Innovization: innovating design principles through optimization

This paper introduces a new design methodology (we call it "innovization") in the context of finding new and innovative design principles by means of optimization techniques. Although optimization algorithms are routinely used to find an optimal solution corresponding to an optimization problem, the task of innovization stretches the scope beyond an optimization task and attempts to unveil new, innovative, and important design principles relating to decision variables and objectives, so that a deeper understanding of the problem can be obtained. The variety of problems chosen in the paper and the resulting innovations obtained for each problem amply demonstrate the usefulness of the innovization task. The results should encourage a wide spread applicability of the proposed innovization procedure (which is not simply an optimization procedure) to other problem-solving tasks.

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

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

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

[4]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

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

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

[7]  H. P. Benson,et al.  Existence of efficient solutions for vector maximization problems , 1978 .

[8]  Kalyanmoy Deb,et al.  Searching for Robust Pareto-Optimal Solutions in Multi-objective Optimization , 2005, EMO.

[9]  Ivan Zelinka,et al.  Mechanical engineering design optimization by differential evolution , 1999 .

[10]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[11]  Ralph E. Steuer,et al.  A Heuristic for Estimating Nadir Criterion Values in Multiple Objective Linear Programming , 1997, Oper. Res..

[12]  Panos Y. Papalambros,et al.  Principles of Optimal Design: Author Index , 2000 .

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

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

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

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

[17]  A. Ravindran,et al.  Engineering Optimization: Methods and Applications , 2006 .

[18]  Kalyanmoy Deb,et al.  Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems , 1995, Complex Syst..

[19]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .