Improved Topological Optimization Method Based on Particle Swarm Optimization Algorithm

Gao, Jie; Zhang, Wenqun

doi:10.1109/access.2022.3174602

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Later, Yu et al [18] present a hybrid PSO with non-linear inertia weight and Gaussian mutation (NGPSO), in which a non-linear inertia weight strategy is introduced to improve the local search capabilities of PSO, and the results show that the proposed NGPSO performs significantly better than the compared algorithms in 62 benchmark instances of JSSP. Recently, Guan and Zhang [19] propose a novel PSO (NPSO), which uses a new inertia weight to calculate the fitness value of structural flexibility, and the numerical results show that the performance of the proposed NPSO algorithm is better than that of the comparison.…”

Section: Introductionmentioning

confidence: 99%

An adaptive particle swarm optimization with information interaction mechanism

Liu,

Wei,

Zhang

2024

Mach. Learn.: Sci. Technol.

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The Particle Swarm Optimization (PSO) algorithm is easy to implement owing to its simple framework, and has been successfully applied to many optimization problems. However, the standard PSO easily falls into the local optimum and has weak search ability. To enhance the optimization ability of the algorithm, this paper proposes an adaptive particle swarm optimization with information interaction mechanism (APSOIIM). First, a chaotic sequence strategy was used to produce uniformly distributed particles and enhance their convergence speed at the initialization stage of the algorithm. Then, an interaction information mechanism is introduced to enhance the diversity of the population with the progress of the search, which can effectively interact with the best information of neighboring particles to maintain the balance between exploration and exploitation. Besides, the convergence was proven to verify the robustness and efficiency of the proposed APSOIIM algorithm. Finally, the proposed APSOIIM was applied to solve the CEC2014 benchmark functions and CEC2017 benchmark functions as well as famous engineering optimization problems. The experimental results show that the proposed APSOIIM has significant advantages over the compared algorithms.

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Section: Introductionmentioning

confidence: 99%

An adaptive particle swarm optimization with information interaction mechanism

Liu,

Wei,

Zhang

2024

Mach. Learn.: Sci. Technol.

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An Improved Quadratic Spline Model Using Curvature Tip Compression—Particle Swarm Optimization to Forecast Accurately the Nonlinear Fluid Calibration Curve

Prakosa,

Alias,

Purwowibowo

et al. 2024

IEEE Access

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Additively Manufactured Lightweight Automobile Cylinder Head—A New Process for Structural Optimization from Concept to Validated Hardware

Kayacan,

Pischinger,

Ahlborn

et al. 2024

SAE Int. J. Engines

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<div>Reducing vehicle weight is a key task for automotive engineers to meet future emission, fuel consumption, and performance requirements. Weight reduction of cylinder head and crankcase can make a decisive contribution to achieving these objectives, as they are among the heaviest components of a passenger car powertrain.</div> <div>Modern passenger car cylinder heads and crankcases have greatly been optimized in terms of cost and weight in all-aluminum design using the latest conventional production techniques. However, it is becoming apparent that further significant weight reduction cannot be expected, as processes such as casting have reached their limits for further lightweighting due to manufacturing restrictions. Here, recent developments in the additive manufacturing (AM) of metallic structures is offering a new degree of freedom.</div> <div>As part of the government-funded research project LeiMot [Lightweight Engine (Eng.)] borderline lightweight design potential of a passenger car cylinder head with the use of automated structural optimization is investigated. A four-cylinder 2.0 L series production Diesel engine cylinder head is taken as basis in terms of bolting and gas flow channels.</div> <div>With the newly gained design freedom by AM, it is demonstrated that a cylinder head with up to 30% weight reduction in comparison to the reference cylinder head can be realized through a novel stiffness concept, while fulfilling the mechanical requirements. The optimized design is initially validated by CAE methods for the hot operational conditions and worst-case circumstances. Required material properties are determined through manufactured specimens. A prototype cylinder head is manufactured using the LPBF (laser powder bed fusion) process, and hardware durability is validated on a hydro-pulse test bench under the maximum cylinder pressure of the reference Diesel engine. Subsequently, a material analysis is performed, and optimization potentials at the component geometry and printing parameters are investigated to further improve material properties and hence fatigue performance.</div>

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Improved Topological Optimization Method Based on Particle Swarm Optimization Algorithm

Cited by 3 publications

References 8 publications

An adaptive particle swarm optimization with information interaction mechanism

An adaptive particle swarm optimization with information interaction mechanism

An Improved Quadratic Spline Model Using Curvature Tip Compression—Particle Swarm Optimization to Forecast Accurately the Nonlinear Fluid Calibration Curve

Additively Manufactured Lightweight Automobile Cylinder Head—A New Process for Structural Optimization from Concept to Validated Hardware

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