Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit *** a spacecraft is in orbit,because the solar array is exposed to t...
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Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit *** a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage *** damage makes solar array power generation unable to fully meet the energy demand of a ***,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of *** this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar *** use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.
During the operation of lithium-ion battery packs, there often exhibit certain abnormalities due to cell faults such as internal short circuit or unavoidable inconsistencies among cells, which affects the operation sa...
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In the high-stakes domain of air defense, resource allocation is a challenging optimization problem that involves many constraints and requires fast solutions. Previous work mainly addressed the single-intercept senso...
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The orienteering problem (OP) is widely applied in real life. However, as the scale of real-world problem scenarios grows quickly, traditional exact, heuristics, and learning-based methods have difficulty balancing op...
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Most multimodal multi-objective evolutionary algorithms(MMEAs)aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However,in real-world problems,decision makers(D...
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Most multimodal multi-objective evolutionary algorithms(MMEAs)aim to find all global Pareto optimal sets(PSs)for a multimodal multi-objective optimization problem(MMOP).However,in real-world problems,decision makers(DMs)may be also interested in local ***,searching for both global and local PSs is more general in view of dealing with MMOPs,which can be seen as generalized ***,most state-of-theart MMEAs exhibit poor convergence on high-dimension MMOPs and are unable to deal with constrained *** address the above issues,we present a novel multimodal multiobjective coevolutionary algorithm(Co MMEA)to better produce both global and local PSs,and simultaneously,to improve the convergence performance in dealing with high-dimension ***,the Co MMEA introduces two archives to the search process,and coevolves them simultaneously through effective knowledge *** convergence archive assists the Co MMEA to quickly approach the Pareto optimal *** knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the-dominance-based method to obtain global and local PSs *** results show that Co MMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.
Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well wi...
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Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.
Independent microgrids are crucial for supplying electricity by combining distributed energy resources and loads in scenarios like isolated islands and field combat. Fast and accurate assessments of microgrid vulnerab...
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The multi-modal orienteering problem (MM-OP) is common in real life and is to plan diverse tours that can visit as many nodes as possible with limited resources. Researchers used to adopt group intelligence algorithms...
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ISBN:
(数字)9798350329520
ISBN:
(纸本)9798350329537
The multi-modal orienteering problem (MM-OP) is common in real life and is to plan diverse tours that can visit as many nodes as possible with limited resources. Researchers used to adopt group intelligence algorithms such as evolutionary algorithms to solve MM-OP, however, show difficulty in designing complex operators and dealing with large-scale cases in real-time. This study first proposes a multi-agent deep reinforcement learning (MA-DRL) method for MM-OP. Our model deploys multiple agents, each trained to independently explore and exploit the solution space, thereby discovering diverse optimal paths. This diversity is critical in multi-modal optimization and is achieved through a unique reward-sharing mechanism implemented within the MA-DRL framework. Empirical evaluation of our model against traditional evolutionary algorithms on various test instances demonstrates its superior optimization accuracy and efficiency, particularly in larger problem sizes. Additionally, the robustness across different instance sizes underscores its great generalization ability.
This study proposed a new genetic algorithm with variable neighbourhood search (GAVNS) for UAV path planning in three-dimensional space. First, an 0–1 integer programming mathematical model is established by inspired...
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In the context of the expanding diversity of energy demands, an increasing number of heterogeneous multi-energy Microgrids (MEMGs) are engaging in the collaborative framework of the multi-energymulti-microgrid system...
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In the context of the expanding diversity of energy demands, an increasing number of heterogeneous multi-energy Microgrids (MEMGs) are engaging in the collaborative framework of the multi-energymulti-microgrid system (MEMMG). However, following this trend, the existing centralized Integrated energy Management system (IEMS) control strategy is unreliable for future energysystems, characterized by more complex optimization control and a flexible system structure. This paper introduces a hierarchical multi-agent Deep Reinforcement Learning (HMADRL) approach for distributed IEMS in MEMMG. Firstly, by employing a hierarchical approach, this method simplifies control complexity by segmenting the overarching control challenge into tasks of collaborative planning and action control, which are distributed across varied multi-agent scenes. Considering both macro and microeconomic factors, alongside carbon emissions, the optimal operation of MEMMG is realized through a bottom-up edge multi-agent control approach, in contrast to traditional top-down centralized methods. Secondly, in the phase of the inter-MEMG collaborative strategy, the Centralized Training Decentralized Execution (CTDE) framework is employed, enabling each heterogeneous MEMG to independently develop local strategies while ensuring its own privacy. Thirdly, within each MEMG, the Trust-Region (TR) model is introduced for multi-agent action control, adeptly addressing the effects of mutual exclusion in decision-making time series. Simultaneously, an initialized Hot Experience Pool (HEP) is implemented, efficiently reducing exploration in complex, high-dimensional spaces. Finally, the off-time agent model is integrated into the HMADRL environment and undergoes secondary training based on real interactions, thereby deriving the optimal energy management policy. The proposed method markedly reduces reliance on exact physical modeling systems. Comparative simulations validate the proposed control scheme's efficacy
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