Illumination correction via optimized random vector functional link using improved Harris hawks optimization

Zhou, Zhiyu; Liu, Dexin; Wang, Yaming; Zhu, Zefei

doi:10.1007/s11042-022-11986-1

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…In recent years, with the application of different evolutionary optimization algorithms in various scenarios [16][17][18], some researchers have studied them in combination with visual tracking problems and made significant progress. By combining an evolutionary optimization algorithm with the particle filter, the researchers [19,20] found that the evolutionary algorithm does not depend on the prior knowledge of the particle filter, and can be explored and exploited in the face of uncertainty so that the particles show arbitrariness.…”

Section: Improved Particle Filter Based On Sample Impoverishmentmentioning

confidence: 99%

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

Yang

Yao

Yang

2023

JMSE

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Due to the complexity of the underwater environment, tracking underwater targets via traditional particle filters is a challenging task. To resolve the problem that the tracking accuracy of a traditional particle filter is low due to the sample impoverishment caused by resampling, in this paper, a new tracking algorithm using Harris-hawks-optimized particle filters (HHOPF) is proposed. At the same time, the problem of particle filter underwater target feature construction and underwater target scale transformation is addressed, the corrected background-weighted histogram method is introduced into underwater target feature recognition, and the scale filter is combined to realize target scaling transformation during tracking. In addition, to enhance the computational speed of underwater target tracking, this paper constructs a nonlinear escape energy using the Harris hawks algorithm in order to balance the exploration and exploitation processes. Based on the proposed HHOPF tracker, we performed detection and evaluation using the Underwater Object Tracking (UOT100) vision database. The proposed method is compared with evolution-based tracking algorithms and particle filters, as well as with recent tracker-based correlation filters and some other state-of-the-art tracking methods. By comparing the results of tracking using the test data sets, it is determined that the presented algorithm improves the overlap accuracy and tracking accuracy by 11% compared with other algorithms. The experiments demonstrate that the presented HHOPF visual tracking provides better tracking results.

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Section: Improved Particle Filter Based On Sample Impoverishmentmentioning

confidence: 99%

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

Yang

Yao

Yang

2023

JMSE

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Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm

Ren

Chang

2023

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Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.

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Illumination correction via optimized random vector functional link using improved Harris hawks optimization

Cited by 2 publications

References 28 publications

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm

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