Relation of Neighborhood Size and Diversity Loss Rate in Particle Swarm Optimization With Ring Topology

Pluháček, Michal; Kazíková, Anežka; Kadavý, Tomáš; Viktorin, Adam; Šenkeřík, Roman

doi:10.13164/mendel.2021.2.074

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…It then exchanges information with its neighbors, considering both its own best position and the best position found by its neighbors. The particle’s velocity and position are updated using the information obtained from these local interactions [ 102 ]. In the ring topology, the updating equation for velocity and position remains the same as in the standard PSO algorithm.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Khan,

Shafi,

Khawaja

et al. 2023

Sensors

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Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Khan,

Shafi,

Khawaja

et al. 2023

Sensors

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“…It is important to note, that many optimizing liaisons (MOL) optimisation is based on the original particle swarm optimisation (PSO) [1,11,17]. Particle swarm optimisation (MOL) was invented and first applied by Marcus Pedersen [15].…”

Section: Many Optimizing Liaisons (Mol)mentioning

confidence: 99%

Improving Initial Aerofoil Geometry Using Aerofoil Particle Swarm Optimisation

Müller

2022

mendel

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Advanced optimisation of the aerofoil wing of a general aircraft is the main subject of this paper. Meta-heuristic optimisation techniques, especially swarm algorithms, were used. Subsequently, a new variant denoted as aerofoil particle swarm optimisation (aPSO) was developed from the original particle swarm optimisation (PSO). A parametric model based on B-spline was used to optimise the initial aerofoil. The simulation software Xfoil was calculating basic aerodynamic features (lift, drag, moment).

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“…A new mutated genetic algorithm was employed to solve the problem of independent job scheduling in grid computing [51]. Study on the inner dynamics of PSO algorithm using network visualization showed the self-adaptive approaches of PSO [35]. The versatility of these algorithms had led to the development of hybrid algorithms.…”

Section: Introductionmentioning

confidence: 99%

Meta-Heuristics Based Inverse Kinematics of Robot Manipulator’s Path Tracking Capability Under Joint Limits

Kanagaraj

Masthan

2022

mendel

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In robot-assisted manufacturing or assembly, following a predefined path became a critical aspect. In general, inverse kinematics offers the solution to control the movement of manipulator while following the trajectory. The main problem with the inverse kinematics approach is that inverse kinematics are computationally complex. For a redundant manipulator, this complexity is further increased. Instead of employing inverse kinematics, the complexity can be reduced by using a heuristic algorithm. Therefore, a heuristic-based approach can be used to solve the inverse kinematics of the robot manipulator end effector, guaranteeing that the desired paths are accurately followed. This paper compares the performance of four such heuristic-based approaches to solving the inverse kinematics problem. They are Bat Algorithm (BAT), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). The performance of these algorithms is evaluated based on their ability to accurately follow a predefined trajectory. Extensive simulations show that BAT and GSA outperform PSO and WOA in all aspects considered in this work related to inverse kinematic problems.

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Relation of Neighborhood Size and Diversity Loss Rate in Particle Swarm Optimization With Ring Topology

Cited by 7 publications

References 12 publications

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Improving Initial Aerofoil Geometry Using Aerofoil Particle Swarm Optimisation

Meta-Heuristics Based Inverse Kinematics of Robot Manipulator’s Path Tracking Capability Under Joint Limits

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