The multi-objective robust optimization method considering sensitivity region
Author(s): YANG Ai-min, LI Jie, LIU Wei-xing, ZHANG Liang-jin, YAN Long-ge
Pages: 205-211
Year: 2016 Issue: 2
Journal: Control Theory & Applications
Keyword: sensitivity region; multi-objective robust; robustness index; interval variables; the worst-case;
Abstract: Engineering design problems are usually uncertain because of the random variables. Designers want to design scheme to meet the goal of not only the best performance, but also want to target performance is affected by the uncertainty within the acceptable range. To solve this problem, we propose a multi-objective robust optimization method considering sensitivity region (multi-objective robust optimization based on sensitive region, SR–MORO). SR–MORO can be used to solve optimization design problem involving uncertainty design variables. This method assumes that the uncertainty variables belong to the interval variables, so it does not need to know the probability distribution of random variables. Non-gradient optimization method is used to solve the robust optimization problem, so that the approach is applicable for cases that have discontinuous objective and constraint functions with respect to uncontrollable parameters. When the parameters changed much over the linear range of the objective function, the method is also applicable. Finally, the applicability is also verified by an example.
Pages: 205-211
Year: 2016 Issue: 2
Journal: Control Theory & Applications
Keyword: sensitivity region; multi-objective robust; robustness index; interval variables; the worst-case;
Abstract: Engineering design problems are usually uncertain because of the random variables. Designers want to design scheme to meet the goal of not only the best performance, but also want to target performance is affected by the uncertainty within the acceptable range. To solve this problem, we propose a multi-objective robust optimization method considering sensitivity region (multi-objective robust optimization based on sensitive region, SR–MORO). SR–MORO can be used to solve optimization design problem involving uncertainty design variables. This method assumes that the uncertainty variables belong to the interval variables, so it does not need to know the probability distribution of random variables. Non-gradient optimization method is used to solve the robust optimization problem, so that the approach is applicable for cases that have discontinuous objective and constraint functions with respect to uncontrollable parameters. When the parameters changed much over the linear range of the objective function, the method is also applicable. Finally, the applicability is also verified by an example.
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