Comparative performance of meta-heuristic algorithms for mass minimisation of trusses with dynamic constraints

•Comparative performance of 24 meta-heuristics for solving truss mass minimisation with dynamic constraints.•Comparison of penalty function techniques for handling dynamic constraints.•Best results for the benchmark problems of truss mass minimisation with dynamic constraints. This paper investigate...

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Vydáno v:Advances in engineering software (1992) Ročník 75; s. 1 - 13
Hlavní autoři: Pholdee, Nantiwat, Bureerat, Sujin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2014
Témata:
Algorithms
Dynamic constraints
Dynamics
Evolution
Evolutionary algorithms
Heuristic methods
Meta-heuristics comparative performance
Optimization
Penalty function
Penalty function techniques
Searching
Shap and sizing optimisation
Truss minimisation
Trusses
Evolutionary algorithms
Truss minimisation
Meta-heuristics comparative performance
Penalty function techniques
Dynamic constraints
Shap and sizing optimisation
ISSN:0965-9978
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Shrnutí:•Comparative performance of 24 meta-heuristics for solving truss mass minimisation with dynamic constraints.•Comparison of penalty function techniques for handling dynamic constraints.•Best results for the benchmark problems of truss mass minimisation with dynamic constraints. This paper investigates the search performances of various meta-heuristics (MHs) for solving truss mass minimisation with dynamic constraints. Several established MHs were used to solve five truss optimisation problems. The results obtained from using the various MHs were statistically compared based upon convergence rate and consistency. It was found that the best optimisers for this design task are evolution strategy with covariance matrix adaptation (CMAES) and differential evolution (DE). Furthermore, the best penalty function technique was discovered while four penalty function techniques assigned with several parameter settings were used in combination with the five best optimisers to solve the truss optimisation problems.
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ISSN:0965-9978
DOI:10.1016/j.advengsoft.2014.04.005