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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This paper aims to explore a novel distributed convex optimization algorithm over directed networks with communication delay based on zero-gradient-sum strategy to solve a class of the distributed convex optimization ...
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Mobile edge computing (MEC) is an efficient method to tackle computationally intensive tasks for mobile devices (MDs). However, current studies about MEC do not consider that different MDs have different preferences f...
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With the rapid development of social economy, the competition in the power market has become more and more fierce, and at the same time, the field of power marketing is gradually moving into intelligence, so as to imp...
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The Long Short-Term Memory (LSTM) network is a commonly used model for time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necess...
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To achieve 'carbon peaking' and 'carbon neutrality', transitioning to electric vehicles (EVs) is crucial. However, unregulated EV charging significantly impacts the power grid's peak-valley load di...
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In order to enhance the efficacy of mobile robots, a new dung beetle optimization algorithm (NewDBO) is proposed. It is improved using four strategies to improve the search capability and convergence speed of the algo...
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In wireless sensor networks (WSN), data collection is a crucial and complex issue. Especially when the objective function is unknown, designing an effective data collection optimization algorithm has become a huge cha...
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Aiming at the current challenges in the field of feature selection, especially when dealing with high-dimensional data and redundant features, which face the problem of large feature dimensions, this paper proposes a ...
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