|Table of Contents|

Improved particle swarm optimization algorithm for solving complex optimization problems

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2015年04期
Page:
386-
Research Field:
Publishing date:

Info

Title:
Improved particle swarm optimization algorithm for solving complex optimization problems
Author(s):
Tang Kezong13Li Huiying2Li Juan2Luo Limin1
1.School of Computer Science and Engineering,Southeast University,Nanjing 210018,China; 2.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China; 3.Loboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry Education,NUST,Nanjing 210094,China
Keywords:
particle swarm optimization optimization strategies optimization problems particles searching cognitive coefficients social coefficients
PACS:
TP391.41
DOI:
-
Abstract:
To improve the efficiency of the particle swarm optimization algorithm for particles searching optimal solutions,an improved particle swarm optimization(IPSO)algorithm is proposed based on the standard PSO.Each particle has a corresponding grading standard by trajectory analysis of flight path,and two dynamic models of coefficients are designed for the cognitive coefficient and social one,respectively.In addition,through the introduction of the migration strategy,the newly obtained particles are more likely closer to the global optimal solution to a certain extent,and it is easy to jump out of the local optimal solution to search for the optimal solution.Simulation results show the IPSO algorithm has powerful optimizing ability and higher search veracity.The experimental data on the test criterion verify the effectiveness and the feasibility of the improved algorithm.

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Last Update: 2015-08-31