization problem with InDM2, the DM can not only express her/his preferences by means of one or more reference points (which define the desired region of interest), but these points can be also modified interactively. InDM2 is enhanced with methods to graphically display the different approximations of the region of interest obtained during the optimization process. In this way, the DM is able to inspect and change, in optimization time, the desired region of interest according to the information displayed. We describe the main features of InDM2 and detail how It is implemented. Its performance is illustrated using both synthetic and real-world dynamic multi-objective optimization problems.

关键词： Dynamic multi-objective optimization Multiple criteria decision making Preferences evolutionary algorithms jMetalSP

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Unassisted thresholding based on multi-objective evolutionary algorithms

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KNOWLEDGE-BASED SYSTEMS 2018年 159卷 221-232页

作者： Hinojosa, Salvador Avalos, Omar Oliva, Diego Cuevas, Erik Pajares, Gonzalo Zaldivar, Daniel Galvez, Jorge Univ Complutense Madrid Dept Ingn Software & Inteligencia Artificial Fac Informat E-28040 Madrid Spain Univ Guadalajara Div Elect & Comp CUCEI Av Revolut 1500 Guadalajara Jalisco Mexico

Multi-Objective evolutionary algorithms (MOEAs) are known to solve problems where two or more conflicting goals are involved. To accomplish it, MOEAs incorporate strategies to determinate optimal trade-offs between each of the objective functions. In this paper, an Unassisted image Thresholding (UTH) methodology is proposed based on MOEAs. UTH takes advantage of the trade-off mechanisms present on MOEAs to perform the image thresholding while simultaneously determinating the number thresholds required to segment each image and the best placement of each threshold along the histogram of the image. The image thresholding problem is commonly addressed as the search for the best possible thresholds able to partition a given image into a finite number of homogeneous classes. Such approach requires the assistance of a designer to determinate the number of threshold values that will properly segment the image. However, as images can vary significantly, the definition of an optimal number of thresholds should be performed for each image. Thus, a methodology able to determinate both the number of thresholds and the best placement of each value contributes to a general segmentation scheme. In the proposed approach, UTH redefines the thresholding problem as a multi-objective task with two conflicting goals. The first goal is the quality of the segmented image, and it is computed as a non parametric criteria to evaluate candidate threshold points. The second goal is the normalized number of threshold points. Since the number of thresholds is not fixed, a particle encoding the thresholds with variable length is used. The strategy of UTH is coupled with three MOEAs namely NSGA-III, PESA-II and MOPSO using as the non-parametric criteria the Cross Entropy. According to the results, the UTH NSGA-III formulation outperforms UTH-PESA-II and UTH-MOPSO regarding convergence and quality of the resulting image.

关键词： Multi-objective optimization evolutionary algorithms NSGA-III Cross entropy Unassisted thresholding

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An efficient two-stage method for optimal sensor placement using graph-theoretical partitioning and evolutionary algorithms

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STRUCTURAL CONTROL & HEALTH MONITORING 2019年第4期26卷 e2325-e2325页

作者： Kaveh, Ali Eslamlou, Armin Dadras Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Tehran Iran

This paper presents a two-stage optimal sensor placement method for modal identification of structures. At the first stage, using a graph theoretical technique, the structure is partitioned into equal substructures. At the second stage, a preset number of triaxial sensors are proportionately allocated to the substructures. The location of sensors is determined using an evolutionary optimization algorithm, which optimizes the triaxial modal assurance criterion of the structure. The first stage leads to the even distribution of the sensors. This stage not only improves the mode shape visualization as the secondary criterion but also accelerates the optimization process by space reduction. Here, various graph-theoretical methods including k-means, k-means++, and spectral partitioning are examined as the partitioning techniques. In addition, a dynamic version for quantum-inspired evolutionary optimization algorithm (DQEA) is proposed and applied to find the placement of triaxial sensors, along with the standard version of quantum-inspired evolutionary algorithm and genetic algorithm. In order to examine the efficiency of the methods, the bridge model of the University of Central Florida, USA, is considered as the benchmark structure. The results show that the proposed method efficiently satisfies both criteria. Moreover, the introduced optimization algorithm (DQEA) outperforms other algorithms.

关键词： evolutionary algorithms graph-theoretical partitioning mode shape visualization optimal sensor placement triaxial sensors

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Quadrature Current Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and evolutionary algorithms

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ENERGIES 2019年第4期12卷 692页

作者： Montoya, Francisco G. Alcayde, Alfredo Arrabal-Campos, Francisco M. Banos, Raul Univ Almeria Dept Engn Almeria 04120 Spain

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control the flow of electricity between the source and the load, passive or active filters are often used. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtains high quality solutions that could be generalized to similar problems of any dimension.

关键词： power factor compensation non-sinusoidal circuits geometric algebra evolutionary algorithms

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A bi-objective bi-level mathematical model for cellular manufacturing system applying evolutionary algorithms

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SCIENTIA IRANICA 2019年第4期26卷 2541-2560页

作者： Behnia, B. Mandavi, I Shirazi, B. Paydar, M. M. Mazandaran Univ Sci & Technol Dept Ind Engn POB 471668563 Babol Sar Iran Babol Noshirvani Univ Technol Dept Ind Engn POB *** Babol Sar Iran

The present study aims to design a bi-objective bi-level model for a multi-dimensional Cellular Manufacturing System (CMS). Minimization of the total number of voids and balancing of the workloads assigned to cells are regarded as two objectives at the upper level of the model. However, at the lower level, attempts are made to maximize the workers' interest to work together in a particular cell. To this end, two Nested Bi-Level metaheuristics, including Particle Swarm Optimization (NBL-PSO) and a Population-Based Simulated Annealing algorithm (NBL-PBSA), were implemented to solve the model. In addition, the goal programming approach was utilized at the upper level of these algorithms. Further, nine numerical examples were applied to verify the suggested framework, and the TOPSIS method was used to find a better algorithm. Furthermore, the best weights for upper-level objectives were tuned by using a weight sensitivity analysis. Based on computational results of all of the three objectives, when decisions about inter- and intra-cell layouts as well as cell formation were simultaneously made in order to balance the assigned workloads by considering voids and workers' interest, making the problem closer to the real world, the outcomes were found different from their ideal. Finally, NBL-PBSA could perform better than NBL-PSO, which confirmed the efficiency of the proposed framework. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： Cellular manufacturing system Bi-level programming approach Workers' interest Bi-objective optimization Goal programming evolutionary algorithms TOPSIS method

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On redundant coverage maximization in wireless visual sensor networks: evolutionary algorithms for multi-objective optimization

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APPLIED SOFT COMPUTING 2019年 82卷 105578-000页

作者： Rangel, Elivelton O. Costa, Daniel G. Loula, Angelo State Univ Feira de Santana UEFS Dept Technol BR-44036900 Feira De Santana BA Brazil State Univ Feira de Santana UEFS Dept Exact Sci BR-44036900 Feira De Santana BA Brazil

Wireless visual sensor networks can provide valuable information for a variety of monitoring and control applications. Frequently, a set of targets must be covered by visual sensors, as such visual sensing redundancy is a desired condition specially when applications have availability requirements for multiple coverage perspectives. If visual sensors become rotatable, their sensing orientations can be adjusted to optimize coverage and redundancy, bringing different challenges as there may be different coverage optimization objectives. Actually, the specific issue of redundant coverage maximization is inherently a multi-objective problem, but usual approaches are not designed accordingly to compute visual sensing redundancy. This article proposes two different evolutionary algorithms that exploit the multi-objective nature of the redundant coverage maximization problem: a lexicographic "a priori" algorithm and a NSGA-II "a posteriori" algorithm. The performance of both algorithms are compared, using a previously proposed single-objective greedy-based algorithm as a reference. Numerical results outline the benefits of employing evolutionary algorithms for adjustments of sensors' orientations, potentially benefiting deployment and management of wireless visual sensor networks for different monitoring scenarios. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Redundant coverage maximization Wireless visual sensor networks evolutionary algorithms Multi-objective optimization

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Dynamically Optimized Unstructured Grid (DOUG) for Analog Ensemble of numerical weather predictions using evolutionary algorithms

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COMPUTERS & GEOSCIENCES 2019年 133卷 104299-000页

作者： Hu, Weiming Cervone, Guido Penn State Univ Geoinformat & Earth Observat Lab Dept Geog University Pk PA 16802 USA Penn State Univ Geoinformat & Earth Observat Lab Inst CyberSci University Pk PA 16802 USA Natl Ctr Atmospher Res Res Applicat Lab POB 3000 Boulder CO 80307 USA

The Analog Ensemble is a statistical technique that generates probabilistic forecasts using a current deterministic prediction, a set of historical predictions, and the associated observations. It generates ensemble forecasts by first identifying the most similar past predictions to the current one, and then summarizing the corresponding observations. This is a computationally efficient solution for ensemble modeling because it does not require multiple numerical weather prediction simulations, but a single model realization. Despite this intrinsic computational efficiency, the required computation can grow very large because atmospheric models are routinely run with increasing resolutions. For example, the North American Mesoscale forecast system contains over 262 792 grid points to generate a 12 km prediction. The North American Mesoscale model generally uses a structured grid to represent the domain, despite the fact that certain physical changes occur non-uniformly across space and time. For example, temperature changes tend to occur more rapidly in mountains than plains. An evolutionary algorithm is proposed to dynamically and automatically learn the optimal unstructured grid pattern. This iterative evolutionary algorithm is guided by Darwinian evolutionary rule generation and instantiation to identify grid vertices. Analog computations are performed only at vertices. Therefore, minimizing the number of vertices and identifying their locations are paramount to optimizing the available computational resources, minimizing queue time, and ultimately achieving better results. The optimal unstructured grid is then reused to guide the predictions for a variety of applications like temperature and wind speed.

关键词： Computational algorithms Ensemble modeling evolutionary algorithms Unstructured grid

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