A Comparative Study of Eighteen Self-adaptive Metaheuristic Algorithms for Truss Sizing Optimisation

Performance comparison of meta-heuristics (MHs) is conducted for truss sizing design. Six traditional truss sizing design problems with mass objective function subject to displacement and stress constraints were employed for performance test. The test problems have two types with and without includi...

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Bibliographic Details
Published inKSCE journal of civil engineering Vol. 22; no. 8; pp. 2982 - 2993
Main Authors Pholdee, Nantiwat, Bureerat, Sujin
Format Journal Article
LanguageEnglish
Published Seoul Korean Society of Civil Engineers 01.08.2018
Springer Nature B.V
대한토목학회
Subjects
Adaptive algorithms
Algorithms
Buckling
Civil Engineering
Comparative analysis
Comparative studies
Deformation
Design
Eigenvectors
Engineering
Evolution
Evolutionary computation
Frameworks
Geotechnical Engineering & Applied Earth Sciences
Heuristic
Heuristic methods
Industrial Pollution Prevention
Mechanical engineering
Objective function
Performance testing
Performance tests
Population (statistical)
Population number
Problem solving
R&D
Research & development
Shade
Size reduction
Sizing
Static loads
Statistical analysis
Structural Engineering
Trusses
토목공학
performance comparison
truss sizing optimisation
self-adaptive algorithms
meta-heuristics
buckling constraints
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Summary:Performance comparison of meta-heuristics (MHs) is conducted for truss sizing design. Six traditional truss sizing design problems with mass objective function subject to displacement and stress constraints were employed for performance test. The test problems have two types with and without including buckling constraints. Eighteen self-adaptive MHs from literature are employed to tackle the truss sizing problems. The results from implementing the self-adaptive MHs are compared in terms of convergence rate and consistency. It is found that for the test problem without buckling constraints, the top two optimisers according to the statistical Wilcoxon rank sum tests are Success-History Based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) and Success-History Based Adaptive Differential Evolution (SHADE) while the top two optimiser for the test problems with buckling constraints is L-SHADE and L-SHADE with Eigenvector-Based Crossover and Successful-Parent-Selecting Framework (SPS-L-SHADE-EIG). The buckling constraints are significantly important and should be included to truss design subjected to static loads.
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ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-017-0095-y