BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Essabri Abdelbasset, Gzara Mariem, Loukil Taicir (Université de Sfax, Tunisia)
Tytuł
A Study of Distributed Evolutionary Algorithms for Multi-objective Optimisation
Źródło
Multiple Criteria Decision Making / University of Economics in Katowice, 2009, vol. 4, s. 89-105, rys., tab., bibliogr. 16 poz.
Słowa kluczowe
Optymalizacja wielokryterialna, Algorytmy genetyczne, Systemy rozproszone
Multiple criteria optimization, Genetic algorithms, Systems diffuse
Uwagi
summ., Korespondencja z redakcją: numeracja wpisana za zgodą redakcji (wynika z ciągłości wydawniczej serii MCDM) - brak numeracji na stronie tytułowej
Abstrakt
Most popular Evolutionary Algorithms for single multi-objective optimisation are motivated by the reduction of the computation time and the resolution larger problems. A promising alternative is to create new distributed schemes that improve the behaviour of the search process of such algorithms. In the multi-objective optimisation problems, more exploration of the search space is required to obtain the whole or the best approximation of the Pareto front. Almost all proposed Parallel Multi-Objective Evolutionary Algorithms (PMOEAs) are based on the specialisation concept which means dividing the objective and/or the search space then assigning each part to a processor. One processor called the organiser or the coordinator is usually charged to direct the whole algorithm. In this paper, we present a new parallel scheme of multi-objective evolutionary algorithms which is based on a clustering technique. This new parallel algorithm is implemented and compared to three PMOEAs which are cone-separation [1], Divided Range Multi-Objective Genetic Algorithm (DRMOGA) [8] and a Parallel Strength Pareto Evolutionary Algorithm (PSPEA) based on the island model without migration. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. Branke J., Schmeck H., Deb K., Reddy M.: Parallelizing Multi-Objective Evolutionary Algorithms: Cone Separation. Congress on Evolutionary Computation. IEEE, Piscataway 2004, pp. 1952-1957.
  2. Cantú-Paz E.: A Survey of Parallel Genetic Algorithms. Technical Report. Illlinois Genetic Algorithms Laboratory, 1997.
  3. Coello C.A., van Veldhuizen D.A., Lamont G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. 2nd Edition, Springer Science + Business Media, New York 2007.
  4. Deb K., Pratap A., Agarwal S., Meyarivan T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. "IEEE Transactions on Evolutionary Computation" 2002, Vol. 6, pp. 182-196.
  5. de Toro F., Ortega J., Fernandez J., Diaz A.: PSFGA: A Parallel Genetic Algorithm for Multiobjective Optimization. Proceedings of the 10th Euromicro Workshop on Parallel, Distributed and Network-Based Processing, IEEE, Gran- -Canaria 2002, pp. 384-391
  6. Erickson M., Mayer A., Horn J.: The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems. In: First International Conference on Evolutionary Multi-Criterion Optimization. E. Zitzler, K. Deb, L. Thiele, C.A. Coello Coello, D. Corne (eds). Lecture Notes in Computer Science, No. 1993, Springer-Verlag, Berlin-Heidelberg 2001, pp. 681-695.
  7. Essabri A., Gzara M., Loukil T.: Parallel Multi-Objective Evolutionary Algorithm with Multi-Front Equitable Distribution. The Fifth International Conference on Grid and Cooperative Computing (GCC 2006), Hunan 2006, pp. 241- 244.
  8. Hiroyasu T., Kaneko M., Miki M.: A Parallel Genetic Algorithm with Distributed Environment Scheme. Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, No. 2, 2000, pp. 619-625.
  9. Hiroyasu T., Miki M., Watanabe S.: Distributed Genetic Algorithms with a New Sharing Approach in Multiobjective Optimization Problems. Congress on Evolutionary Computation, Washington, D.C. July 1999, pp. 69-76.
  10. Horn J., Nafpliotis N., Goldberg D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. Procedings 1st IEEE Conference Evolutionary Computation. IEEE World Congress Computational Computation, 1, Orlando, Florida 1994, pp. 82-87.
  11. Kamiura J., Hiroyasu T., Miki M.: MOGADES: Multi-Objective Genetic Algorithm with Distributed Environment Scheme. Computational Intelligence and Applications (Proceedings of the 2nd International Workshop on Intelligent Systems Design and Applications: ISDA-02 ), Atlanta 2002, pp. 143-148.
  12. Van Veldhuizen D.A., Lamont G.B.: Multiobjective Evolutionary Algorithm Test Suites. In: Proceedings of the 1999 ACM Symposium on Applied Computing. J. Carroll et al. (eds). ACM, San Antonio, Texas 1999, pp. 351-357.
  13. Xu K., Louis S.J., Mancini R.C.: A Scalable Parallel Genetic Algorithm for X-ray Spectroscopic Analysis. Genetic and Evolutionary Computation Conference (GECCO-2005)-Proceedings-ToC, Washington D.C. 2005, pp. 811-816.
  14. Zitzler E., Thiele L.: Multi-Objective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. "IEEE Transactions on Evolutionary Computation" 1999, 3, pp. 257-271.
  15. Zitzler E., Laumanns M., Thiele L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multi-Objective Optimization. "Evolutionary Methods for Design, Optimisation, and Control" 2002, pp. 19-26.
  16. Srinivas N., Deb K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. "Evolutionary Computation" 1995, 2, pp. 221-248.
Cytowane przez
Pokaż
ISSN
2084-1531
Język
eng
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu