The research of PSO algorithms with non-linear time-decreasing inertia weight

The particle swarm optimization (PSO) firstly proposed by Eberhart and Kennedy, is a computational intelligence technique. The inertia weight is an important parameter of PSO algorithm. In this paper, we designed 2 nonlinear time-decreasing inertia weight to use in GCPSO algorithm. At last a series...

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Bibliographic Details
Published in2008 7th World Congress on Intelligent Control and Automation pp. 4002 - 4005
Main Authors Lei wen, Zhaocai xi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Algorithm design and analysis
Convergence
Electricity
Evolutionary computation
GCPSO
non-linear inertia weight
Particle swarm optimization
PSO
Roads
Search problems
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ISBN1424421136
9781424421138
DOI10.1109/WCICA.2008.4593569

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Summary:The particle swarm optimization (PSO) firstly proposed by Eberhart and Kennedy, is a computational intelligence technique. The inertia weight is an important parameter of PSO algorithm. In this paper, we designed 2 nonlinear time-decreasing inertia weight to use in GCPSO algorithm. At last a series of experiment is performed to test the performance of GCPSO with different inertia weight function. for most case, The result indicates the nonlinear time-decreasing inertia weight, especially the convex nonlinear time-decreasing inertia weight has a better performance than linear time-decreasing inertia weight and constant inertia weight.
ISBN:1424421136
9781424421138
DOI:10.1109/WCICA.2008.4593569