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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(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
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 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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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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The research of traditional cooperation communication is based on the assumption that the nodes are willing to cooperate, but in fact the selfish nodes are not willing to cooperate in order to save their own resources...
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In order to solve the problems of low efficiency and high cost in traditional distribution network coordination planning, a multi-stage active distribution network coordination planning method based on adaptive partic...
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We consider the setting of online convex optimization with adversarial time-varying constraints in which actions must be feasible w.r.t. a fixed constraint set, and are also required on average to approximately satisf...
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We consider the setting of online convex optimization with adversarial time-varying constraints in which actions must be feasible w.r.t. a fixed constraint set, and are also required on average to approximately satisfy additional time-varying constraints. Motivated by scenarios in which the fixed feasible set (hard constraint) is difficult to project on, we consider projection-free algorithms that access this set only through a linear optimization oracle (LOO). We present an algorithm that, on a sequence of length T and using overall T calls to the LOO, guarantees Õ(T3/4) regret w.r.t. the losses and O(T7/8) constraints violation (ignoring all quantities except for T). In particular, these bounds hold w.r.t. any interval of the sequence. This algorithm however also requires access to an oracle for minimizing a strongly convex nonsmooth function over a Euclidean ball. We present a more efficient algorithm that does not require the latter optimization oracle but only first-order access to the time-varying constraints, and achieves similar bounds w.r.t. the entire sequence. We extend the latter to the setting of bandit feedback and obtain similar bounds (as a function of T) in expectation. Copyright 2024 by the author(s)
Due to the incorrect proportions between the exploitation and exploration phases, the whale optimization algorithm (WOA) gets stuck into the local optima, which causes premature convergence. To address this issue, qua...
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This paper presents an innovative approach employing persistence-based clustering in Riemannian manifolds within evolutionary computation algorithms to address multi-modal optimization problems. The proposed framework...
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Wireless sensor networks are widely used in areas such as monitoring of complex environments. Nodes consume energy due to sensing and transmission, and there is a hot-spot problem. Studies have shown that clustering i...
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