An Adaptive Individual Inertia Weight Based on Best, Worst and Individual Particle Performances for the PSO Algorithm

Spavieri, Guilherme; Cavalca, Diego L.; Fernandes, Ricardo Augusto Souza; Lage, Guilherme G.

doi:10.1007/978-3-319-91253-0_50

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…A particle swarm in an E-dimensional target search space consists of M particles. Each particle is represented by an E-dimensional vector, and its position x i in space can be described by Formula (1) [17].…”

Section: Parameter Setting For Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Evaluation and Analysis of Heuristic Intelligent Optimization Algorithms for PSO, WDO, GWO and OOBO

Huang,

Xu,

et al. 2023

Mathematics

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In order to comprehensively evaluate and analyze the effectiveness of various heuristic intelligent optimization algorithms, this research employed particle swarm optimization, wind driven optimization, grey wolf optimization, and one-to-one-based optimizer as the basis. It applied 22 benchmark test functions to conduct a comparison and analysis of performance for these algorithms, considering descriptive statistics such as convergence speed, accuracy, and stability. Additionally, time and space complexity calculations were employed, alongside the nonparametric Friedman test, to further assess the algorithms. Furthermore, an investigation into the impact of control parameters on the algorithms’ output was conducted to compare and analyze the test results under different algorithms. The experimental findings demonstrate the efficacy of the aforementioned approaches in comprehensively analyzing and comparing the performance on different types of intelligent optimization algorithms. These results illustrate that algorithm performance can vary across different test functions. The one-to-one-based optimizer algorithm exhibited superior accuracy, stability, and relatively lower complexity.

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Section: Parameter Setting For Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Evaluation and Analysis of Heuristic Intelligent Optimization Algorithms for PSO, WDO, GWO and OOBO

Huang,

Xu,

et al. 2023

Mathematics

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“…20 The inertia-adaptive PSO with the adjusted maximum and minimum values of inertia factor has the feature of rapid convergence in the first 1000 iterations, but a slightly worse result is obtained for complex highly nonlinear function, especially Sphere and Ackley's functions for premature convergence. 21 So, the adapted PSO with smoothing iteration is proposed to avoid the problem of being trapped in local optimum.…”

Section: Human-machine Interface Layout Optimized By the Adapted Pso mentioning

confidence: 99%

“…Most cockpit facilities must be reachable for drivers to ensure driving safety, so the model should make facilities reachable by the driver's hands. The reachable zones of hands are shown in equations (21) and (22) x…”

Section: Human-machine Interface Layout Optimized By the Adapted Pso mentioning

confidence: 99%

“…In equations (21) and (22), the origin of the coordinate scheme is the center of the driver's eyes. S denotes the shoulder width; E is the horizontal distance from the eye to the shoulder along the y-axis; L is the vertical distance from the eye to the shoulder; T is the distance from the outside of the shoulder to the articulatio humeri; r is the radius of the circle formed by human arm's normal attainable region; and R 1 , R 2 , and R 3 are the axis lengths of the ellipsoid formed by the human arm's maximum attainable region in the x-, y-, and zaxis, respectively.…”

Section: Human-machine Interface Layout Optimized By the Adapted Pso mentioning

confidence: 99%

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Adapted particle swarm optimization algorithm–based layout design optimization of passenger car cockpit for enhancing ergonomic reliability

Liu

et al. 2019

Advances in Mechanical Engineering

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The mechanical reliability problem of passenger car cockpit facilities layout is increasingly complex and has potential and uncertain risks for human safety while the number of private cars is increasing. A new system of layout design optimization is proposed to solve this problem. First, the optimization sequences of facilities are determined using a hybrid method of multiple-attribute decision-making and entropy. Second, the degree of feeling crowded in the cockpit layout can be adjusted based on customers' preference. Third, an adapted particle swarm optimization algorithm is proposed to solve the problem of three-dimensional layout optimization in car cockpit human-machine interface according to the ergonomic principles, and the adapted algorithm called smoothing iteration particle swarm optimization is contrasted with those of other common algorithms to demonstrate its advantages. Finally, the optimized layout is analyzed by virtual simulations and compared with the original layout to show the feasibility and effectiveness of the proposed design system. Analysis results indicate that the optimized layout by the new particle swarm optimization can make the operation easier and safer than the original one to enhance ergonomic reliability.

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GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

Wang,

Song,

Wang

et al. 2024

Artif Intell Rev

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An Adaptive Individual Inertia Weight Based on Best, Worst and Individual Particle Performances for the PSO Algorithm

Cited by 6 publications

References 5 publications

Evaluation and Analysis of Heuristic Intelligent Optimization Algorithms for PSO, WDO, GWO and OOBO

Evaluation and Analysis of Heuristic Intelligent Optimization Algorithms for PSO, WDO, GWO and OOBO

Adapted particle swarm optimization algorithm–based layout design optimization of passenger car cockpit for enhancing ergonomic reliability

GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

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