Title
Synthesis of a Hybrid Five-Bar Mechanism with Particle Swarm Optimization Algorithm
Abstract
Hybrid mechanism is a new type of mechanism with flexible transmission behavior. Hybrid five-bar mechanism is the most representative one of them. In this paper, modeling and analysis for a hybrid five-bar mechanism based on power bond graph theory is introduced. An optimal dimensional synthesis of hybrid mechanism is performed with reference to dynamics objective function. Compared with conventional optimum evaluation methods such as simplex search and Powell method, Particle Swarm Optimization (PSO) algorithm can improve the efficiency of searching in the whole field by gradually shrinking the area of optimization variable. Compared to GA, PSO is easy to implement and there are few parameters to adjust. In order to solve the synthesis problem, integrating PSO optimization algorithm and MATLAB Optimization Toolbox for the constraint equations. Optimum link dimensions are obtained assuming there are no dimensional tolerances or clearances. Finally, a numerical example is carried out, and the simulation results show that the optimization method is feasible and satisfactory for hybrid mechanism.
Year
DOI
Venue
2008
10.1007/978-3-540-87732-5_96
ISNN (1)
Keywords
Field
DocType
particle swarm optimization,hybrid five-bar mechanism,conventional optimum evaluation method,hybrid mechanism,matlab optimization toolbox,powell method,pso optimization algorithm,dimensional tolerance,optimization method,particle swarm optimization algorithm,optimization variable,mechanism,bond graph,optimization,objective function,pso algorithm
MATLAB,Computer science,Artificial intelligence,Imperialist competitive algorithm,Metaheuristic,Particle swarm optimization,Mathematical optimization,Derivative-free optimization,Meta-optimization,Algorithm,Multi-swarm optimization,Bond graph,Machine learning
Conference
Volume
ISSN
Citations 
5263
0302-9743
1
PageRank 
References 
Authors
0.37
2
1
Name
Order
Citations
PageRank
Ke Zhang176.11