Two new GA-based methods for multiobjective optimization

Abstract In this paper, we introduce two new multiobjective optimization techniques based on the genetic algorithm (GA), and implemented as part of a multiobjective optimization tool called MOSES (Multiobjective Optimization of Systems in the Engineering Sciences). These methods are based in the concept of min-max optimum, and can produce the Pareto set and the best trade-off among the objectives. The results produced by these approaches are compared to those produced with other mathematical programming techniques and GA-based approaches using two engineering design problems, showing the new techniques’ capability to generate better trade-offs than the approaches previously reported in the literature.

[1]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[2]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  A. Osyczka.  An approach to multicriterion optimization problems for engineering design , 1978 .

[4]  Singiresu S. Rao.  Game theory approach for multiobjective structural optimization , 1987 .

[5]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[6]  R. DeVor,et al.  An application of multiple criteria decision making principles for planning machining operations , 1984 .

[7]  H. Adeli,et al.  Augmented Lagrangian genetic algorithm for structural optimization , 1994 .

[8]  Alan D. Christiansen,et al.  An empirical study of evolutionary techniques for multiobjective optimization in engineering design , 1996 .

[9]  S. Ranjithan,et al.  Using genetic algorithms to solve a multiple objective groundwater pollution containment problem , 1994 .

[10]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[11]  Andrzej Osyczka,et al.  7 – Multicriteria optimization for engineering design , 1985 .

[12]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

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

[14]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[15]  Michael P. Fourman,et al.  Compaction of Symbolic Layout Using Genetic Algorithms , 1985, ICGA.

[16]  Michael M. Skolnick,et al.  Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints , 1993, ICGA.

[17]  P. Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

[18]  Andrzej Osyczka,et al.  Multicriterion optimization in engineering with FORTRAN programs , 1984 .

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

[20]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[21]  C. Tseng,et al.  MINIMAX MULTIOBJECTIVE OPTIMIZATION IN STRUCTURAL DESIGN , 1990 .

[22]  Singiresu S Rao,et al.  Multiobjective optimization in structural design with uncertain parameters and stochastic processes , 1984 .

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

[24]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[25]  Alan D. Christiansen,et al.  Using genetic algorithms for optimal design of trusses , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.