Active Robust Optimization: Enhancing Robustness to Uncertain Environments

Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of...

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Published in:IEEE transactions on cybernetics Vol. 44; no. 11; pp. 2221 - 2231
Main Authors: Salomon, Shaul, Avigad, Gideon, Fleming, Peter J., Purshouse, Robin C.
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation
Adaptive design
Algorithms
Artificial Intelligence
Brightness
Computer Simulation
Cybernetics
Decision Support Techniques
dynamic optimization
Dynamic tests
Ecosystem
Equivalence
evolutionary algorithms
Expenses
Feedback
Linear programming
Models, Statistical
multiobjective optimization
Nonlinear programming
Optimization
Pareto optimization
Pattern Recognition, Automated - methods
robust optimization
Robustness
Tables (data)
Uncertainty
Vectors
multiobjective optimization
Adaptive design
dynamic optimization
evolutionary algorithms
robust optimization
ISSN:2168-2267, 2168-2275, 2168-2275
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Abstract Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the solution's parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes at the expense of peak performances. In this paper, active robust optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named active robust optimization problem is formulated. The problem amalgamates robust optimization with dynamic optimization to evaluate the performance of a candidate solution, while considering possible environmental conditions. The adaptation's influence on the solution's performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict, the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.
AbstractList Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the solution's parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes at the expense of peak performances. In this paper, active robust optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named active robust optimization problem is formulated. The problem amalgamates robust optimization with dynamic optimization to evaluate the performance of a candidate solution, while considering possible environmental conditions. The adaptation's influence on the solution's performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict, the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.
Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the solution's parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes at the expense of peak performances. In this paper, active robust optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named active robust optimization problem is formulated. The problem amalgamates robust optimization with dynamic optimization to evaluate the performance of a candidate solution, while considering possible environmental conditions. The adaptation's influence on the solution's performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict, the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the solution's parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes at the expense of peak performances. In this paper, active robust optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named active robust optimization problem is formulated. The problem amalgamates robust optimization with dynamic optimization to evaluate the performance of a candidate solution, while considering possible environmental conditions. The adaptation's influence on the solution's performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict, the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.
Author Fleming, Peter J.
Avigad, Gideon
Purshouse, Robin C.
Salomon, Shaul
Author_xml – sequence: 1
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  surname: Salomon
  fullname: Salomon, Shaul
  email: ssalomon1@sheffield.ac.uk
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  surname: Purshouse
  fullname: Purshouse, Robin C.
  organization: Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
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SubjectTerms Adaptation
Adaptive design
Algorithms
Artificial Intelligence
Brightness
Computer Simulation
Cybernetics
Decision Support Techniques
dynamic optimization
Dynamic tests
Ecosystem
Equivalence
evolutionary algorithms
Expenses
Feedback
Linear programming
Models, Statistical
multiobjective optimization
Nonlinear programming
Optimization
Pareto optimization
Pattern Recognition, Automated - methods
robust optimization
Robustness
Tables (data)
Uncertainty
Vectors
Title Active Robust Optimization: Enhancing Robustness to Uncertain Environments
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