| Paper title: |
Strength Pareto Evolutionary Algorithm using Self-Organizing Data Analysis Techniques |
| Published in: | Issue 1, (Vol. 9) / 2015 |
| Publishing date: | 2015-03-31 |
| Pages: | 16-22 |
| Author(s): | BALAN Ionut |
| Abstract. | Abstract–Multiobjective optimization is widely used in problems solving from a variety of areas. To solve such problems there was developed a set of algorithms, most of them based on evolutionary techniques. One of the algorithms from this class, which gives quite good results is SPEA2, method which is the basis of the proposed algorithm in this paper. Results from this paper are obtained by running these two algorithms on a flow-shop problem. |
| Keywords: | Multiobjective Optimization, Dominance, Pareto, Evolutionary, Classification, SPEA2 |
| References: | 1. W. Nunkaew, B. Phruksaphanrat, „A Multiobjective Programming for Tranportation Problem with the Consideration of both Depot to Customer and Customer to Customer Relationships”, Proceedings of the International MultiConference Of Engineers and Computer Scientists, Hong Kong, China, 2009 2. M. Emmerich, A. Deutz, „Multicriteria Optimization and Decision Making. Principles, Algorithms and Case Studies”, LIACS Master Course, 2006 3.A.M. Brintrup, H. Takagi, A. Tiwari, J.J. Ramsden, „Evaluation of sequencial, multi-objective, and parallel interactive genetic algorithms for multi-objectives optimization problems”, Journal of Biological Physics and Chemistry , Nr. 6, Collegium Basilea &AMSI, p.137-146, 2006 4.J. Schaffer, „Multiple objective optimization with vector evaluated genetic algorithm”, Thesis, Vanderbilt University, 1984 5.C.M. Fonseca, P.J. Fleming, „Genetic Algorithms for multiobjective optimization: Formulation, discussion and generalization”, Proceedings of the Fifth International Conference on Genetic Algorithms, p.416-423, 1993 6.K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, „A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”, PPSN VI: Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, Nr. 1917 in LNCS, Springer-Verlag, p.849-858, 2000 7.E. Zitzler, M. Laumanns, L. Thiele, „SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization”, Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), K. Gianakoglou et al., Eds. International Center for Numerical Methods in Engineering (CIMNE), p.95- 100, 2002 8.F. De Toro, J. Ortega, J. Fernandez, A. Diaz, „PSFGA- A Parallel Genetic Algorithm for Multiobjective Optimization”, Proceedings of the 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing (EUROMICROPDP’02), 2002 9.A. Konak, D.W. Coit, A.E. Smith, „Multi-objective optimization using genetic algorithms: A tutorial”, Reliability Engineering and System Safety, Nr.91, p.992-1007, 2006 10. M.M. Raghuwanshi, O.G. Kakde, „Survey on multiobjective evolutionary and real coded genetic algorithms”, Complexity International, vol.11, p. 151-163, 2005 11. E. Zitzler, L. Thiele, „Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach”, IEEE Trans Evol Comput 3(4), p.257-271, 1999 12. E. Zitzler, M. Laumanns, L. Thiele, „SPEA2: Improving the Strenght Pareto Evolutionary Algorithm”, Technical Report 103, Gloriastrasse 35, CH-8092, Zurich, Switzerland, 2001 13. F.Y. Tzeng, K.L. Ma, „A Cluster-Space Visual Interface for Arbitrary Dimensional Classification Of Volume Data”, Joint EUROGRAPHICS, IEEE TCVG Symposium on Visualisation, 2004 14. SOA http://soa.iti.es/instances-results-and-other-materialsfor-multi-objective-pfsp-with-sdst -accessed on 09.2011 |
| Back to the journal content | |
