Generating optimal topologies in structural design using a homogenization method
Computer Methods in Applied Mechanics and Engineering
Topology optimization of plate structures using a single- or three-layered artificial material model
Advances in Engineering Software
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Compact Unstructured Representations for Evolutionary Design
Applied Intelligence
Genetic Algorithms in Noisy Environments
Machine Learning
Self-Adaptive Genetic Algorithm for Numeric Functions
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Computer simulation of a living cell
Computer simulation of a living cell
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Structural topology optimization using ant colony optimization algorithm
Applied Soft Computing
Topology optimization of structures using modified binary differential evolution
Structural and Multidisciplinary Optimization
A binary particle swarm optimization for continuum structural topology optimization
Applied Soft Computing
Structural and Multidisciplinary Optimization
On the usefulness of non-gradient approaches in topology optimization
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization
Mathematical and Computer Modelling: An International Journal
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Genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization. A two stage adaptive genetic algorithm (TSAGA) involving a self-adaptive island genetic algorithm (SAIGA) for the first stage and adaptive techniques in the second stage is proposed for the use in bit-array represented topology optimization. The first stage, consisting a number of island runs each starting with a different set of random population and searching for better designs in different peaks, helps the algorithm in performing an extensive global search. After the completion of island runs the initial population for the second stage is formed from the best members of each island that provides greater variety and potential for faster improvement and is run for a predefined number of generations. In this second stage the genetic parameters and operators are dynamically adapted with the progress of optimization process in such a way as to increase the convergence rate while maintaining the diversity in population. The results obtained on several single and multiple loading case problems have been compared with other GA and non-GA-based approaches, and the efficiency and effectiveness of the proposed methodology in reaching the global optimal solution is demonstrated.