Some Recent Advances in the Integrated Layout Design of Multicomponent Systems

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Weihong Zhang ◽  
Liang Xia ◽  
Jihong Zhu ◽  
Qiao Zhang
Keyword(s):  
Volume Fraction ◽  
Polynomial Interpolation ◽  
Circle Method ◽  
Layout Design ◽  
Local Optimum ◽  
Multicomponent Systems ◽  
Structure Topology ◽  
Detail Design ◽  
Gradient Based ◽  
Integrated Layout Design

We provide an introduction and state of the art overview of integrated layout design of multicomponent systems. We review several packing optimization and overlap detection strategies, some tree-based methods, such as octrees and spheretrees, and a finite circle method (FCM) proposed to favor gradient-based optimization algorithms. Integrated layout design techniques for simultaneous packing and structure topology optimization of multicomponent systems are reviewed; two typical approaches for system stiffness maximization are reviewed and compared in detail. Design of multicomponent systems under inertia forces is presented using polynomial interpolation models; constraints to the centroid position, moment of inertia, and volume fraction are included. Applications to piezoelectric multi-actuated microtools and integrated layout design of bridge systems are presented. Finally, the effectiveness of the FCM, applications to 3D problems, and local optimum phenomena are discussed.

scholarly journals Image Gradient-based Joint Direct Visual Odometry for Stereo Camera

2017 ◽  
Author(s):  
Jianke Zhu
Keyword(s):  
Visual Odometry ◽  
Research Problem ◽  
Feature Representation ◽  
Local Optimum ◽  
Multi Scale ◽  
Illumination Changes ◽  
Pyramid Scheme ◽  
Gradient Based ◽  
Feature Based ◽  
Dense 3D Reconstruction

Visual odometry is an important research problem for computer vision and robotics. In general, the feature-based visual odometry methods heavily rely on the accurate correspondences between local salient points, while the direct approaches could make full use of whole image and perform dense 3D reconstruction simultaneously. However, the direct visual odometry usually suffers from the drawback of getting stuck at local optimum especially with large displacement, which may lead to the inferior results. To tackle this critical problem, we propose a novel scheme for stereo odometry in this paper, which is able to improve the convergence with more accurate pose. The key of our approach is a dual Jacobian optimization that is fused into a multi-scale pyramid scheme. Moreover, we introduce a gradient-based feature representation, which enjoys the merit of being robust to illumination changes. Furthermore, a joint direct odometry approach is proposed to incorporate the information from the last frame and previous keyframes. We have conducted the experimental evaluation on the challenging KITTI odometry benchmark, whose promising results show that the proposed algorithm is very effective for stereo visual odometry.

scholarly journals Parameter Identification in Nonlinear Mechanical Systems with Noisy Partial State Measurement Using PID-Controller Penalty Functions

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1084 ◽  
Author(s):  
R. Manikantan ◽  
Sayan Chakraborty ◽  
Thomas K. Uchida ◽  
C. P. Vyasarayani
Keyword(s):  
Pid Controller ◽  
Optimization Problem ◽  
Mechanical Systems ◽  
Global Optimum ◽  
Magnetorheological Damper ◽  
Local Optimum ◽  
Nonlinear Mechanical ◽  
Gradient Based ◽  
Engine Mount ◽  
Nonlinear Mechanical Systems

Dynamic models of physical systems often contain parameters that must be estimated from experimental data. In this work, we consider the identification of parameters in nonlinear mechanical systems given noisy measurements of only some states. The resulting nonlinear optimization problem can be solved efficiently with a gradient-based optimizer, but convergence to a local optimum rather than the global optimum is common. We augment the dynamic equations with a morphing parameter and a proportional–integral–derivative (PID) controller to transform the objective function into a convex function; the global optimum can then be found using a gradient-based optimizer. The morphing parameter is used to gradually remove the PID controller in a sequence of steps, ultimately returning the model to its original form. An optimization problem is solved at each step, using the solution from the previous step as the initial guess. This strategy enables use of a gradient-based optimizer while avoiding convergence to a local optimum. The efficacy of the proposed approach is demonstrated by identifying parameters in the van der Pol–Duffing oscillator, a hydraulic engine mount system, and a magnetorheological damper system. Our method outperforms genetic algorithm and particle swarm optimization strategies, and demonstrates robustness to measurement noise.

Blade Shape Optimization of Marine Propeller via Genetic Algorithm for Efficiency Improvement

2012 ◽  
Author(s):  
Ramin Taheri ◽  
Karim Mazaheri
Keyword(s):  
Genetic Algorithm ◽  
Parallel Processing ◽  
Thickness Distribution ◽  
Processing System ◽  
Optimization Method ◽  
Initial Guess ◽  
Hydrodynamic Performance ◽  
Local Optimum ◽  
Lattice Method ◽  
Gradient Based

In this paper, a numerical optimization method has been carried out to optimize the shape and efficiency of a propeller. For analysis of the hydrodynamic performance parameters, an extended vortex lattice method was used by implementing an open-source code which is called OpenProp. The method of optimization is a non-gradient based algorithm. After a trade-off between a few gradient-based and non-gradient based algorithms, it is found that the problem of being trapped in local optimum solutions can be easily solved by choosing nongradient based ones. Hence, modified Genetic algorithm is used to implement the so-called hydrodynamic performance analyzer code. The objective function is to maximize efficiency by considering the design variables as non-dimensional blade’s chord and thickness distribution along the blade. For initial guess data of the DTRC 4119 propeller which are radially distributed along the blade is used. The hydrodynamic performance analyzer code is modified by a higher order QuasiNewton scheme. Also hybrid function is used to accurate the convergence. Finally, parallel processing implementation on the codes has been done successfully. To improve the computation speed, the algorithm is improved to be extended on a parallel processing system. The process of parallelizing has been done simplicity by Matlab M-code and the number of cores has been chosen as 4. The final results verify both fast convergence in comparison with common methods and nearly 10% improvement in propeller efficiency (mechanical efficiency of the system) which is significant for these kinds of problems. Therefore, the algorithm starts with geometry arrived at by other researchers and improves it to a more efficient propeller.

A comparative study of gradient-based and meta-heuristic optimization methods using Griewank benchmark function/ Um estudo comparativo de métodos de otimização baseados em gradientes e meta-heurísticos usando a função de benchmark do Griewank

2021 ◽  
Vol 7 (6) ◽  
pp. 55341-55350
Author(s):  
Carlos Eduardo Rambalducci Dalla ◽  
Wellington Betencurte da Silva ◽  
Júlio Cesar Sampaio Dutra ◽  
Marcelo José Colaço
Keyword(s):  
Heuristic Method ◽  
Computation Time ◽  
Optimization Methods ◽  
Search Space ◽  
Performance Comparison ◽  
Local Optimum ◽  
Multimodal Function ◽  
Gradient Based ◽  
Griewank Function ◽  
Global And Local

Optimization methods are frequently applied to solve real-world problems such, engineering design, computer science, and computational chemistry. This paper aims to compare gradient-based algorithms and the meta-heuristic particle swarm optimization to minimize the multidimensional benchmark Griewank function, a multimodal function with widespread local minima. Several approaches of gradient-based methods such as steepest descent, conjugate gradient with Fletcher-Reeves and Polak-Ribiere formulations, and quasi-Newton Davidon-Fletcher-Powell approach were compared. The results presented showed that the meta-heuristic method is recommended for function with this behavior because is no needed prior information of the search space. The performance comparison includes computation time and convergence of global and local optimum.

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