Automated discovery of vital knowledge from Pareto-optimal solutions: First results from engineering design

Real world multi-objective optimization problems are often solved with the only intention of selecting a single trade-off solution by taking up a decision-making task. The computational effort and time spent on obtaining the entire Pareto front is thus not justifiable. The Pareto solutions as a whole contain within them a lot more information than that is used. Extracting this knowledge would not only give designers a better understanding of the system, but also bring worth to the resources spent. The obtained knowledge acts as governing principles which can help solve other similar systems easily. We propose a genetic algorithm based unsupervised approach for learning these principles from the Pareto-optimal dataset of the base problem. The methodology is capable of discovering analytical relationships of a certain type between different problem entities.

[1]  Kalyanmoy Deb,et al.  Unveiling innovative design principles by means of multiple conflicting objectives , 2003 .

[2]  Shigeru Obayashi,et al.  Kriging-model-based multi-objective robust optimization and trade-off-rule mining using association rule with aspiration vector , 2009, 2009 IEEE Congress on Evolutionary Computation.

[3]  Amitabha Mukerjee,et al.  THE BIRTH OF SYMBOLS IN DESIGN , 2009 .

[4]  Yacov Y. Haimes,et al.  Multiobjective Decision Making: Theory and Methodology , 1983 .

[5]  Daisuke Sasaki,et al.  Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map , 2003, EMO.

[6]  B. Williams,et al.  ACTIVITY ANALYSIS: SIMPLIFYING OPTIMAL DESIGN PROBLEMS THROUGH QUALITATIVE PARTITIONING† , 1996 .

[7]  Kalyanmoy Deb,et al.  Scope of stationary multi-objective evolutionary optimization: a case study on a hydro-thermal power dispatch problem , 2008, J. Glob. Optim..

[8]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[9]  Shigeru Obayashi,et al.  Multi-Objective Design Exploration of a Centrifugal Impeller Accompanied With a Vaned Diffuser , 2007 .

[10]  A. Messac,et al.  Normal Constraint Method with Guarantee of Even Representation of Complete Pareto Frontier , 2004 .

[11]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[12]  Andrzej Osyczka,et al.  Evolutionary Algorithms for Single and Multicriteria Design Optimization , 2001 .

[13]  Kalyanmoy Deb,et al.  Deciphering innovative principles for optimal electric brushless D.C. permanent magnet motor design , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[14]  Aravind Srinivasan,et al.  Innovization: innovating design principles through optimization , 2006, GECCO.

[15]  Haym Hirsh,et al.  Learning to set up numerical optimizations of engineering designs , 1998 .

[16]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[17]  Jonathan Cagan,et al.  A conceptual framework for combining artificial intelligence and optimization in engineering design , 1997 .

[18]  Marc Teboulle,et al.  Grouping Multidimensional Data - Recent Advances in Clustering , 2006 .

[19]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[20]  Alex H. B. Duffy,et al.  The "What" and "How" of Learning in Design , 1997, IEEE Expert.

[21]  John S. Gero,et al.  A LEARNING AND INFERENCE MECHANISM FOR DESIGN OPTIMIZATION PROBLEM (RE)- FORMULATION USING SINGULAR VALUE DECOMPOSITION , 2008 .

[22]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[23]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[24]  Rashmi.  Data Mining: A Knowledge Discovery Approach , 2012 .