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Chaotic Particle Swarm Optimization With Adaptive Inertia Weight(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2012年01期
Page:
64-69
Research Field:
Publishing date:

Info

Title:
Chaotic Particle Swarm Optimization With Adaptive Inertia Weight
Author(s):
Xu Yujie1; Qiu Lei2; Liu Qing1
1.School of Computer Science and Technology,Nanjing Normal University,Nanjing 210046,China
Keywords:
PSO algorithms; premature convergence; chaos population; adaptive inertia weight
PACS:
TP18
DOI:
-
Abstract:
To overcome the problem of premature convergence and local optimal in conventional particle swarm optimization ( PSO) ，a new adaptive inertia weight chaos particle swarm optimization ( ACPSO) is presented． The algorithm generates initial population with segmented logistic map，and varies inertia weight dynamically based on the evolutionary state of the population． After the detailed illustrations of how to generate initial population and how to adjust the inertia weight，this paper tests some classical functions with some improved PSO algorithms and ACPSO algorithm． Compared with other algorithms，the ACPSO algorithm not only has a great advantage of convergence property，but also avoids the premature convergence problem effectively，and at the same，it shows the feasibility and validity of the ACPSO algorithm．

References:

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Last Update: 2013-03-11