RETRACTED: Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm

Liu, Tingting; Yuan, Zhi; Wu, Lijun; Badami, Benjamin

doi:10.1002/ima.22559

Cited by 28 publications

(12 citation statements)

References 51 publications

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“…New and improved metaheuristic algorithms have also been applied in various fields [16]. Liu et al [17] designed an immune cooperative particle swarm algorithm for the multiparameter identification of permanent magnet synchronous motors. This method can effectively identify the changing values of parameters such as the motor resistance and rotor flux linkage.…”

Section: Introductionmentioning

confidence: 99%

Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

et al. 2022

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To address the problem of the sparrow search algorithm (SSA) has poor global search ability, weak local development ability, and easily falls into the local optimal solution, a multi-strategy improved evolutionary sparrow search algorithm (MSSA) is proposed. The introduction of the tent chaotic map improves the diversity of the initialization population, accelerates algorithm convergence, and improves convergence accuracy. Endow sparrow finders with a random search ability to coordinate the balance between global search and local development. To discover dangerous sparrow individuals, the mutation evolution operation is completed, and a greedy strategy is combined to improve the processing ability of the algorithm for local optimal solutions and make full use of each sparrow individual. Six benchmark functions were used to comprehensively verify the feasibility of the proposed algorithm based on four aspects: optimization ability, robustness, convergence ability, and optimization trajectory. These results indicate that the proposed algorithm is superior. Finally, through the comparison and analysis of the parameter identification and control strategies of the two servo systems in practical application, on the one hand, the advantages of the proposed algorithm in practical engineering applications are illustrated. In addition, a fuzzy PID control strategy based on MSSA is proposed. By adding step, sinusoidal, triangular wave and disturbance signals, simulation experiments show that the control strategy can significantly improve the dynamic and steady performance of the servo system.

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Section: Introductionmentioning

confidence: 99%

Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

et al. 2022

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“…Applying it to Distributed Maximum Power Point Tracking (DMPPT) provides conditions for the stable operation of the microgrid. Liu et al [ 19 ] came up with a balanced sparrow search algorithm (BSSA), and the random walk strategy of Levy flight method was exerted to appropriately adjust the local search, which brought the improving efficiency of CNN focus. Besides, they applied it to the medical field to improve MRI image diagnosis of the brain robustness and accuracy of tumors.…”

Section: Introductionmentioning

confidence: 99%

A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems

Wang

Huang

Zhu

2022

Computational Intelligence and Neuroscience

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The swarm intelligence algorithm is a new technology proposed by researchers inspired by the biological behavior of nature, which has been practically applied in various fields. As a kind of swarm intelligence algorithm, the newly proposed sparrow search algorithm has attracted extensive attention due to its strong optimization ability. Aiming at the problem that it is easy to fall into local optimum, this paper proposes an improved sparrow search algorithm (IHSSA) that combines infinitely folded iterative chaotic mapping (ICMIC) and hybrid reverse learning strategy. In the population initialization stage, the improved ICMIC strategy is combined to increase the distribution breadth of the population and improve the quality of the initial solution. In the finder update stage, a reverse learning strategy based on the lens imaging principle is utilized to update the group of discoverers with high fitness, while the generalized reverse learning strategy is used to update the current global worst solution in the joiner update stage. To balance exploration and exploitation capabilities, crossover strategy is joined to update scout positions. 14 common test functions are selected for experiments, and the Wilcoxon rank sum test method is achieved to verify the effect of the algorithm, which proves that IHSSA has higher accuracy and better convergence performance to obtain solutions than 9 algorithms such as WOA, GWO, PSO, TLBO, and SSA variants. Finally, the IHSSA algorithm is applied to three constrained engineering optimization problems, and satisfactory results are held, which proves the effectiveness and feasibility of the improved algorithm.

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“…Lei et al(2020) uses Levy flight to improve the flexibility of the sparrow search algorithm and to apply it to positioning problems in wireless sensor networks, which has a good effect. In addition, Liu et al(2021) have applied it to the diagnosis of diseases with good diagnostic results. Zhang et al(2021)introduced the sine-cosine search algorithm and proposed a cooperative idea, which can be applied to the adaptive enhancement classifier with better binary classification results.Lu introduced the idea of the bird swarm algorithm and proposed an improved sparrow search algorithm (ISSA), which is applied to the multi-threshold image segmentation field with better segmentation speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Learning Sparrow Algorithm With Non-Uniform Search for Global Optimization

Chen

Zhang

2022

International Journal of Swarm Intelligence Research

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Sparrow Algorithm as a New Swarm Intelligence Search Algorithm, the sparrow algorithm has good optimization ability, but in complex environments, it still has certain limitations, such as weak learning ability. Therefore, this paper proposes a learning sparrow search algorithm for non-uniform search(Sparrow search algorithm with non-uniform search, NSSSA). A learning behavior selection strategy is proposed, and saltation learning and a random walk learning are introduced respectively.To a certain extent, the algorithm avoided alling into the local optimum, and a non-uniform variable spiral search is proposed to balance the development and search capabilities of the algorithm. In the experimental simulation, the effectiveness of the NSSSA algorithm is verified by using the benchmark function, and it is tested on the CEC 2013 test set. Compared with the algorithms with better performance in recent years, the results show that the NSSSA algorithm has better universality . Finally, the NSSSA algorithm is applied to the WSN coverage optimization problem. The results show that NSSSA achieves more than 90% and 96% coverage on the two models of 50×50 and 100×100, respectively, which verifies the practicability of the algorithm.

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RETRACTED: Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm

Cited by 28 publications

References 51 publications

Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

Research on Multistrategy Improved Evolutionary Sparrow Search Algorithm and its Application

A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems

Learning Sparrow Algorithm With Non-Uniform Search for Global Optimization

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