Unveiling innovative design principles by means of multiple conflicting objectives

Optimization principles are often used in engineering design activities for finding solutions which cannot be bettered. The use of a single objective for design usually results in only one optimum solution, whereas the consideration of multiple conflicting objectives results in a number of trade-off Pareto-optimal solutions. Investigating the Pareto-optimal solutions for any similarity or relationship among their design variables may provide vital design principles, which may not be possible to obtain by any other means. This paper illustrates the concept of optimization in the presence of multiple conflicting objectives and then presents one multi-objective optimization algorithm based on evolutionary algorithms. Thereafter, a number of engineering design optimization case studies are presented to first find a set of Pareto-optimal solutions and then analyze them to unveil important design principles which would be of great importance to a designer. The breadth of case studies considered in this paper and the demonstrated discovery of useful design principles should encourage the study of multi-objective evolutionary optimization and motivate researchers and practitioners to perform similar studies involving other engineering design problems.

[1]  E. Polak,et al.  On Multicriteria Optimization , 1976 .

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[4]  Alice M. Agogino,et al.  Theory of design: An optimization perspective , 1990 .

[5]  E Sandgren,et al.  TOPOLOGICAL DESIGN OF STRUCTURAL COMPONENTS USING GENETIC OPTIMIZATION METHOD , 1990 .

[6]  Michael de la Maza,et al.  Book review: Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz (Springer-Verlag, 1992) , 1993 .

[7]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

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

[9]  Marc Schoenauer,et al.  Adaptive Techniques for Evolutionary Topological Optimum Design , 2000 .

[10]  Nestor V. Queipo,et al.  Multiobjective Optimal Placement of Convectively and Conductively Cooled Electronic Components on Printed Wiring Boards , 2000 .

[11]  M. Jakiela,et al.  Continuum structural topology design with genetic algorithms , 2000 .

[12]  Kalyanmoy Deb,et al.  A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design , 2001, EMO.

[13]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

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

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

[16]  Kalyanmoy Deb,et al.  Multi-Speed Gearbox Design Using Multi-Objective Evolutionary Algorithms , 2003 .

[17]  Kalyanmoy Deb,et al.  Optimization for Engineering Design: Algorithms and Examples , 2004 .