A wireless sensor network (WSN) is a distributed network system composed of a great many sensor nodes that rely on self-organization. The random deployment of WSNs in city planning often leads to the problem of low coverage of monitoring areas. In the construction of smart cities in particular, a large number of sensor nodes need to be deployed to maintain the reception, processing, and transmission of data throughout the city. However, the uneven distribution of nodes can cause a lot of wasted resources. To solve this problem, this paper proposes a WSN coverage optimization model based on an improved marine predator algorithm (IMPA). The algorithm introduces a dynamic inertia weight adjustment strategy in the global exploration and local exploitation stages of the standard marine predator algorithm to balance the exploration and exploitation capabilities of the algorithm and improve its solution accuracy. At the same time, the improved algorithm uses a multi-elite random leading strategy to enhance the information exchange rate between population individuals and improve the algorithm’s ability to jump out of the local optimum. The optimization experiment results of 11 benchmark test functions and part of the CEC2014 test functions show that the optimization performance of the improved algorithm is significantly better than the standard marine predator algorithm and other algorithms in the literature. Finally, the improved algorithm is applied to the WSN coverage optimization problem. The simulation results demonstrate that the IMPA has a better coverage rate than other metaheuristic algorithms and other improved algorithms in the literature for solving the WSN coverage optimization problem.
With the rapid development of the economy, the quality of power systems has assumed an increasingly prominent influence on people’s daily lives. In this paper, an improved equilibrium optimizer (IEO) is proposed to solve the optimal power flow (OPF) problem. The algorithm uses the chaotic equilibrium pool to enhance the information interaction between individuals. In addition, a nonlinear dynamic generation mechanism is introduced to balance the global search and local development capabilities. At the same time, the improved algorithm uses the golden sine strategy to update the individual position and enhance the ability of the algorithm to jump out of local optimums. Sixteen benchmark test functions, Wilcoxon rank sum test and 30 CEC2014 complex test function optimization results show that the improved algorithm has better global searching ability than the basic equilibrium optimizer, as well as faster convergence and a more accurate solution than other improved equilibrium optimizers and metaheuristic algorithms. Finally, the improved algorithm is applied to the standard IEEE 30-bus test systems for different objectives. The obtained results demonstrate that the improved algorithm has better solutions than other algorithms in the literature for solving the optimal power flow problem.
Aspect-level sentiment analysis aims to identify the sentiment polarity of one or more aspect terms in a sentence. At present, many researchers have applied dependency trees and graph neural networks (GNNs) to aspect-level sentiment analysis and achieved promising results. However, when a sentence contains multiple aspects, most methods model each aspect independently, ignoring the issue of sentiment connection between aspects. To address this problem, this paper proposes a dual-channel interactive graph convolutional network (DC-GCN) model for aspect-level sentiment analysis. The model considers both syntactic structure information and multi-aspect sentiment dependencies in sentences and employs graph convolutional networks (GCN) to learn its node information representation. Particularly, to better capture the representations of aspect and opinion words, we exploit the attention mechanism to interactively learn the syntactic information features and multi-aspect sentiment dependency features produced by the GCN. In addition, we construct the word embedding layer by the BERT pre-training model to better learn the contextual semantic information of sentences. The experimental results on the restaurant, laptop, and twitter datasets show that, compared with the state-of-the-art model, the accuracy is up to 1.86%, 2.50, 1.36%, and 0.38 and the Macro-F1 values are up to 1.93%, 0.61%, and 0.4%, respectively.
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