论文信息 - Two New Approaches to Multiobjective Optimisation Using Genetic Algorithms

Two New Approaches to Multiobjective Optimisation Using Genetic Algorithms

In this paper, two new multiobjective optimization techniques based on the genetic algorithm (GA) are introduced. 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 mathematical programming techniques and other GA-based approaches using a multiobjective optimization tool called MOSES. This tool, developed by the author, is a convenient testbed for analyzing the performance of new and existing multicriteria optimization techniques, and it is an effective engineering design tool.

Carlos A. Coello Coello | C. Coello

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

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

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

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

[5] J. David Schaffer,et al. Proceedings of the third international conference on Genetic algorithms , 1989 .

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

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

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

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

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