Efficient optimization of plane trusses
Advances in Engineering Software
Design of truss-structures for minimum weight using genetic algorithms
Finite Elements in Analysis and Design
Layout optimisation of trusses using simulated annealing
Advances in Engineering Software - Engineering computational technology
Short Communication: Fuzzy multiobjective optimization of truss-structures using genetic algorithm
Advances in Engineering Software
Advantages Of Evolutionary Computation Used For Exploration In The Creative Design Process
Journal of Integrated Design & Process Science
Solving the feeder bus network design problem by genetic algorithms and ant colony optimization
Advances in Engineering Software
Shape and discrete sizing optimization of timber trusses by considering of joint flexibility
Advances in Engineering Software
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Vector quantization using the firefly algorithm for image compression
Expert Systems with Applications: An International Journal
Mixed variable structural optimization using Firefly Algorithm
Computers and Structures
Expert Systems with Applications: An International Journal
Multilevel minimum cross entropy threshold selection based on the firefly algorithm
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
A survey of non-gradient optimization methods in structural engineering
Advances in Engineering Software
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This paper presents an efficient single-stage Firefly-based algorithm (FA) to simultaneously optimise the size, shape and topology of truss structures. The optimisation problem uses the minimisation of structural weight as its objective function and imposes displacement, stress and kinematic stability constraints. Unstable and singular topologies are disregarded as possible solutions by checking the positive definiteness of the stiffness matrix. Because cross-sectional areas are usually defined by discrete values in practice due to manufacturing limitations, the optimisation algorithm must assess a mixed-variable optimisation problem that includes both discrete and continuous variables at the same time. The effectiveness of the FA at solving this type of optimisation problem is demonstrated with benchmark problems, the results for which are better than those reported in the literature and obtained with lower computational costs, emphasising the capabilities of the proposed methodology. In addition, the procedure is capable of providing multiple optima and near-optimal solutions in each run, providing a set of possible designs at the end of the optimisation process.