A combined heuristic optimization technique
2005
Realistic problems of structural optimization are characterized by non-linearity, non-convexity and by continuous and/or discrete design variables. There are non-linear dependencies between the optimised parameters. Real-world problems are rarely decomposable or separable. In this contribution a combined heuristic algorithm is described which is well suited for problems, for which the application-requirements of gradient-based algorithms are not fulfilled. The present contribution describes a combination of the Threshold Accepting Algorithm with Differential Evolution with particular emphasis on structural optimization, it can be classified as a Hybrid Evolutionary Algorithm. The Threshold Accepting Algorithm is similar to Simulated Annealing. Differential Evolution is based on Genetic Algorithms.
Keywords:
- Population-based incremental learning
- Differential evolution
- Cultural algorithm
- Null-move heuristic
- Mathematical optimization
- Meta-optimization
- Genetic algorithm
- Imperialist competitive algorithm
- Metaheuristic
- Machine learning
- Mathematics
- Artificial intelligence
- Evolutionary algorithm
- Algorithm
- Simulated annealing
- Computer science
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