With the rapid development of 5G technology and the swift growth in the number of heterogeneous devices in the Electric Power Internet of Things (EP-IoT), the issue of communication resource allocation in 5G EP-IoT sy...
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Wireless sensor networks (WSNs) are crucial for monitoring events, but limited energy from sensor nodes reduces network lifetime. Hierarchical routing, particularly clustering, effectively manages energy consumption a...
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This paper investigates projection-free algorithms for stochastic constrained multi-level optimization. In this context, the objective function is a nested composition of several smooth functions, and the decision set...
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This paper investigates projection-free algorithms for stochastic constrained multi-level optimization. In this context, the objective function is a nested composition of several smooth functions, and the decision set is closed and convex. Existing projection-free algorithms for solving this problem suffer from two limitations: 1) they solely focus on the gradient mapping criterion and fail to match the optimal sample complexities in unconstrained settings;2) their analysis is exclusively applicable to non-convex functions, without considering convex and strongly convex objectives. To address these issues, we introduce novel projection-free variance reduction algorithms and analyze their complexities under different criteria. For gradient mapping, our complexities improve existing results and match the optimal rates for unconstrained problems. For the widely-used Frank-Wolfe gap criterion, we provide theoretical guarantees that align with those for single-level problems. Additionally, by using a stage-wise adaptation, we further obtain complexities for convex and strongly convex functions. Finally, numerical experiments on different tasks demonstrate the effectiveness of our methods. Copyright 2024 by the author(s)
This work treats parametric optimization of nonlinear systems using genetic algorithms. The authors focus, in this paper, in combining the ARX-Laguerre model and the multimodel approach with its coupled configuration ...
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This paper proposes an oversampling-guided search framework to improve the search efficiency of multiobjective evolutionary algorithms (MOEAs) for high-dimensional prob-lems. The proposed algorithm uses an oversamplin...
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In view of the limitations of the traditional simulated annealing algorithm in the research of network information security situational awareness optimization method, a research scheme of security situational awarenes...
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ISBN:
(纸本)9798350361537
In view of the limitations of the traditional simulated annealing algorithm in the research of network information security situational awareness optimization method, a research scheme of security situational awareness optimization method based on decision tree algorithm is proposed. Firstly, the influencing factors is accurately located through probability theory, and the indicators is reasonably divided to reduce interference, and the decision tree algorithm is used to construct a research scheme of security situational awareness optimization method. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional simulated annealing algorithm in terms of research accuracy and processing time of influencing factors, and has obvious advantages. The research on security situational awareness optimization method plays an extremely important role in network information, which can accurately predict and optimize the growth characteristics and product generation of network information. However, the traditional simulated annealing algorithm has certain limitations in solving the perceptual optimization simulation problem, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of security situational awareness optimization method based on decision tree algorithm to better solve this problem. In this scheme, the influencing factors is accurately located through probability theory, so as to determine the division of indicators, and the decision tree algorithm is used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on decision tree algorithm can better solve the limitations of traditional simulation annealing algorithms and improve the simulation accuracy and efficiency in the resear
A class of counting problems asks for the number of regions of a central hyperplane arrangement. By duality, this is the same as counting the vertices of a zonotope. Efficient algorithms are known that solve this prob...
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A class of counting problems asks for the number of regions of a central hyperplane arrangement. By duality, this is the same as counting the vertices of a zonotope. Efficient algorithms are known that solve this problem by computing the vertices of a zonotope from its set of generators. Here, we give an efficient algorithm, based on a linear optimization oracle, that performs the inverse task and recovers the generators of a zonotope from its set of vertices. We also provide a variation of that algorithm that allows to decide whether a polytope, given as its vertex set, is a zonotope and when it is not a zonotope, to compute its greatest zonotopal summand. (C) 2021 Elsevier B.V. All rights reserved.
This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria consid...
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This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods.
In order to adapt to the complex battlefield environment and various types of weapon systems in modern war, we built a detailed air defense simulation scenario and transformed it into mathematical and programming mode...
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Avstract-The integration of vast amounts of renewables into the power generation mix is paramount for the green transition. These intermittent resources bring promise of a cleaner and less expensive generation portfol...
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