Knowing at least an accurate estimate of the network size is necessary for many real-world applications (e.g., routing, synchronization, etc.). However, estimating the network size in a distributed way is not a trivia...
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The distributed nonconvex constrained optimization problem with equality and inequality constraints is researched in this paper, where the objective function and the function for constraints are all nonconvex. To solv...
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The PBFT consensus algorithm solves the Byzantine node tolerance problem of Raft and Paxos consensus algorithms, but the three-stage confirmation leads to its poor scalability and high communication latency. In respon...
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The concept of "dynamic adaptation gain/learning rate" has been introduced in Landau et al. (2023) in order to accelerate the adaptation/learning transients in the context of adaptation/learning algorithms u...
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Network disintegration, which aims to degrade network functionality through the optimal set of node or edge removals, has been widely applied in various domains such as epidemic control and rumor containment. Hypernet...
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This paper considers the distributed convex optimization problem over directed multi-agent networks. We introduce a continuous-time coordination algorithm to solve unconstrained optimization problems with additive str...
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Evidential c-means (ECM) is a prototype-based clustering algorithm that generates a credal partition. Such a partition encompasses the notions that can be encountered with a hard, fuzzy or possibilistic partition, all...
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Joint ToA source localization and synchronization determines the location and time offset of a radiating source using time-of-arrival measurements collected from a time-synchronized array of sensors. Various approache...
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In order to efficiently lock the optimal layout strategy and avoid the trap of local optimal solution of a single SA algorithm, a layout design method of artistic elements in indoor space environment based on GA-SA hy...
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The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA)– another simple EDA–, the cGA has been subject t...
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The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA)– another simple EDA–, the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to competing approaches. Surprisingly though, up to date and in contrast to the UMDA and many other heuristics, we lack a rigorous runtime analysis of the cGA on the LEADINGONES benchmark–one of the most studied theory benchmarks in the domain of evolutionary computation. We fill this gap in the literature by conducting a formal runtime analysis of the cGA on LEADINGONES. For the cGA’s single parameter–called the hypothetical population size–at least polylogarithmically larger than the problem size, we prove that the cGA samples the optimum of LEADINGONES with high probability within a number of function evaluations quasi-linear in the problem size and linear in the hypothetical population size. For the best hypothetical population size, our result matches, up to polylogarithmic factors, the typical quadratic runtime that many randomized search heuristics exhibit on LEADINGONES. Our analysis exhibits some noteworthy differences in the working principles of the two algorithms which were not visible in previous works. 1997-2012 IEEE.
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