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. A...
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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.
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...
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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.
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 ...
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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.
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 f...
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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.
Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate...
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Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate research, and ionosphere monitoring, further Global Navigation Satellite System (GNSS) RO missions are being followingly planned. Higher spatial and also temporal sampling rates of RO observations, achievable with higher number of GNSS/receiver satellites or optimization of the Low Earth Orbit (LEO) constellation, are being studied by high number of researches. The objective of this study is to design GNSS RO missions which provide multi-GNSS RO events (ROEs) with the optimal performance over the globe. The navigation signals from GPS, GLONASS, BDS, Galileo, and QZSS are exploited and two constellation patterns, the 2D-lattice flower constellation (2D-LFC) and the 3D-lattice flower constellation (3D-LFC), are used to develop the LEO constellations. To be more specific, two evolutionary algorithms, including the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used for searching the optimal constellation parameters. The fitness function of the evolutionary algorithms takes into account the spatio-temporal sampling rate. The optimal RO constellations are obtained for which consisting of 6-12 LEO satellites. The optimality of the LEO constellations is evaluated in terms of the number of global ROEs observed during 24 h and the coefficient value of variation (COV) representing the uniformity of the point-to-point distributions of ROEs. It is found that for a certain number of LEO satellites, the PSO algorithm generally performs better than the GA, and the optimal 2D-LFC generally outperforms the optimal 3D-LFC with respect to the uniformity of the spatial and temporal distributions of ROEs.
Multiobjective evolutionary algorithms (MOEAs) effectively solve several complex optimization problems with two or three objectives. However, when they are applied to many-objective optimization, that is, when more th...
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Multiobjective evolutionary algorithms (MOEAs) effectively solve several complex optimization problems with two or three objectives. However, when they are applied to many-objective optimization, that is, when more than three criteria are simultaneously considered, the performance of most MOEAs is severely affected. Several alternatives have been reported to reproduce the same performance level that MOEAs have achieved in problems with up to three objectives when considering problems with higher dimensions. This work briefly reviews the main search difficulties, visualization, evaluation of algorithms, and new procedures in many-objective optimization using evolutionary methods. Approaches for the development of evolutionary many-objective algorithms are classified into: (a) based on preference relations, (b) aggregation-based, (c) decomposition-based, (d) indicator-based, and (e) based on dimensionality reduction. The analysis of the reviewed works indicates the promising future of such methods, especially decomposition-based approaches;however, much still need to be done to develop more robust, faster, and predictable evolutionary many-objective algorithms. This article is categorized under: Technologies > Computational Intelligence
Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current *** proposed application is for engineering design ***/methodology/approach–T...
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Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current *** proposed application is for engineering design ***/methodology/approach–This study proposes two novel approaches which focus on faster convergence to the Pareto front(PF)while adopting the advantages of Strength Pareto evolutionary Algorithm-2(SPEA2)for better *** first method,decision variables corresponding to the optima of individual objective functions(Utopia Point)are strategically used to guide the search toward *** second method,boundary points of the PF are calculated and their decision variables are seeded to the initial ***–The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance *** evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms(such as NSGA-II and SPEA2)and recent ones such as NSLS and E-NSGA-II in most of the benchmark *** is also tested on an engineering design problem and compared with a currently used *** implications–The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives.A complex example of Welded Beam has been shown at the end of the *** implications–The algorithm would be useful for many design problems and social/industrial problems with conflicting ***/value–This paper presents two novel hybrid algorithms involving SPEA2 based on:local search;and Utopia point directed search *** concept has not been investigated before.
evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crosso...
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evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.
In this paper, examining some games, we show that classical techniques are not always effective for games with not many stages and players and it can't be claimed that these techniques of solution always obtain th...
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In this paper, examining some games, we show that classical techniques are not always effective for games with not many stages and players and it can't be claimed that these techniques of solution always obtain the optimal and actual Nash equilibrium point. For solving these problems, two evolutionary algorithms are then presented based on the population to solve general dynamic games. The first algorithm is based on the genetic algorithm and we use genetic algorithms to model the players' learning process in several models and evaluate them in terms of their convergence to the Nash Equilibrium. in the second algorithm, a Particle Swarm Intelligence Optimization (PSO) technique is presented to accelerate solutions' convergence. It is claimed that both techniques can find the actual Nash equilibrium point of the game keeping the problem's generality and without imposing any limitation on it and without being caught by the local Nash equilibrium point. The results clearly show the benefits of the proposed approach in terms of both the quality of solutions and efficiency.
An experimental comparison of evolutionary algorithms and random search algorithms for the optimal control problem is carried out. The problem is solved separately by several representatives of each type of algorithms...
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An experimental comparison of evolutionary algorithms and random search algorithms for the optimal control problem is carried out. The problem is solved separately by several representatives of each type of algorithms. The simulation is performed on a mobile robot model. The results of each algorithm performance are compared according to the best found value of the fitness function, the mean value and the standard deviation.
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