Multi-objective optimization problems with arena principle and NSGA-II

Information Technology Journal

Volumn 9, Issue 2, 2010, Pages 381-385

  Dong Feng, Wang ^b   Feng, Xu ^a  

^a ANHUI UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b NONE

Author keywords

Arena principle; Non dominated set; Pareto optimal fronts; Running efficiency

Indexed keywords

EVOLUTIONARY PROCESS; FAST CONVERGENCE; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; NONDOMINATED SOLUTIONS; NSGA-II; PARETO OPTIMAL SOLUTIONS; PARETO-OPTIMAL FRONT; RUNNING EFFICIENCY; SIMULATION RESULT; SORTING ALGORITHM; STANDARD TEST FUNCTIONS; TEST PROBLEM;

COMPUTATIONAL COMPLEXITY; COMPUTATIONAL EFFICIENCY; EVOLUTIONARY ALGORITHMS; GLOBAL OPTIMIZATION; MULTIOBJECTIVE OPTIMIZATION;

PARETO PRINCIPLE;

EID: 77649297851     PISSN: 18125638     EISSN: 18125646     Source Type: Journal    
DOI: 10.3923/itj.2010.381.385     Document Type: Article

References (11)

1. Corne, D.W., J.D. Knowles and M.J. Oates, 2000. The pareto envelope-based selection algorithm for multiobjective optimization. Lecture Notes in Comput. Sci., 1917/2000: 839-848.
2. Deb, K., A. Pratap, S. Agarwal and T. Meyarivan, 2002. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., 6: 182-197.
3. Deb, K., A. Sinha and S. Kukkonen, 2006. Multi-objective test problems, linkages and evolutionary methodologies. Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation Conference, July 8-12, ACM, New York, USA., pp: 1141-1148.
4. Fonseca, C.M. and P.J. Fleming, 1993. Genetic algorithms for multiobjective Optimization: Formulation, discussion and generalization Proceedings of the 5th International Conference on Genetic Algorithms, (ICGA'93), Morgan Kaufmann Inc., SanMateo, California, pp: 416-423.
5. Horn, J., N. Nafpliotis and D.E. Goldberg, 1994. A niched pareto genetic algorithm for multiobjective optimization. Proc. 1st IEEE Conf. Evolut. Comput. IEEE World Cong. Comput, 1: 82-87.
6. Knowles, J.D. and D.W. Corne, 1999. The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimization. Proc. Cong. Evolut. Comput., 1: 98-105.
7. Srinivas, N. and K. Deb, 1995. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolut. Comput., 2: 221-248.
8. Zheng, J.H., H. Jiang, D. Kuang and Z.Z. Shi, 2007. An approach of constructing multi-objective pareto optimal solutions using arena's principle. Ruan Jian Xue Bao J. Software, 18: 1287-1297.
9. Zitzler, E. and L. Thiele, 1998. An evolutionary algorithm for multiobjective optimization: The strength pareto approach. Technical Report 43. Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich,Gloriastrasse 35, CH-8092 Zurich, Switzerland. http://www.tik.ee.ethz.ch/sop/publicationListFiles/zt1998a.pdf
10. Zitzler, E. and L. Thiele, 1999. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput., 3: 257-271.
11. Zitzler, E., K. Deb and L.Thiele, 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. J. Evol. Comput., 8: 173-195.