Multi-scenario, multi-objective optimization using evolutionary algorithms: Initial results

Most designs in practice go through a number of different loading or operating conditions. Therefore, a meaningful and resilient design must be such that it performs well under all such scenarios. Despite its practical importance, multi-scenario consideration has not been paid much attention in mult...

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Bibliographic Details
Main Authors: Deb, Kalyanmoy, Ling Zhu, Kulkarni, Sandeep
Format: Conference Proceeding
Language:English
Subjects:
Aggregates
Bridges
Context
Decision making
Linear programming
Loading
Optimization
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Summary:Most designs in practice go through a number of different loading or operating conditions. Therefore, a meaningful and resilient design must be such that it performs well under all such scenarios. Despite its practical importance, multi-scenario consideration has not been paid much attention in multi-objective optimization literature. In this paper, we address this challenging issue by suggesting an aggregate based handling of multiple scenarios and contrasts the proposed approach against a recently suggested approach which involves running multi-objective optimization multiple times and a rigid decision-making method. The proposed method is applied to two numerical test problems and two engineering design problems. This first evolutionary based multi-scenario, multi-objective optimization study should spur further interests among EMO researchers.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2015.7257115