Performance of Particle Swarm Optimization and Pattern Search on CEC 2013 real parameter single objective optimization

Author(s):  
K. Remya ◽  
P. Varun ◽  
Prakash Kotecha
2013 ◽  
Vol 694-697 ◽  
pp. 2733-2737
Author(s):  
Qin Zhou ◽  
Ming Hui Zhang ◽  
Hui Yong Chen ◽  
Yong Hui Xie

An optimization design system for fir-tree root of turbine blade has been developed in this paper. In the system, a parametric model of the blade and rim was established based on the parametric design language APDL, and nonlinear contact method was used for analysis by ANSYS, meanwhile some optimization algorithms, such as Pattern Search Algorithm, Genetic Algorithm, Simulated Annealing Algorithm and Particle Swarm Optimization, were adopted to control the optimizing process. Five cases of manufacturing variation in contact surfaces between root and rim were taken into account, and the design objective was to minimize the maximum equivalent stress of root-rim by optimizing eight critical geometrical dimensions of the root and rim. As a result, the maximum equivalent stress of root-rim decreases markedly after the optimization in all cases. In consideration of both precision and computing time, particle swarm optimization is assessed as the best algorithm to solve structure optimization problem in this work. Corresponding to five different cases of manufacturing variation, the maximum equivalent stress of root and rim reduces by 7%, 8%; 27%, 24%; 27%, 22%; 25%, 19%; 10%, 14% using the Particle Swarm Optimization.


2012 ◽  
Vol 532-533 ◽  
pp. 1664-1669 ◽  
Author(s):  
Jun Li Zhang ◽  
Da Wei Dai

For the purpose of overcoming the premature property and low execution efficiency of the Particle Swarm Optimization (PSO) algorithm, this paper presents a particle swarm optimization algorithm based on the pattern search. In this algorithm, personal and global optimum particles are chosen in every iteration by a probability. Then, local optimization will be performed by the pattern search and then the original individuals will be replaced. The strong local search function of the pattern search provides an effective mechanism for the PSO algorithm to escape from the local optimum, which avoids prematurity of the algorithm. Simulation shows that this algorithm features a stronger function of global search than conventional PSO, so that the optimization process can be improved remarkably.


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