Optimising frame structures by different strategies of genetic algorithms
Finite Elements in Analysis and Design
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Structural frames robust optimum design under uncertain loads is handled simultaneously minimizing the constrained mass (adding structural mass and constraint average distribution), as well as the constraint violation distribution standard deviation, using the non-dominated sorting genetic algorithm NSGA-II. The consideration of external loads as random variables is handled by the use of Monte-Carlo simulations for each structural candidate solution. A variance-reduction inspired simulation procedure based in engineering design knowledge is proposed and applied in a test case, allowing a high computational cost reduction without harming the non-dominated front quality. Results obtain a solution set that allow selecting minimum mass optimum designs and maximum robustness for external load uncertainty.