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Sensitivity analysis based on variance decomposition for factors in bat algorithm

Sensitivity analysis based on variance decomposition for factors in bat algorithm This paper aims to quantify the dependence relationship of bat algorithm’s (BA) behaviour on the factors that could possibly affect the outputs, and rank the importance of the various uncertain factors thus suggesting research priorities.Design/methodology/approachThis paper conducts a sensitivity analysis based on variance decomposition of factors in both of original and improved BA. The data sets for sensitivity analysis are generated by optimal Latin hyper sampling in the design of experiment. The optimal factor sets are screened by stochastic error bar measures for the effective and robust implementation of BA.FindingsThe paper reveals the inner dependent relationship between factors and output in both of original and improved BA. It figures out the weakness in original BA and improves that. It suggests that uncertainty brought about by factors are mainly caused by the interaction effect and all the higher-order term in sensitivity indices for both of original and improved BA. It ranks the main effect and the total effect of factors and screens out some optimal factor sets for BA.Originality/valueThis paper quantifies the dependence relationship of BA’s behaviour on the factors that could affect outputs using sensitivity analysis based on variance decomposition. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations Emerald Publishing

Sensitivity analysis based on variance decomposition for factors in bat algorithm

Yin, Shi; Zhu, Ming
Engineering Computations , Volume 36 (5): 18 – Aug 15, 2019
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18 pages

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0264-4401
DOI
10.1108/ec-09-2018-0402
Publisher site
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Abstract

This paper aims to quantify the dependence relationship of bat algorithm’s (BA) behaviour on the factors that could possibly affect the outputs, and rank the importance of the various uncertain factors thus suggesting research priorities.Design/methodology/approachThis paper conducts a sensitivity analysis based on variance decomposition of factors in both of original and improved BA. The data sets for sensitivity analysis are generated by optimal Latin hyper sampling in the design of experiment. The optimal factor sets are screened by stochastic error bar measures for the effective and robust implementation of BA.FindingsThe paper reveals the inner dependent relationship between factors and output in both of original and improved BA. It figures out the weakness in original BA and improves that. It suggests that uncertainty brought about by factors are mainly caused by the interaction effect and all the higher-order term in sensitivity indices for both of original and improved BA. It ranks the main effect and the total effect of factors and screens out some optimal factor sets for BA.Originality/valueThis paper quantifies the dependence relationship of BA’s behaviour on the factors that could affect outputs using sensitivity analysis based on variance decomposition.

Journal

Engineering Computations – Emerald Publishing

Published: Aug 15, 2019

Keywords: Uncertainty; Sensitivity analysis; Variance decomposition; Bat algorithm; Metaheuristic algorithm; Stochastic process

References

  • The development of a changing range genetic algorithm
    Amirjanov, A.
  • Global sensitivity analysis for structure system with multivariate outputs
    Fei, W.
  • A comprehensive review of firefly algorithms
    Fister, I.; Yang, X.S.; Brest, J.
  • Optimization by simulated annealing
    Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P.
  • A new Meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice
    Lee, K.S.; Geem, Z.W.
  • A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization
    Meng, X.B.; Gao, X.Z.; Liu, Y.; Zhang, H.
  • An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems
    Osaba, E.; Yang, X.S.; Diaz, F.; Lopez-Garcia, P.; Carballedo, R.
  • Optimal Latin-hypercube experiments
    Park, J.S.
  • An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing
    Pholdee, N.; Bureerat, S.
  • On the relative importance of input factors in mathematical models: safety assessment for nuclear waste disposal
    Saltelli, A.; Tarantola, S.
  • Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index
    Saltelli, A.; Annoni, P.; Azzini, I.; Campolongo, F.; Ratto, M.; Tarantola, S.
  • Sensitivity analysis of expensive black-box systems using metamodeling
    Steenkiste, T.V.; van der Herten, J.; Couckuyt, I.; Dhaene, T.
  • Three-dimensional path planning for UCAV using an improved bat algorithm
    Wang, G.G.; Chu, H.E.; Mirjalili, S.
  • Engineering optimization with particle swarm
    Xiaohui, H.; Eberhart, R.C.; Yuhui, S.
  • A new metaheuristic bat-inspired algorithm
    Yang, X.S.