A simple PID-based strategy for particle swarm optimization algorithm

Xiang, Zhenglong; Ji, Daomin; Zhang, Heng; Wu, Hongxi; Li, Yuanxiang

doi:10.1016/j.ins.2019.06.042

Cited by 42 publications

(29 citation statements)

References 21 publications

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“…Inspired by the success achieved by the proportional integral derivative (PID) controller in automation and its vast industrial applications [5][6][7] attempted using PID techniques to improve the performance of PSO. Reference [8] proposed the novel PID based strategy PSO (PBSPSO) algorithm based on the PID controller which was found to improve convergence while reducing stagnation of the PSO algorithm. A new variant of PSO with cross-over operation (PSOCO) was introduced by [9] which improved divergent search abilities of the PSO while avoiding stagnation by implementing a new learning model for the particles' velocity formula and two cross-over operations, while [10] used PSO and multi-objective PSO to develop a two-stage auto-tuning technique for PID controllers.…”

Section: Swarm Intelligencementioning

confidence: 99%

“…The homogeneous matrix of each successive pair of frames can be obtained using the formula in (7) below from the D-H parameters. the transformation matrix T for the robot manipulator's end effector is a product of post multiplication as shown in the formula in (8). Table 6.…”

Section: Fitness Functionmentioning

confidence: 99%

“…For all DH parameters; 1 joint displacement is Theta, 2 off-set displacement of joint is Alfa, see Equations 7and (8). The fourth, sixth, and eight joints are parallel to each other and perpendicular to the first joint while the second joint is perpendicular to all the other joints.…”

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confidence: 99%

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Developing a New Robust Swarm-Based Algorithm for Robot Analysis

et al. 2020

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Metaheuristics are incapable of analyzing robot problems without being enhanced, modified, or hybridized. Enhanced metaheuristics reported in other works of literature are problem-specific and often not suitable for analyzing other robot configurations. The parameters of standard particle swarm optimization (SPSO) were shown to be incapable of resolving robot optimization problems. A novel algorithm for robot kinematic analysis with enhanced parameters is hereby presented. The algorithm is capable of analyzing all the known robot configurations. This was achieved by studying the convergence behavior of PSO under various robot configurations, with a view of determining new PSO parameters for robot analysis and a suitable adaptive technique for parameter identification. Most of the parameters tested stagnated in the vicinity of strong local minimizers. A few parameters escaped stagnation but were incapable of finding the global minimum solution, this is undesirable because accuracy is an important criterion for robot analysis and control. The algorithm was trained to identify stagnating solutions. The algorithm proposed herein was found to compete favorably with other algorithms reported in the literature. There is a great potential of further expanding the findings herein for dynamic parameter identification.

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Section: Swarm Intelligencementioning

confidence: 99%

Section: Fitness Functionmentioning

confidence: 99%

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Developing a New Robust Swarm-Based Algorithm for Robot Analysis

et al. 2020

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“…PSO [1] has been successfully applied to various optimization problems in scientific and engineering tasks. Based on its simplicity, various modifications to the basic PSO algorithm have been proposed and analyzed in the literature [2][3][4][5][6]. PSO mimics the behaviors of searching strategies in flocking birds, swarm communities, schooling fish, and other swarms of organisms in nature.…”

Section: Pso and Its Variantsmentioning

confidence: 99%

“…Numerous attempts have been made to improve the performance of particle swarm optimization (PSO) [1] and its variants [2][3][4][5]. Such methods can be regarded as meta-heuristic algorithms with population-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence for Additive White Gaussian Noise Level Estimation

Prasetyo¹,

Salamah²

2020

IJFIS

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This paper presents a simple technique for estimating the noise levels in noisy images corrupted by additive white Gaussian noise. The proposed technique modifies the existing singular-valuedecomposition-based noise level estimation method. The proposed method calculates the sum of trailing singular values to infer noise levels. Particle swarm optimization and its variants can be used compute the optimal scalar value for the proposed noise level estimation method over a set of training images. As discussed in the experimental section, the proposed method outperforms existing schemes on noise level estimation tasks. Additionally, the estimated noise obtained from the proposed method can be used to improve the quality of denoised images.

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A PID Controller Architecture Inspired Enhancement to the PSO Algorithm

Bhattacharyya

2022

Lecture Notes in Networks and Systems

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A simple PID-based strategy for particle swarm optimization algorithm

Cited by 42 publications

References 21 publications

Developing a New Robust Swarm-Based Algorithm for Robot Analysis

Developing a New Robust Swarm-Based Algorithm for Robot Analysis

Swarm Intelligence for Additive White Gaussian Noise Level Estimation

A PID Controller Architecture Inspired Enhancement to the PSO Algorithm

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