Multi-threshold segmentation is widely used in image segmentation, but its computational complexity increases with the number of thresholds, which can compromise segmentation precision. Additionally, existing heuristi...
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Multi-threshold segmentation is widely used in image segmentation, but its computational complexity increases with the number of thresholds, which can compromise segmentation precision. Additionally, existing heuristic methods for multi-threshold segmentation still suffer from slow convergence and premature convergence. To address these limitations, this study proposes a novel aptenodytes forsteri optimization algorithm with an adaptive perturbation of oscillation and mutation operation (AFOA-APM) to determine the optimal image threshold. AFOA-APM improves upon existing methods with several key innovations: firstly, an adaptive perturbation of the oscillation strategy (AP) improves exploitation ability;secondly, improved methods for move strategies 2 and 3 reduce time complexity and the probability of stagnation;thirdly, a random mutation strategy keeps diversity and preserves inheritance;and finally, an adaptive selection strategy balances search capabilities. AFOA-APM is validated using CEC 2017 benchmark functions and image segmentation problems, and results demonstrate its superior performance in terms of quality, consistency, and accuracy. These findings indicate that AFOA-APM has a high potential to address image multi-threshold segmentation problems effectively.
The dynamic characteristics of the power system are becoming more and more complex, and the difficulty of operation control is increasing. Preventive control is the main means of power system transient stability contr...
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The dynamic characteristics of the power system are becoming more and more complex, and the difficulty of operation control is increasing. Preventive control is the main means of power system transient stability control. This paper proposes a stacking ensemble learning-driven power system transient stability preventive control optimization method. Firstly, a transient stability assessment model based on Stacking Ensemble Deep Belief Nets (SEDBN) network is established in this research. The performance of weak classifiers is improved by SEDBN's multi-layer ensemble structure, and the created transient stability estimator can extract diverse features and has better robustness and generalization abilities. Secondly, the trained transient stability estimator is integrated into the aptenodytesforsterioptimization (AFO) algorithm as a "transient stability constraint discriminator". Finally, with the goal of minimizing the cost of preventive control, an optimizationalgorithm for the preventive control of power system transient stability driven by SEDBN is established. Simulation results on IEEE 39-bus systems show that the proposed method can achieve highly efficient control solutions. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://***/licenses/by/4.0/).
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