A Dyad Search Algorithm for Solving Planar Linkages Using the Dyad Method

1992 ◽  
Author(s):  
Lofti Romdhane
Keyword(s):  
Search Algorithm ◽  
Class Ii ◽  
Graph Representation ◽  
Step Size ◽  
Lower Pair ◽  
Fast Solution ◽  
Complete Automation ◽  
Newton Raphson ◽  
Dyad Analysis ◽  
Planar Linkages

Abstract Based on graph representation of planar linkages, a new algorithm was developed to identify the different dyads of a mechanism. A dyad or class II group, is composed of two binary links connected by either a revolute (1) or a slider (0) pair with provision for attachment to other links by lower pair connectors located at the end of each link. There are five types of dyads: the D111, D101, D011, D001, and D010. The dyad analysis of a mechanism is predicated on the ability to construct the system from one or more of the five binary structure groups or class II groups. If the mechanism is complicated and several dyads are involved, the task of identifying these dyads by inspection could be difficult and time consuming for the user. This algorithm allows a complete automation of this task. This algorithm is based on the Dijkstra’s algorithm, for finding the shortest path in a graph, and it is used to develop a computer program, called KAMEL: Kinematic Analysis of MEchanical Linkages, and implemented on an IBM-PC PS/2 model 80. When compared to algorithmic methods, like the Newton-Raphson, the dyad method proved to be a very efficient one and requires as little as one tenth of the time needed by the method using Newton-Raphson algorithm. Moreover, the dyad method yields the exact solution of the position analysis and no initial estimates are needed to start the analysis. This method is also insensitive to the value of the step-size crank rotation, therefore, allowing a very accurate and fast solution of the mechanism at any position of the input link.

Dynamic analysis of planar elastic mechanisms using the dyad method

2003 ◽  
Vol 217 (1) ◽  
pp. 1-14 ◽  
Author(s):  
L Romdhane ◽  
H Dhuibi ◽  
H Bel Hadj Salah
Keyword(s):  
Class Ii ◽  
Graph Representation ◽  
Elastic Mechanism ◽  
Complete Automation ◽  
Newton Raphson ◽  
Set Up ◽  
Dyad Analysis ◽  
Planar Linkages ◽  
Raphson Method ◽  
Good Agreement

Based on graph representation of planar linkages, a new algorithm has been developed to identify the different dyads of a mechanism. A dyad, or class II group, is composed of two binary links connected by either a revolute (1) or a slider (0) pair, with provision for attachment of other links by lower pair connectors located at the end of each link. There are five types of dyad: D111, D101, D011, D001 and D010. The dyad analysis of a mechanism is predicated on the ability to construct the system from one or more of the five binary structure groups or class II groups. If the mechanism is complicated and several dyads are involved, the task of identifying these dyads, by inspection, can be difficult and time consuming for the user. This algorithm allows complete automation of this task. It is based on Dijkstra's algorithm for finding the shortest path in a graph. When compared with algorithmic methods, such as the Newton-Raphson method, the dyad method proved to be a very efficient one and requires as little as one-tenth of the time needed by the method using the Newton-Raphson algorithm. The second part of this work presents an extension of the dyad method to non-rigid or elastic mechanisms. Here also, this method is predicated on the ability to subdivide the elastic mechanism into elastic dyads. The solution for each type of elastic dyad is derived and can be applied to each dyad in the mechanism. Therefore, a solution of the complete elastic mechanism is possible when the mechanism is made of dyads only. This method makes a powerful and simple tool for analysing complex elastic mechanisms. Moreover, the complexity of the model does not increase as the mechanism becomes more complex. The D111 dyad is taken as an example to demonstrate this method. A finite element (FE) analysis was made for this type of dyad, and an experimental set-up was built to validate the analysis. The dyad-FE results were in good agreement with the experimental ones.

Performances of Adaptive Cuckoo Search Algorithm in Engineering Optimization

2016 ◽  
pp. 676-699 ◽  
Author(s):  
Pauline Ong ◽  
S. Kohshelan
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Breeding Strategy ◽  
Function Optimization ◽  
Engineering Optimization ◽  
Optimal Solutions ◽  
Step Size ◽  
Swarm Optimization ◽  
Searching Strategy

A new optimization algorithm, specifically, the cuckoo search algorithm (CSA), which inspired by the unique breeding strategy of cuckoos, has been developed recently. Preliminary studies demonstrated the comparative performances of the CSA as opposed to genetic algorithm and particle swarm optimization, however, with the competitive advantage of employing fewer control parameters. Given enough computation, the CSA is guaranteed to converge to the optimal solutions, albeit the search process associated to the random-walk behavior might be time-consuming. Moreover, the drawback from the fixed step size searching strategy in the inner computation of CSA still remain unsolved. The adaptive cuckoo search algorithm (ACSA), with the effort in the aspect of integrating an adaptive search strategy, was attached in this study. Its beneficial potential are analyzed in the benchmark test function optimization, as well as engineering optimization problem. Results showed that the proposed ACSA improved over the classical CSA.

scholarly journals Adaptive Cuckoo Search Algorithm for Unconstrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Pauline Ong
Keyword(s):  
Marked Improvement ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimal Solutions ◽  
Step Size ◽  
Adjustment Strategy ◽  
Global Optimal ◽  
Adaptive Step ◽  
Size Adjustment

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

Experimental Analysis of Search-Based Selection of Sample Points for Straightness and Flatness Estimation

2005 ◽  
Vol 127 (1) ◽  
pp. 96-103 ◽  
Author(s):  
M. Affan Badar ◽  
Shivakumar Raman ◽  
P. Simin Pulat ◽  
Randa L. Shehab
Keyword(s):  
Experimental Analysis ◽  
Manufacturing Process ◽  
Adaptive Sampling ◽  
Least Squares Method ◽  
Search Algorithm ◽  
Large Population ◽  
Population Sample ◽  
Step Size ◽  
Factor C ◽  
Optimization Search

In earlier work [Bader et al., ASME J. Manuf. Sci. Eng. 125(2), pp. 263–271 (2003); Int. J. Mach. Tools Manuf. 45(1), pp. 63–75 (2005)] the authors have presented an adaptive sampling method utilizing manufacturing error patterns and optimization search techniques for straightness and flatness evaluation. The least squares method was used to compute a tolerance zone. In this paper, experimental analysis is performed to verify the sturdiness of the adaptive sampling procedure. Experiments are carried out to investigate the effects of different factors on the sample size and absolute percent error of the estimated tolerance from that of a large population sample. Twelve 7075-T6 aluminum plates are end-milled and 12 cast iron plates are face-milled. Two sets of four plates from each lot are selected randomly, one each for straightness and flatness estimation. Factor A used in both straightness and flatness analyses is manufacturing process (i.e., surface error profile). Factor B for straightness is step size whereas for flatness it is search strategy (i.e., number of bad moves and restart allowed). Factor C for flatness is search algorithm (i.e., tabu and hybrid). Plates are nested within the levels of manufacturing process. The results have been analyzed and compared with other sampling methods. The analyses reveal that the current approach is more efficient and reliable.

Fast Solution of Transient Elastohydrodynamic Line Contact Problems Using the Trajectory Piecewise Linear Approach

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Daniel Maier ◽  
Corinna Hager ◽  
Hartmut Hetzler ◽  
Nicolas Fillot ◽  
Philippe Vergne ◽  
...  
Keyword(s):  
Piecewise Linear ◽  
Contact Problems ◽  
Galerkin Projection ◽  
Line Contact ◽  
Reduced Order ◽  
Fast Solution ◽  
Speed Up ◽  
Newton Raphson ◽  
First Time ◽  
Piecewise Linear Approach

In order to obtain a fast solution scheme, the trajectory piecewise linear (TPWL) method is applied to the transient elastohydrodynamic (EHD) line contact problem for the first time. TPWL approximates the nonlinearity of a dynamical system by a weighted superposition of reduced linearized systems along specified trajectories. The method is compared to another reduced order model (ROM), based on Galerkin projection, Newton–Raphson scheme and an approximation of the nonlinear reduced system functions. The TPWL model provides further speed-up compared to the Newton–Raphson based method at a high accuracy.

Multi-Swarm Cuckoo Search Algorithm with Q-Learning Model

2020 ◽  
Author(s):  
Juan Li ◽  
Dan-dan Xiao ◽  
Ting Zhang ◽  
Chun Liu ◽  
Yuan-xiang Li ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Size Control ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Local Optimum ◽  
Step Size ◽  
Q Learning ◽  
Fitness Value ◽  
The Individual ◽  
Evolution Effect

Abstract As a novel swarm intelligence optimization algorithm, cuckoo search (CS) has been successfully applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS has some disadvantages, such as premature convergence, easy to fall into local optimum and poor balance between exploitation and exploration. In order to improve the optimization performance of the CS algorithm, a new CS extension with multi-swarms and Q-Learning namely MP-QL-CS is proposed. The step size strategy of the CS algorithm is that an individual fitness value is examined based on a one-step evolution effect of an individual instead of evaluating the step size from the multi-step evolution effect. In the MP-QL-CS algorithm, a step size control strategy is considered as action, which is used to examine the individual multi-stepping evolution effect and learn the individual optimal step size by calculating the Q function value. In this way, the MP-QL-CS algorithm can increase the adaptability of individual evolution, and a good balance between diversity and intensification can be achieved. Comparing the MP-QL-CS algorithm with various CS algorithms, variants of differential evolution (DE) and improved particle swarm optimization (PSO) algorithms, the results demonstrate that the MP-QL-CS algorithm is a competitive swarm algorithm.

scholarly journals Eccentricity Parameters Identification for a Motorized Spindle System Based on Improved Maximum Likelihood Method

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wengui Mao ◽  
Chaoliang Hu ◽  
Jianhua Li ◽  
Zhonghua Huang ◽  
Guiping Liu
Keyword(s):  
Maximum Likelihood ◽  
Measurement Errors ◽  
Maximum Likelihood Method ◽  
Search Algorithm ◽  
A Priori ◽  
Likelihood Method ◽  
Step Size ◽  
Spindle System ◽  
Grinding Machine ◽  
Search Interval

As a kind of rotor system, the electric spindle system is the core component of the precision grinding machine. The vibration caused by the mass imbalance is the main factor that causes the vibration of the grinding machine. Identifying the eccentricity parameters in an electric spindle system is a key issue in eliminating mass imbalances. It is difficult for engineers to understand the approximate range of eccentricity by experience; that is, it is difficult to obtain a priori information about eccentricity. At the same time, due to the geometric characteristics of the electrospindle system, the material factors and the randomness of the measurement response, these uncertain factors, even in a small case, are likely to cause large deviations in the eccentricity recognition results. The search algorithm used in the maximum likelihood method to identify the eccentricity parameters of the electrospindle system is computationally intensive, and the sensitivity in the iterative process brings some numerical problems. This paper introduces an Advance-Retreat Method (ARM) of the search interval to the maximum likelihood method, the unknown parameter increment obtained by the maximum likelihood method is used as the step size in the iteration, and the Advance-Retreat Method of the search interval is used to adjust the next design point so that the objective function value is gradually decreasing. The recognition results under the three kinds of measurement errors show that the improved maximum likelihood method improves the recognition effect of the maximum likelihood method and can reduce the influence of uncertainty factors on the recognition results, and the robustness is satisfactory.

scholarly journals An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wentan Jiao ◽  
Wenqing Chen ◽  
Jing Zhang
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Threshold Function ◽  
Size Factor ◽  
Step Size ◽  
Multiple Thresholds

Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image segmentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly, the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under four different thresholding conditions.

A Sensitivity-Based Pattern Search Algorithm for 3D Component Layout

2002 ◽  
Author(s):  
Chandankumar Aladahalli ◽  
Jonathan Cagan ◽  
Kenji Shimada
Keyword(s):  
Objective Function ◽  
Search Algorithm ◽  
Search Space ◽  
Pattern Search ◽  
Step Size ◽  
Simple Method ◽  
Pattern Search Algorithm ◽  
Component Layout ◽  
Extended Pattern Search ◽  
Better Than

This paper introduces the Sensitivity-based Pattern Search (SPS) algorithm for 3D component layout. Although based on the pattern search algorithm, SPS differs in that at any given step size the algorithm does not necessarily perturb the search space along all possible search dimensions. Instead all possible perturbations, or moves are ranked in decreasing order of their effect on the objective function and are applied in that order. The philosophy behind this algorithm is that moves that affect the objective function more must be applied before the moves that affect the objective function less. We call this effect on the objective function the sensitivity of the objective function to a particular move and present a simple method to quantify it. This algorithm performs better than the previous Extended Pattern Search algorithm with decrease in run time of up to 28%.

Sign in / Sign up

Export Citation Format

Share Document