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Cited by 8,798

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Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach

Zitzler, E., Thiele, L.·1999·IEEE Transactions on Evolutionary Computation
DOI 10.1109/4235.797969
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Abstract

Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.

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Cites

Research works printed but not linked: 47

  1. 10.1162/evco.1993.1.2.127 · DOI
  2. smith, 1992, population diversity in an immune system model: implications for genetic search, Foundations of Genetic Algorithms 2 (FOGA-92)
  3. oei, 1991, Tournament selection niching and the preservation of diversity
  4. 10.1162/evco.1996.4.4.361 · DOI · Paper page
  5. martello, 1990, Knapsack Problems Algorithms and Computer Implementations
  6. 10.1109/ICEC.1996.542368 · DOI
  7. 10.1007/BFb0029771 · DOI
  8. holland, 1975, Adaption in Natural and Artificial Systems
  9. chambers, 1983, Graphical Methods for Data Analysis
  10. deb, 1989, an investigation of niche and species formation in genetic function optimization, Proc 3rd Int Conf Genetic Algorithms, 42
  11. kursawe, 1992, evolution strategies for vector optimization, Proc 10th Int Conf Multiple Criteria Decision Making, 187
  12. 10.1162/evco.1993.1.3.191 · DOI
  13. 10.1007/BF01759923 · DOI
  14. 10.1109/ICEC.1996.542345 · DOI
  15. goldberg, 1989, Genetic Algorithms in Search Optimization and Machine Learning
  16. fonseca, 1993, genetic algorithms for multiobjective optimization: formulation, discussion and generalization, Proc 5th Int Conf Genetic Algorithms, 416
  17. greenwood, 1996, fitness functions for multiple objective optimization problems: combining preferences with pareto rankings, Foundations of Genetic Algorithms 4 (FOGA-96), 437
  18. mahfoud, 1995, Niching methods for genetic algorithms
  19. goldberg, 1987, genetic algorithms with sharing for multimodal function optimization, Genetic Algorithms and Their Applications Proceedings of the Second International Conference on Genetic Algorithms, 41
  20. horn, 1993, Multiobjective optimization using the niched Pareto genetic algorithm
  21. todd, 1997, a multiple criteria genetic algorithm for containership loading, Proc 7th Int Conf Genetic Algorithms, 674
  22. michalewicz, 1994, genetic algorithms for the 0/1 knapsack problem, Methodologies for Intelligent Systems (ISMIS 94), 134, 10.1007/3-540-58495-1_14 · DOI
  23. 10.1887/0750308958/b386c85 · DOI
  24. 10.1145/326619.326694 · DOI · Paper page
  25. 10.1162/evco.1994.2.3.221 · DOI · Paper page
  26. 10.1007/BFb0056872 · DOI · Paper page
  27. 10.1109/ICSMC.1995.537834 · DOI
  28. 10.1007/978-1-4615-3612-3 · DOI
  29. steuer, 1986, Multiple Criteria Optimization Theory Computation Application
  30. valenzuela-rendo´n, 1997, a nongenerational genetic algorithm for multiobjective optimization, Proc 7th Int Conf Genetic Algorithms, 658
  31. 10.1162/evco.1995.3.1.1 · DOI · Paper page
  32. fourman, 1985, compaction of symbolic layout using genetic algorithms, Proc Int Conf Genetic Algorithms and Their Applications, 141
  33. 10.1109/HSC.1997.584597 · DOI
  34. cunha, 1997, use of genetic algorithms in multicriteria optimization to solve industrial problems, Proc 7th Int Conf Genetic Algorithms, 682
  35. zitzler, 1999, Comparison of multiobjective evolutionary algorithms Empirical results
  36. harik, 1995, finding multimodal solutions using restricted tournament selection, Proc 6h Int Conf Genetic Algorithms, 24
  37. de jong, 1975, An analysis of the bevavior of a class of genetic adaptive systems
  38. 10.1023/A:1008899229802 · DOI
  39. loughlin, 1997, the neighborhood constraint-method: a genetic algorithm-based multiobjective optimization technique, Proc 7th Int Conf Genetic Algorithms, 666
  40. 10.1016/0305-0548(80)90014-3 · DOI
  41. blickle, 1996, Theory of evolutionary algorithms and application to system-synthesis
  42. paredis, 1995, the symbiotic evolution of solutions and their representations, Proc 7th Int Conf Genetic Algorithms, 359
  43. ryan, 1995, niche and species formation in genetic algorithms, Practical Handbook of Genetic Algorithms, 1, 57
  44. 10.1109/ICEC.1994.350037 · DOI
  45. schaffer, 1984, Multiple objective optimization with vector evaluated genetic algorithms
  46. 10.1080/03052158508902489 · DOI
  47. 10.1109/ICEC.1996.542653 · DOI

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Metadata

Authors
Zitzler, E., & Thiele, L.
Venue
IEEE Transactions on Evolutionary Computation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1999
Volume
3
Issue
4
Pages
257-271
ISSN
1089-778X (ISSN-L)
DOI
10.1109/4235.797969
OpenAlex
W2106334424
Cited by
8,798
References
1
FWCI
75.73
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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