HybridParticleSwarmOptimizationAlgorithmBasedon#br#
DynamicAdjustmentofInertiaWeight

doi:10.3969/j.issn.1006-2475.2018.06.006

Abstract

Abstract:

Whensolvinghighdimensionalnonlinearproblems,ParticleSwarmOptimizationalgorithmiseasytofallintolocaloptimalsolution.Inthiscase,anewnonlinearadaptiveweightadjustmentstrategybasedonSigmodfunctionisproposed.Inaddition,aLatinHypercubeSamplingmethodisusedtoproduceahomogeneousinitialpopulation,andanicheeliminationstrategyisusedtoenhancetheglobaloptimizationabilityofthealgorithm.Finally,sixstandardtestfunctionsareusedtotesttheperformanceoftheimprovedalgorithm.Theresultsshowthattheimprovedparticleswarmoptimizationalgorithmachievessatisfactoryresultsinconvergencespeed,convergenceaccuracyandtheacquisitionofglobaloptimalsolution.

Key words: Latinhypercubesampling, adaptiveinertiaweight, fitnessvariance, nicheeliminationstrategy

CLC Number:

TP18

GUMing-liang,LIMin. HybridParticleSwarmOptimizationAlgorithmBasedon#br# DynamicAdjustmentofInertiaWeight[J]. Computer and Modernization, 2018, 0(06): 25-.

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HybridParticleSwarmOptimizationAlgorithmBasedon#br# DynamicAdjustmentofInertiaWeight

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