Most of the complex research problems can be formulated as optimization problems. Emergence of big data technologies have also commenced the generation of complex optimization problems with large size. The high computational cost of these problems has rendered the development of optimization algorithms with parallelization. Particle swarm optimization (PSO) algorithm is one of the most popular swarm intelligence-based algorithm, which is enriched with robustness, simplicity and global search capabilities. However, one of the major hindrance with PSO is its susceptibility of getting entrapped in local optima and; alike other evolutionary algorithms the performance of PSO gets deteriorated as soon as the dimension of the problem increases. Hence, several efforts are made to enhance its performance that includes the parallelization of PSO. The basic architecture of PSO inherits a natural parallelism, and receptiveness of fast processing machines has made this task pretty convenient. Therefore, parallelized PSO (PPSO) has emerged as a well-accepted algorithm by the research community. Several studies have been performed on parallelizing PSO algorithm so far. Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications.

A Survey on Parallel Particle Swarm Optimization Algorithms

Genetic Algorithm and its Applications to Mechanical Engineering: A Review

Geometrical deviation modeling and monitoring of 3D surface based on multi-output Gaussian process

Comparative study of surrogate approaches while optimizing computationally expensive reaction networks

Multi-objective micro-geometry optimization of gear tooth supported by response surface methodology

Global optimization of reliability design for large ball mill gear transmission based on the Kriging model and genetic algorithm

Point cloud registration algorithm based on curvature feature similarity

This paper describes a dynamic model with four degrees of freedom for simulating the rollover stability performances of articulated loaders. The dynamic model considers three translational degrees of freedom and a rotational degree of freedom about the tilting axis and was verified by field experiments and virtual prototype experiments. The field experiments using a scaled articulated loader are detailed, and the experimental data of the lateral load transfer ratio, the lateral acceleration and the roll angle of the vehicle are provided for classification at different speeds and different turning radii. The virtual prototype experiments are modelled on the experimental model as well as the simulation conditions. Comparing the experimental data with the theoretical data shows that the dynamics model is reasonable and that the virtual prototype simulation can be used instead of the field experiments to improve the security of active safety technology development.

Dynamic simulation for the rollover stability performances of articulated vehicles

This paper devotes both analytical and experimental efforts in developing a comprehensive dynamic model for an articulated steering wheel loader. The general motion of a wheel loader without suspension is described by seven degrees of freedom (DOF) (three for translation and four for rotation) in this model, which can be used to study the problem of wheel loader dynamics on slopes and over obstacles. A scale wheel loader was designed and manufactured to validate the dynamic model in three conditions (turning on level ground, turning on slopes, and passing over obstacles). The test results reasonably agree with the simulation results. The developed dynamic model was found to be useful and could serve as an important tool for analysing the stability of wheel loaders.

Dynamic model and validation of an articulated steering wheel loader on slopes and over obstacles

Low-frequency vibrations (0.5–5 Hz) that harm drivers occur in off-road vehicles. Thus, researchers have focused on finding methods to effectively isolate or control low-frequency vibrations. A novel nonlinear seat suspension structure for off-road vehicles is designed, whose static characteristics and seat-human system dynamic response are modeled and analyzed, and experiments are conducted to verify the theoretical solutions. Results show that the stiffness of this nonlinear seat suspension could achieve real zero stiffness through well-matched parameters, and precompression of the main spring could change the nonlinear seat suspension performance when a driver’s weight changes. The displacement transmissibility curve corresponds with the static characteristic curve of nonlinear suspension, where the middle part of the static characteristic curve is gentler and the resonance frequency of the displacement transmissibility curve and the isolation minimum frequency are lower. Damping should correspond with static characteristics, in which the corresponding suspension damping value should be smaller given a flatter static characteristic curve to prevent vibration isolation performance reduction.

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Xuefei Li

Papers

Global optimization of reliability design for large ball mill gear transmission based on the Kriging model and genetic algorithm

Point cloud registration algorithm based on curvature feature similarity

Dynamic simulation for the rollover stability performances of articulated vehicles

Dynamic model and validation of an articulated steering wheel loader on slopes and over obstacles

Modeling and Analysis of Static and Dynamic Characteristics of Nonlinear Seat Suspension for Off-Road Vehicles