Constrained Test Problems for Multi-objective Evolutionary Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Layout optimisation of trusses using simulated annealing
Advances in Engineering Software - Engineering computational technology
Particle swarm approach for structural design optimization
Computers and Structures
An efficient simulated annealing algorithm for design optimization of truss structures
Computers and Structures
Evolutionary multiobjective optimization of steel structural systems in tall buildings
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Artificial Bee Colony algorithm for optimization of truss structures
Applied Soft Computing
A distributed Cooperative coevolutionary algorithm for multiobjective optimization
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
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This paper presents an integrated design technique to carry out simultaneous topology, shape and sizing optimisation of a three-dimensional truss structure. Design objectives include mass, compliance, natural frequencies, frequency response function (FRF), and force transmissibility (FT). The Pareto fronts are explored by using: strength Pareto evolutionary algorithm (SPEA2), population-based incremental learning (PBIL), and archived multiobjective simulated annealing (AMOSA). The results obtained from using the optimisers are compared based upon the hypervolume (HV) and generational distance (GD). It is shown that PBIL is the best for optimising compliance and natural frequency, while SPEA2 is superior when dealing with FRF and FT.