Sensitivity analysis based on variance decomposition for factors in bat algorithm

• Published in: Engineering computations Vol. 36; no. 5; pp. 1608 - 1625
• Main Authors: Yin, Shi, Zhu, Ming
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 15.08.2019; Emerald Group Publishing Limited
• Subjects: Algorithms; Behavior; Decomposition; Dependence; Diversification; Error analysis; Monte Carlo simulation; Screens; Sensitivity analysis; Variance analysis; Uncertainty; Variance decomposition; Sensitivity analysis; Bat algorithm; Metaheuristic algorithm; Stochastic process
• ISSN: 0264-4401; 1758-7077
• DOI: 10.1108/EC-09-2018-0402

Purpose 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/approach This 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. Findings The 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/value This paper quantifies the dependence relationship of BA’s behaviour on the factors that could affect outputs using sensitivity analysis based on variance decomposition.