Fuzzy optimization of cold-formed steel sheeting using genetic algorithms

In this paper, genetic algorithms are applied for optimization of dimensions of cold-formed steel trapezoidal sheeting. The objective of the optimization is to obtain the minimum weight subjected to the given constraints in accordance with Eurocode 3, Part 1.3. In traditional optimization, these constraints are defined with crisp number. However, in practical engineering, constraints with a small certain percentage of violation can be acceptable. Thus, in this research, sheeting is optimized to satisfy the constraints considering the fuzziness so that the optimization is more practical from the engineering point of view. The better performance of introducing the fuzziness into a constraints-handling technique has been demonstrated with a design example.