|Table of Contents|

Adaptive particle swarm optimization algorithm based on discrete estimate strategy of diversity

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

Issue:
2013年03期
Page:
12-
Research Field:
Publishing date:

Info

Title:
Adaptive particle swarm optimization algorithm based on discrete estimate strategy of diversity
Author(s):
Tang Kezong12Xiao Xuan1Jia Jianhua1Xu Xing1
1.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333000,China; 2.School of Information Engineering,Nanchang Institute of Technology,Nanchang 330029,China
Keywords:
discrete estimate strategy of diversity particle swarm optimization entropy mutation strategy
PACS:
TP301.6
DOI:
-
Abstract:
In order to balance the ability between the global searching ability of particles and the optimization speed by enhancing population diversity,an adaptive particle swarm optimization(APSO)algorithm is proposed here.The diversity of standard particle swarm optimization(SPSO)algorithm is analyzed based on population entropy and a discrete estimate strategy of diversity is given.The dynamic function relationship between inertia weight and diversity of the SPSO algorithm is analyzed to balance the trade-off between exploration and exploitation and incorporated into the APSO algorithm.To avoid obtaining partial optimal solutions in the searching later stage,the APSO algorithm introduces a mutation strategy to enhance the diversity of the population.The simulation results show that compared with the dissipative particle swarm optimization(DPSO)algorithm,the APSO algorithm enhances the searching ability of the unexplored space and accelerates the searching process of the particles in the whole solution space.In the development phase,the inertia weight decreases with the decrease of the diversity; in the prospection phase,the inertia weight increases with the increase of the diversity.The APSO algorithm balances the global searching ability and the local careful searching ability,and the particles can search out the area of the optimum solution in a wider space,and develop careful searching.

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Memo

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Last Update: 2013-03-25