To solve the problem of large search demand and insufficient search ability of UAVs,this article has proposed a task planning method of UAV group based on nested learning *** to the characteristics of the target path,...
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To solve the problem of large search demand and insufficient search ability of UAVs,this article has proposed a task planning method of UAV group based on nested learning *** to the characteristics of the target path,the method combines task allocation strategy and path planning algorithm to make *** method can give reasonable suggestions on the number of UAVs,and effectively improve the efficiency of UAV group search *** algorithm uses the elite individual selection strategy based on tournament selection method to improve the optimization *** the algorithm uses neighborhood method to avoid local ***,the algorithm is verified by the data of a search *** experimental results show that the planning method used in this paper is suitable for the UAVs' cooperative search *** with other planning methods,it has faster solution speed and is conducive to emergency *** method also has reference value for the cooperative planning of search tasks.
The multi-microgrid network structure optimisation problem (MNSDOP) aims to minimise the circuit lengths while maximising power transmission stability, a crucial aspect for system robustness enhancement. While existin...
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The multi-microgrid network structure optimisation problem (MNSDOP) aims to minimise the circuit lengths while maximising power transmission stability, a crucial aspect for system robustness enhancement. While existing literature primarily focuses on achieving Pareto optimal solutions there is an overlooked emphasis on solution diversity. In situations like MNSDOPs, several optimal solutions might possess identical or comparable objective values, classifying them under multimodal multi-objective optimisation roblems (MMOPs). Offering an array of optimal solutions equips decision-makers with a holistic understanding of the problem, aiding in the identification of preferred solutions. Motivated by this gap, we introduce a multimodal multi-objective evolutionary algorithm tailored for MNSDOPs, termed MMO-BM. We also propose a diversity evaluation metric specifically for binary optimisation problems, substantially amplifying the exploration capabilities of evolutionary algorithms. In addition, a novel matrix-oriented DE operator is proposed to accerate the convergence process. Experimental evaluations attest to our method's prowess in yielding diverse and high-quality solutions.
Battery capacity of Unmanned Aerial Vehicle (UAV) limits its ability to perform long-term reconnaissance missions. This paper proposes a model of Mobile Charging Vehicles (MCVs) to charge UAVs on missions. In this sce...
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ISBN:
(纸本)9781665434263
Battery capacity of Unmanned Aerial Vehicle (UAV) limits its ability to perform long-term reconnaissance missions. This paper proposes a model of Mobile Charging Vehicles (MCVs) to charge UAVs on missions. In this scenario, the UAV can land on top of the MCV and move together along its original route. Scheduling and routing of MCVs are optimized to minimize the investment cost and maximize the quality of mission completion while satisfying time- and power-related constraints. For this multi-objective optimization problem, an improved decomposition-based multi-objective evolutionary algorithm (MOEA/D) is adopted to solve it. Finally, the effectiveness of this model is verified by numerical experiments.
With the wide application of lithium-ion batteries,the safety and stability of batteries have attracted much attention in recent *** capacity estimation can help understand the health status of lithium-ion ***-based m...
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With the wide application of lithium-ion batteries,the safety and stability of batteries have attracted much attention in recent *** capacity estimation can help understand the health status of lithium-ion ***-based methods can perform well on capacity estimation with enough prior ***,the internal electrochemical mechanism of lithium-ion battery degradation is so complicated that the cost of prior knowledge is usually *** to no need for prior knowledge,data-driven methods introduce new solutions to solve this *** these,deep learning methods(DL) show great potential for their advantages of high-dimensional feature *** this paper,an LSTM(Long Short-Term Memory) network model is proposed to estimate the capacity,in which seven time-related health features in the charge stage are used as input of the *** the one hand,health features extracted from charge time data can decrease the difficulties in data *** the other hand,the LSTM model has the ability to learn the long-term dependencies between data so that suit for constructing mapping relationship between health features and *** validation experiments are conducted based on two simulation *** results show that the proposed model can accurately track the capacity recovery effect and estimate the RUL of lithium-ion batteries.
Evolutionary algorithms (EAs) have been widely applied in various optimization problems. However, EAs are found as less effective on multi-modal optimization due to their multiple local optima. Inspired by the idea of...
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Evolutionary algorithms (EAs) have been widely applied in various optimization problems. However, EAs are found as less effective on multi-modal optimization due to their multiple local optima. Inspired by the idea of self-paced learning, that is, problems can be solved step by step, from easy to difficult. In this study, we propose to design a helper objective function to assist the optimization of multi-modal problems via multitask optimization framework. In principle the helper objective function shares common features with the original function but is easier to solve. Thus, the information gained by solving the helper objective can be utilized to tackle the multi-modal problems. Specifically, the Gaussian process is applied to build the helper objective function. The multi-factorial evolutionary algorithm is applied to optimize the helper and original objective functions simultaneously. Experimental results show that the idea is effective on a set of multi-modal optimization benchmarks.
Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the grap...
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Because the rotational current stabilizes the flame by creating a recirculation zone,it may increase the risk of *** this reason,low-spin combustion is used to stabilize the flame while preventing ***,in this study,th...
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Because the rotational current stabilizes the flame by creating a recirculation zone,it may increase the risk of *** this reason,low-spin combustion is used to stabilize the flame while preventing ***,in this study,the combustion flow of methane gas in a low-swirl burner is simulated using a partially premixed combustion ***,the fuel flow rate is considered *** research parameters include swirl angle(θ=35°–47°),equivalence ratio(φ=0.6–0.9)and inlet axial flow radius(R=0.6–0.7)and effect of these parameters on temperature distribution,flame length,flame rise length,velocity field,and streamlines of the number of pollutant species are *** contours of streamline,temperature distribution,and velocity distribution are also presented for analysis of flow *** results show that with increasing the fuel-air ratio,the strength of the axial flow decreases,and the position of the maximum flame temperature shifts toward the inlet of the *** results also reveal that by increasing the swirl angle of the flow,the position of the minimum velocity value(opposite to the direction of the axis)tends towards the *** results also indicate that the maximum temperature of the combustion chamber increases with increasing the swirl angle,and inθ=35°,the maximum temperature is 1711℃and inθ=41°,this value is 1812℃.Finally,by increasing the swirl angle toθ=47°,the maximum flame temperature position is found at a considerable distance from the inlet and is 1842℃.
Evolutionary algorithms (EAs) are often applied to deal with UAV route planning. The solution encoding is one of important factor in designing effective EAs. In a traditional encoding mechanism, each individual repres...
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Evolutionary algorithms (EAs) are often applied to deal with UAV route planning. The solution encoding is one of important factor in designing effective EAs. In a traditional encoding mechanism, each individual represents one route. The whole population then consists of a number of routes. We argue that such an encoding is less effective in route planning, and then proposed an alternative encoding mechanism in which one individual represents only one navigation point. The whole population then represents one route. This implicitly turns EAs into single-point based search with high exploitation ability. To further improve the exploration ability of algorithms using this new encoding, a slightly modified differential evolution operator is applied. Combining the modified DE operator and the new encoding mechanism, the performance of the derived algorithm is significantly improved, obtaining much better route planning results than DE with the traditional encoding mechanism.
This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to d...
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Nanofluids with full-spectrum absorption properties are highly desirable for direct solar thermal energy conversion *** this work,Ag and CsW03 nanofluids,which exhibit absorption both in the visible and near-infrared(...
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Nanofluids with full-spectrum absorption properties are highly desirable for direct solar thermal energy conversion *** this work,Ag and CsW03 nanofluids,which exhibit absorption both in the visible and near-infrared(NIR)region,are integrated to obtain two>component hybrid *** hybrid nanofluids show broad band absorption with a solar weighted absorption fraction of 99.6%,compared to 18%and 54%for the base liquid(ethylene glycol)and CsW03 nanofluids,*** highest photo-thermal conversion performance for the hybrid nanofluids is obtained with Ag/CsW03 weight ratio of 3/*** solar thermal conversion efficiency of the optimum hybrid nanofluids is 67%,10%and 15%higher than single Ag and CsW03 *** two-component hybrid nanofluid provides an alternative for making the best use of solar energy.
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