Multi-scenario, multi-objective optimization using evolutionary algorithms: Initial results
Abstract
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.
Authors: Kalyanmoy Deb, Ling Zhu, Sandeep S. Kulkarni
Published in: IEEE Congress on Evolutionary Computation (CEC) (2015)