Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Layout optimisation of trusses using simulated annealing
Advances in Engineering Software - Engineering computational technology
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
A heuristic particle swarm optimizer for optimization of pin connected structures
Computers and Structures
An improved genetic algorithm with initial population strategy and self-adaptive member grouping
Computers and Structures
An efficient simulated annealing algorithm for design optimization of truss structures
Computers and Structures
Covariance Matrix Adaptation Revisited --- The CMSA Evolution Strategy ---
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
Discrete optimum design of truss structures using artificial bee colony algorithm
Structural and Multidisciplinary Optimization
Artificial Bee Colony algorithm for optimization of truss structures
Applied Soft Computing
Structural and Multidisciplinary Optimization
Improved harmony search algorithms for sizing optimization of truss structures
Computers and Structures
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Following the principles of the state-of-the-art Evolution Strategies in continuous optimization, a novel algorithm is introduced which simultaneously optimizes shape and size of truss structures. The algorithm, called Fully Stressed Design Evolution Strategy (FSD-ES), combines advantages of the well-known deterministic approach of Fully Stressed Design and potent global search of Evolutionary Algorithms. Based on available engineering knowledge on truss analysis, a novel adaptive penalty term is also introduced and utilized to reinforce the near-bound search abilities of the algorithm. Following recent advances in empirical evaluation, the performance of FSD-ES is assessed on a reasonable test suite and compared to the best results available in the literature. Performance for the case without grouping members or exploiting symmetry are also reported which significantly increase the problem dimension, leading to more challenging test problems, more discriminating results and more reliable conclusions. Result comparison demonstrates that for more complicated problems FSD-ES reaches the same fitness noticeably faster and finds a relatively lighter structure than those previously reported in the literature.