Compromise Based Evolutionary Multiobjective Optimization Algorithm for Multidisciplinary Optimization

International audienceMultidisciplinary Design Optimization deals with engineering problems composed of several sub-problems - called disciplines - that can have antagonist goals and thus require to find compromise solutions. Moreover, the sub-problems are often multiobjective optimization problems. In this case, the compromise solutions between the disciplines are often considered as compromises between all objectives of the problem, which may be not relevant in this context. We propose two alternative definitions of the compromise between disciplines. Their implementations within the well-known NSGA-II algorithm are studied and results are discussed

in multiple objective programming

Exact generation of epsilon-efficient solution

The multicriteria big cube small cube method

Multicriteria optimization, Bicriteria optimization, Approximation algorithms, Facility location problems, Continuous location, Global optimization, Nondifferentiable optimization, Semi-obnoxious location, 90-80, 90B85, 90C29,