Interactive Multiobjective Optimization Procedure

This research focuses on multiobjective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multiobjective optimization algorithm that takes into account the designer's preferences during the design process. In this research, an interactive multiobjective optimization procedure (IMOOP) that uses an aspiration-level approach to generate Pareto points is developed. This method provides the designer or the decision maker (DM) with a formal means for efficient design exploration around a given Pareto point. More specifically, the procedure provides the DM with the Pareto sensitivity information and the Pareto surface approximation at a given Pareto design for decision making and tradeoff analysis. The IMOOP has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for its objective and constraints. The second problem is the design and sizing of a high-performance and low-cost 10-bar structure that has multiple objectives. The results indicate that the Pareto designs predicted by the Pareto surface approximation are reasonable and the performance of the second-order approximation is superior compared to that of the first-order approximation. Using this procedure a set of new aspirations that reflect the DM's preferences are easily and efficiently generated, and the new Pareto design corresponding to these aspirations is close to the aspirations themselves. This is important in that it builds the confidence of the DM in this interactive procedure for obtaining a satisfactory final Pareto design in a minimal number of iterations.

[1]  Masataka Yoshimura,et al.  A Multiobjective Optimization Strategy with Priority Ranking of the Design Objectives , 1993 .

[2]  William A. Crossley,et al.  Using the Two-Branch Tournament Genetic Algorithm for Multiobjective Design , 1999 .

[3]  Singiresu S. Rao,et al.  Game theory approach for the integrated design of structures and controls , 1988 .

[4]  Farrokh Mistree,et al.  THE COMPROMISE DECISION SUPPORT PROBLEM AND THE ADAPTIVE LINEAR PROGRAMMING ALGORITHM , 1998 .

[5]  Alejandro R. Diaz.  Interactive solution to multiobjective optimization problems , 1987 .

[6]  Augustine R. Dovi,et al.  Aircraft design for mission performance using nonlinear multiobjective optimization methods , 1989 .

[7]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[8]  C. Chen,et al.  The interactive step trade-off method (ISTM) for multiobjective optimization , 1990, IEEE Trans. Syst. Man Cybern..

[9]  Prabhat Hajela,et al.  Multiobjective optimum design in mixed integer and discrete design variable problems , 1990 .

[10]  P. Hajela,et al.  IMMUNE NETWORK MODELING IN MULTICRITERION DESIGN OF STRUCTURAL SYSTEMS , 1998 .

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

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

[13]  Hans A. Eschenauer,et al.  SAPOP: an optimization procedure for multicriteria structural design , 1993 .

[14]  W. Stadler.  Caveats and Boons of Multicriteria Optimization , 1995 .

[15]  K. Furukawa,et al.  Satisficing trade-off method with an application to multiobjective structural design , 1985 .

[16]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[17]  S Hernandez.  A general sensitivity analysis for unconstrained and constrained Pareto optima in multiobjective optimization , 1995 .

[18]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[19]  Hirotaka Nakayama,et al.  Theory of Multiobjective Optimization , 1985 .

[20]  Achille Messac,et al.  Physical programming - Effective optimization for computational design , 1996 .

[21]  Anoop Dhingra,et al.  Multiple Objective Design Optimization , 1996 .