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Optimization of morphological process parameters in contactless laser scanning system using modified particle swarm algorithm

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MEASUREMENT 2017年 109卷 27-35页

作者： Pathak, Vimal Kumar Singh, Amit Kumar Malaviya Natl Inst Technol Jaipur Dept Mech Engn Jaipur 302017 Rajasthan India

This paper models the influence of the morphology of an object on the accuracy of the scanned data, for the case of contactless laser scanning. Using the scanned objects morphology, two important process parameters are specified namely scanning angle and distance of the laser beam from the part surface. Experiments have been performed with different scanning conditions using full factorial design. A mathematical prediction model for estimating the standard deviation of the final surface is developed in terms of the above scanning parameters using response surface methodology (RSM). Furthermore, analysis of variance (ANOVA) has been employed to establish the statistical significance of the scanning parameters and the developed model. In addition, it has been observed that the measured and predicted standard deviations are well in agreement, which confirms the effectiveness of the developed model. The mathematical model is further optimized using a modified particle swarm optimization (MPSO) algorithm. The proposed MPSO algorithm generates new swarm positions and fitness solutions using an improved and modified search equation. Finally, two realistic non-trivial case studies are considered for validation of the proposed methodology. This methodology provides the optimal combination of morphological process parameters with a considerable reduction in standard deviation for final scanned surface models. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Contactless laser scanning modified particle swarm algorithm Optimization

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Optimal Parameter Estimation for Muskingum Model Using a modified particle swarm algorithm

Optimal Parameter Estimation for Muskingum Model Using a Mod...

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The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

作者： Wenchuan Wang Yingbin Kang Lin Qiu Faculty of Water conservancy Engineering North China Institute of Water Conservancy and Hydroelectri Faculty of environmental & municipal engineering North China Institute of Water Conservancy and Hydr

The accurate parameter estimation for Muskingum model is to be useful to give the flood forecasting for flood control in water resources planning and management. Although some methods have been used to estimate the parameters for Muskingum model, an efficient method for parameter estimation in the calibration process is still lacking. In order to reduce the computational amount and improve the computational precision for parameter estimation, a modified particle swarm algorithm (MPSO) is presented for parameter optimization of Muskingum model. The technique found the best parameter values compared to previous results in terms of the sum of least residual absolute value. Empirical results that involve historical data from existed paper reveal the proposed MPSO outperforms other approaches in the literature.

关键词： component Muskingum Model Optimal Parameter Estimation modified particle swarm algorithm

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A modified algorithm of particle swarm Optimization for form error evaluation

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TM-TECHNISCHES MESSEN 2017年第4期84卷 272-292页

作者： Pathak, Vimal Kumar Singh, Amit Kumar Singh, Ramanpreet Chaudhary, Himanshu Malaviya Natl Inst Technol Dept Mech Engn JLN Marg Jaipur 302017 Rajasthan India

The set of measured data points acquired from the Coordinate Measuring Machine (CMM) need to be processed and analyzed for evaluating the form errors inside the manufactured components. This paper presents a modified algorithm of particle swarm optimization (MPSO) for assessing the form error from the set of coordinate measured data points. In the classical algorithm of the particle swarm optimization (PSO), the value of the candidate solution is updated from its existing value without actually comparing the value obtained in the consecutive iterations for fitness. This behaviour attributes to a lack of exploitation ability in the defined search space. The proposed algorithm generates new swarm position and fitness solution for the objective function through an improved and modified search equation based on a proposed heuristic step. In this step, the swarm searches around the best solution of the previous iteration for improving the swarm exploitation capability. The particle swarm uses greedy selection procedure to choose the best candidate solution. A non-linear minimum zone objective function is formulated mathematically for different types of form errors and then optimized using proposed MPSO. Five benchmark functions are used to prove the effectiveness of the modified algorithm, which is verified by comparing its solution and convergence with those obtained from the established algorithms namely PSO and genetic algorithm (GA). Finally, the result of the proposed algorithm for form error evaluation is compared with previous work and other established nature-inspired algorithms. The results demonstrate that the proposed MPSO algorithm is more efficient and accurate than the other conventional heuristic optimization algorithms. Furthermore, it is well suited for form error evaluation using CMM acquired data.

关键词： Form error modified particle swarm algorithm minimum zone evaluation

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A modified PSO algorithm for Aero-engine Non-linear Programming Model

A Modified PSO Algorithm for Aero-engine Non-linear Programm...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Wang, Yonghua Li, Benwei Sun, Tao Jiang, Keyi Wang, Xiuxia Naval Aeronaut & Astronaut Univ Dept Aerocraft Engn Yantai Shandong Peoples R China

ISBN: (纸本)9781424467129

According to the characteristics of aero-engine's accelerating process, the control parameters and constraints are determined for the optimal acceleration control. A non-linear programming model is established to the optimal acceleration control for a certain turbofan engine, which high pressure rotor speed and high pressure turbine inlet temperature, are as the objective function of accelerating control. From two factors, increasing the diversity of particle swarm and accelerating the convergence speed, the particle swarm algorithm has been modified. By judging the particle properties, the particle swarm algorithm has be expanded from the feasible region to the entire solution space. The optimization simulation of control laws is performed using the modified particle swarm optimization for a turbofan engine's accelerating process. Results show that the algorithm has a strong border search capability and the engine's accelerating abilty has been enhanced.

关键词： Aero-engine modified particle swarm algorithm Optimal control simulation model

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