A simulation-driven surrogate parallel improved adaptive genetic algorithm (SDS-IAGA) method is proposed. This method aims to improve the efficiency of topology optimization for the automated design of antenna. The op...
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A simulation-driven surrogate parallel improved adaptive genetic algorithm (SDS-IAGA) method is proposed. This method aims to improve the efficiency of topology optimization for the automated design of antenna. The optimization process involves two stages: initialization, population screening and algorithm application. In the first stage, a coarse-mesh electromagnetic (EM) simulation model combined with a current-driven search is utilized to provide a high-quality initial population. In the second stage, variable-fidelity surrogate and correction technology assist the IAGA in optimizing the antenna topology. During this stage, the IAGA uses new adaptive crossover and mutation operators based on nonlinear improvement to enhance the efficiency in reaching the target solution. To verify the efficacy of the proposed SDS-IAGA, the design task of a planar tri-band antenna with center frequencies at 2.45 GHz/3.5 GHz/5.8 GHz is completed. The experimental results demonstrate that, compared to AGA and IAGA, the SDS-IAGA enhances the optimization efficiency of antenna topology by 62.97% and 54.22%, respectively. Furthermore, compared to existing optimization methods, SDS-IAGA can complete the target design task with fewer full-wave EM simulations.
This letter investigates reconfigurable intelligent surface (RIS)-assisted autonomous aerial vehicle (AAV) downlink networks with fluid antennas (FA), where RIS enables non-line-of-sight (NLoS) transmissions. Moreover...
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This letter investigates reconfigurable intelligent surface (RIS)-assisted autonomous aerial vehicle (AAV) downlink networks with fluid antennas (FA), where RIS enables non-line-of-sight (NLoS) transmissions. Moreover, the FA is equipped on the AAV offering dynamic antenna position adjustment, enhancing spatial diversity besides AAV deployment. We aim at total downlink rate maximization while ensuring minimum user rate requirement. We consider joint optimization of active AAV beamforming, passive RIS beamforming, AAV deployment and FA position adjustment. To address the complex problem, we propose beamfomring for RIS/AAV and FA-AAV deployment (BRAUD) scheme by employing alternative optimization, successive convex approximation (SCA) and sequential rank-one constraint relaxation (SROCR) method for the decomposed subproblems. Simulation results demonstrate the effectiveness of RIS-FA-AAV, achieving the highest rate among existing architectures without FA/AAV/RIS deployment and without proper beamforming.
In this article, we introduce a convex combination-based distributed momentum method (CDM) for solving distributed optimization to minimize a sum of smooth and strongly convex local objective functions over directed g...
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In this article, we introduce a convex combination-based distributed momentum method (CDM) for solving distributed optimization to minimize a sum of smooth and strongly convex local objective functions over directed graphs. The proposed method integrates the convex combination, row- and column-stochastic weights, and the adapt-then-combination rule. By selecting different parameters, it can be reduced to other distributed momentum methods, such as the parametric distributed momentum. CDM converges to the optimal solution at a global R-linear rate for any smooth and strongly convex function when the step-size and momentum coefficient satisfy some bounded conditions. Numerical results for some distributed optimization problems demonstrate that CDM yields a performance that is superior to that of the state-of-the-art methods.
Multivariate time series (MTS) clustering has become a critical research area. Current methods typically rely on space projection or representation learning for clustering but tend to overlook the significance and con...
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Multivariate time series (MTS) clustering has become a critical research area. Current methods typically rely on space projection or representation learning for clustering but tend to overlook the significance and contribution of MTS dimensions, leading to a failure in accurately modeling the intricate correlations and dependencies among dimensions. Meanwhile, the lack of adaptive regulation for MTS dimensions in distance measures significantly impacts clustering accuracy. In view of these issues, we propose a data-adaptive dynamic time warping (DTW) based fuzzy clustering method for MTS. This method utilizes locally weighted DTW as the kernel distance measure, enabling the adaptive regulation of MTS dimensions. To address the non-convex optimization problem associated with DTW-based clustering, we formulate a comprehensive objective function and present an efficient optimization method based on closed-form solutions. This unsupervised learning method significantly improves the precision of DTW, leading to more accurate and interpretable clustering outcomes. Extensive experiments conducted on eight public datasets, along with comparisons to 10 benchmark methods, demonstrate the competitive performance of our method in terms of both accuracy and efficiency.
When railway line sections are partially disrupted due to damage from large-scale natural disasters, it is necessary to prepare crew schedule plans for temporary timetables. In such a case, a crew schedule plan ensure...
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This article aims to establish the fundamental notions on which stability analysis in optimization is based. It is a topic of singular importance in the analysis of any optimization problem, since the lack of stabilit...
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This article is devoted to the study of discrete potentials on the sphere in Rn for sharp codes. We show that the potentials of most of the known sharp codes attain the universal lower bounds for polarization for sphe...
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This article is devoted to the study of discrete potentials on the sphere in Rn for sharp codes. We show that the potentials of most of the known sharp codes attain the universal lower bounds for polarization for spherical tau-designs previously derived by the authors, where ``universal'' is meant in the sense of applying to a large class of potentials that includes absolutely monotone functions of inner products. We also extend our universal bounds to T-designs and the associated polynomial subspaces determined by the vanishing moments of spherical configurations and thus obtain the minima for the icosahedron, dodecahedron, and sharp codes coming from E8 and the Leech lattice. For this purpose, we investigate quadrature formulas for certain subspaces of Gegenbauer polynomials P-j((n)) which we call PULB subspaces, particularly those having basis {P-j((n))}(j=0)(2k+2)\{P-2k((n))}. Furthermore, for potentials with h((tau+1))<0 we prove that the strong sharp codes and the antipodal sharp codes attain the universal bounds and their minima occur at points of the codes. The same phenomenon is established for the 600-cell when the potential h satisfies h((i) )>= 0, i=1,& mldr;,15, and h((16))<= 0.
Background The French West Indies are facing increasing rates of obesity and diet-related chronic diseases. Food prices are more than 30% higher compared with mainland France, while a large part of the population is s...
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Background The French West Indies are facing increasing rates of obesity and diet-related chronic diseases. Food prices are more than 30% higher compared with mainland France, while a large part of the population is socioeconomically disadvantaged. The affordability of a healthy diet is a key issue. Objective To identify dietary shifts allowing to achieve nutritional adequacy while reducing the cost of Guadeloupean and Martinican adult diets. Methods Dietary intakes of 1112 adults (>= 16y) were obtained from a cross-sectional survey conducted on a representative sample of the Guadeloupean and Martinican populations. Diet cost was based on mean prices of 1357 foods compiled from a Martinican supermarket website. Individual optimized diets respecting all nutritional recommendations with minimized departure from the initial diet were designed under different scenarios of cost constraint: none, not exceeding the initial diet cost (COSTinit), and 10%-step reductions (COST-X%);the initial diet cost referring to the cost of the diet based on initial dietary intakes and mean food prices. Results Without cost constraint, achieving nutritional adequacy while departing the least from initial diet increased diet cost on average (+ 20%) and for 74% of adults. In COSTinit, achieving nutritional adequacy was possible for 98% of adults and induced an increase in the amount of fruit & vegetables, unrefined starches, dairy products (especially milk), eggs and vegetable fats, and a decrease in sweetened beverages (especially among < 30y), refined cereals, sweetened products, meat and *** COST-30% scenario, achieving nutritional adequacy was possible for 93% of adults and induced the same dietary shifts as in COSTinit, but modified their magnitude, notably a smaller increase of vegetables (increase of + 7 g/d in the COST-30% scenario and + 86 g/d in the COSTinit scenario, both relative to initial diet), a larger increase of dairy (+ 90 g/d and + 72 g/d, respectively) and starchy fo
Genotyping provides breeders with new information at the single nucleotide polymorphism level that can be used in mating programmes. This study used stochastic simulation to explore the long-term effects of genomic ma...
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Genotyping provides breeders with new information at the single nucleotide polymorphism level that can be used in mating programmes. This study used stochastic simulation to explore the long-term effects of genomic mating allocations combining economic scores and linear programming at the level of commercial herds. The economic scores included genetic level, a favourable monogenic trait (polledness), a recessive genetic defect, and parent relationships. The results showed that compared with only maximising genetic level, including genomic or pedigree relationship in the economic score lowered the rate of pedigree and genomic inbreeding with minimal effect on genetic gain. Including the cost of a recessive genetic defect in the score almost eliminated the risk of expression. We set the start allele frequency of polledness to similar to 12%, and the value of polledness varied in the different scenarios (0, 10, 50, and 100). Including an economic value for polledness of (>= 50) in the economic score increased the frequency of polled animals by up to 0.037 per generation, without negatively impacting other comparison criteria. The use of genomic relationships was favourable for the rate of genomic inbreeding and performed as well as pedigree relationships concerning the rate of pedigree inbreeding. Limiting the number of females per bull and herd to a maximum of 5% instead of 10% also decreased the rate of inbreeding. The 5% females per bull and herd constraint lowered the variation in carrier frequency for genetic defects, which reduced the risk of mating two carriers of an unknown genetic defect in future generations after the widespread use of carriers in previous generations. However, the 10% females per bull constraint accelerated the increase in the polled allele. Therefore, planning matings with genomic information at the herd level involves important risk management decisions, such as balancing the trade-off between using fewer bull
An effective evaluation method for analyzing the actual environmental multi-index systems is an important task. However, the inherent complexity and interrelatedness of environmental factors pose substantial challenge...
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An effective evaluation method for analyzing the actual environmental multi-index systems is an important task. However, the inherent complexity and interrelatedness of environmental factors pose substantial challenges to this task. Many methods have been proposed and verified to solve this problem. However, unreasonable weight determination and sole single-index assessments limit the practical application of these analysis methods. In this study, we propose an optimally improved entropy weight calculation method (OIEW) that combines mathematical programming with the principle of consistency in entropy weight variation to determine the weight of each index. The simulation results demonstrate that our proposed method enhances robustness against extreme data while also effectively mitigating the over-correction of normal data during the weight determination process. Furthermore, by combining the OIEW method with the fuzzy comprehensive evaluation (FCE) method, the OIEW-FCE approach can be utilized to evaluate soil quality grades. Soil data from ten Chinese provinces are selected as the research specimens in this paper. An evaluation system for soil physical and chemical properties is developed, comprising two first-grade indices and six second-grade indices. The evaluation results show that the OIEW-FCE method significantly reduced the overall error in soil grade evaluation by approximately 20% compared to the improved entropy weight evaluation system utilizing the FCE and the OIEW methods employing the single factor evaluation method. This result indicates that our evaluation system can maintain accuracy and reliability in practical applications. Our method quantitatively assesses performance deviations from actual soil usage scenarios and has potential applications in relevant fields, such as environmental impact assessment, ecological resource management, and sustainable development planning.
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