2022
DOI: 10.1155/2022/3475806
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Automatic Console Image Processing Aided by Improved Particle Swarm Computing Intelligent Algorithm

Abstract: The search range and search position of the particle are closely related to the shrinkage-expansion factor of the particle and the value of the center of the potential well. As the number of iterations increases, its search will gradually fall near the center of the potential well, and the search range will gradually decrease. Therefore, when the center of the potential well and the search range gradually approach the global optimum, the final result of the algorithm can be guaranteed to be the global optimum.… Show more

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Cited by 3 publications
(2 citation statements)
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“…In the case of the constrained minimization problem, it is not only necessary to ensure that the value of the objective function can be continuously reduced during the iterations but also to consider whether the solution is feasible in the actual situation. In order to simplify the process of searching for the optimal solution of the PSO problem, the algorithm is generally constructed in accordance with the decomposition of the constrained optimization problem, which is transformed into an unconstrained optimization problem, and the nonlinear planning problem is transformed into a linear planning problem, so that the problem with the complexity of the characteristics of the problem can be finally transformed into a simple problem [23][24].…”
Section: Solving Constrained Optimization Problems With Particle Swar...mentioning
confidence: 99%
“…In the case of the constrained minimization problem, it is not only necessary to ensure that the value of the objective function can be continuously reduced during the iterations but also to consider whether the solution is feasible in the actual situation. In order to simplify the process of searching for the optimal solution of the PSO problem, the algorithm is generally constructed in accordance with the decomposition of the constrained optimization problem, which is transformed into an unconstrained optimization problem, and the nonlinear planning problem is transformed into a linear planning problem, so that the problem with the complexity of the characteristics of the problem can be finally transformed into a simple problem [23][24].…”
Section: Solving Constrained Optimization Problems With Particle Swar...mentioning
confidence: 99%
“…Therefore, it is necessary to design a reasonable and effective resource allocation strategy for CAEVs IoT environments. To address these issues, swarm intelligent computing has become an effective approach [4]. Swarm intelligent computing involves forming a group of individuals and equipping them with intelligence and behavioural rules, enabling the collective intelligence of the entire group.…”
Section: Introductionmentioning
confidence: 99%