Uncertainty quantifications of Pareto optima in multiobjective problems

Design is a multi-objective decision-making process considering manufacturing, cost, aesthetics, usability among many other product attributes. The set of optimal solutions, the Pareto set, indicates the trade-offs between objectives. Decision-makers generally select their own optima from the Pareto set based on personal preferences or other judgements. However, uncertainties from manufacturing processes and from operating conditions will change the performances of the Pareto optima. Evaluating the impacts of uncertainties on Pareto optima requires a large amount of data and resources. Comparing multiple Pareto solutions under uncertainty are also very costly. In this work, local Pareto set approximation is integrated with uncertainty propagation technique to quantify design variations in the objective space. An optimality influence range is proposed using linear combinations of objective functions that creates a more accurate polygon objective variation subspace. A set of ‘virtual samples’ is then generated to form two quantifications of the objective variation subspace, namely an influence noise to indicate how a design remains optimal, and an influence range that quantifies the overall variations of a design. In most engineering practices, a Pareto optimum with a smaller influence noise and a smaller influence range is preferred. We also extend the influence noise/range concept to nonlinear Pareto set with the second-order approximation. The quadratic local Pareto approximation method in the literature is also extended in this work to solve multi-objective engineering problems with black-box functions. The usefulness of the proposed quantification method is demonstrated using a numerical example as well as using an engineering problem in structural design.

[1]  Tzu-Chieh Hung,et al.  Multi-objective design and tolerance allocation for single- and multi-level systems , 2013, J. Intell. Manuf..

[2]  Christopher A. Mattson,et al.  Pareto Frontier Based Concept Selection Under Uncertainty, with Visualization , 2005 .

[3]  S. Rachev,et al.  Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures , 2008 .

[4]  A. Giassi,et al.  Multidisciplinary design optimisation and robust design approaches applied to concurrent design , 2004 .

[5]  Byung Chai Lee,et al.  Development of a simple and efficient method for robust optimization , 2002 .

[6]  M. Guenov,et al.  Local Pareto approximation for multi-objective optimization , 2008 .

[7]  Shapour Azarm,et al.  Multiobjective Collaborative Robust Optimization With Interval Uncertainty and Interdisciplinary Uncertainty Propagation , 2008 .

[8]  John E. Renaud,et al.  Multiobjective Collaborative Optimization , 1997 .

[9]  Kwon-Hee Lee,et al.  Robust optimization considering tolerances of design variables , 2001 .

[10]  Tzu-Chieh Hung,et al.  Multi-Objective Design and Tolerance Allocation for Single- and Multi-Level Systems , 2010, DAC 2010.

[11]  I. Doltsinis,et al.  ROBUST DESIGN OF STRUCTURES USING OPTIMIZATION METHODS , 2004 .

[12]  Xiaoping Du,et al.  Efficient Uncertainty Analysis Methods for Multidisciplinary Robust Design , 2002 .

[13]  John E. Renaud,et al.  A Comparison of Equality Constraint Formulations for Concurrent Design Optimization , 1997 .

[14]  John Dalsgaard Sørensen,et al.  Reliability-Based Optimization in Structural Engineering , 1994 .

[15]  Panos Y. Papalambros,et al.  On Selecting Single-Level Formulations for Complex System Design Optimization , 2007 .