Cross-searching strategy for multi-objective particle swarm optimization

2007 IEEE Congress on Evolutionary Computation, CEC 2007

Volumn , Issue , 2007, Pages 3135-3141

  Chiu, Shih Yuan ^b   Sun, Tsung Ying ^b   Hsieh, Sheng Ta ^a,b   Lin, Cheng Wei ^b  

^a IEEE

^b NATIONAL DONG HWA UNIVERSITY   (Taiwan)

Author keywords

Cross searching strategy; Local guide; Multi objective particle swarm optimization (MOPSO)

Indexed keywords

CROSS-SEARCHING STRATEGY (CSS); PARETO OPTIMUM;

DISTRIBUTION FUNCTIONS; EVOLUTIONARY ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PARETO PRINCIPLE; PROBLEM SOLVING;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 77951203785     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2007.4424872     Document Type: Conference Paper

References (30)

1. J. D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms, Ph.D. thesis, Vanderbilt University, 1984.
2. J. D. Schaffer, "Multiple objective optimization with vector evaluated genetic algorithms," in Proc. 1st IEEE Int. Conf. Genetic Algorithms, pp. 93-100, 1985.
3. P. Haiela and C.-Y. Lin, "Genetic search strategies in multi-criterion optimal design." Structural and Multidisciplinary Optimization vol. 4, no. 2, pp.99-107. Jun. 1992.
4. C. M. Fonseca and P. J. Fleming, "Genetic algorithms for multiobjective Optimization: Formulation, discussion and generalization," In Stephanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416-423, San Mateo, California, University of Illinois at Urbbna-Champaign, Morgan Kauffman Publishers, 1993.
5. N. Srinivas and K. Deb, "Multiobjective optimization using nondominated sorting in genetic algorithms," Evolutionary Computation, vol. 2, no. 3, pp. 221-248, Fall 1994.
6. J. Horn, N. Nafpliotis and D.E. Goldberg, "A niched pareto genetic algorithm for multiobjective optimization," In Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 1, pp. 82-87, Jun. 1994.
7. E. Zitzler and L. Thiele, "Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach," IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257-271, Nov. 1999.
8. E. Zitzler, M. Laumanns, and L. Thiele, "SPEA2: Improving the strength pareto evolutionary algorithm," In K. Giannakoglou et al., editor, EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95-100, Athens, Greece, 2002.
9. J. D. Knowles and D. W. Corne, "Approximarting the nondominated front using the pareto archived evolution strategy," Evolutionary Computation, vol. 8, no. 2, pp. 149-172, 2000.
10. K. Deb, S. Agrawal, A. Pratab and T. Meyarivan, "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II," In Marc Schoenauer et al., editor, Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 849-858, Paris, France, 2000. Springer, Lecture Notes in Computer Science No. 1917.
11. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, Apr. 2002.
12. C. A. Coello Coello and M. S. Lechuga, "MOPSO: A proposal for multiple objective particle swarm optimization," in Proc. of IEEE World Congress on Computational Intelligence (CEC'02), pp. 1051-1056, May 2002.
13. J. E. Fieldsend and S. Singh, "A multi-objective algorithm based upon particle swarm optimization, and efficient data structure and turbulence," Workshop on Computational Intelligence, pp. 34-44, Sep. 2002.
14. X. Hu and R. Eberhart, "Multiobjective optimization using dynamic neighborhood particle swarm optimization," in Proc. of IEEE World Congress on Computational Intelligence (CEC'02), pp. 1677-1681, May 2002.
15. K. E. Parsopoulos and M. N. Vrahatis, "Particle swarm optimization method in multiobjective," in Proc. of the ACM Symposium on Applied Computing (SAC 2002), pp. 603-607, 2002.
16. C. A. Coello Coello, D. A. Van Veldhuizen, and G. B. Lamont, Evolutionary algorithms for solving multi-objective problems, Kluwer, May 2002.
17. S. Mostaghim and J. Teich, "Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO)," in Proc. of IEEE Swarm Intelligence Symposium, pp. 26-33, Dec. 2003.
18. T. Bartz-Beielstein, P. Limbourg, J. Mehnen, K. Schmitt, K. E. Parsopoulos and M. N. Vrahatis, "Particle swarm optimizers for Pareto optimization with enhanced archiving techniques," in Proc. of IEEE Congress on Evolutionary Computation (CEC' 03), vol. 3, pp. 1780-1787, Dec. 2003.
19. K. E. Parsopoulos, D. K. Tasoulis, and M. N. Vrahatis, "Multiobjective optimization using parallel vector evaluated particle swarm optimization," in Proc. of The IASTED International Conference on Artificial Intelligence and Applications, Feb. 2004.
20. C. A. Coello Coello, G. T. Pulido, and M. S. Lechuga, "Handling Multiple Objectives With Particle Swarm Optimization," IEEE Trans. on Evolutionary computation, vol. 8, pp. 256-279, Jun. 2004.
21. C. A. Coello Coello, "A comprehensive survey of evolutionary-based multiobjective optimization technique," Knowledge and Information Systems, vol. 1, no. 3, pp. 269-308, Aug. 1999.
22. V. G. Gudise and G. K. Venayagamoorthy, "Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks." IEEE Swarm Intelligence Symposium, pp. 110-117, Apr. 2003.
23. R. C. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Proc. 6th Int. Symp. Micro Machine and Human Science, Nagoya, Japan, pp. 39-43, 1995.
24. Y. Shi and R. Eberhart, "A modified particle swarm optimizer," in Proc. of IEEE World Congress on Computational Intelligence, pp. 69-73, May 1998.
25. E. Zitzler, K. Deb, and L. Thiele, "Comparison of multiobjective evolutionary algorithms: Empirical results," Evolutionary Computation, vol. 8, issue 2, pp. 173-195, 2000.
26. J. D. Schaffer, "Multiple objective optimization with vector evaluated genetic algorithms," in Proc. of the First International Conference on Genetic Algorithms, J. J. Grefensttete, Ed. Hillsdale, NJ: Lawrence Erlbaum, pp.93-100, 1987.
27. C. M. Fonseca and P. J. Fleming, "Multiobjective genetic algorithms made easy: selection sharing and mating restriction." in proc. of the First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK, 1995
28. D. A. Van Veldhuizen and G. B. Lamont, Multiobjective evolutionary algorithm research: A history and analysis, Dept. Elec. Comput. Eng., Graduate School of Eng., Air Force Inst. Technol., Wright-Patterson AFB, OH, Tech. Rep. TR-98-03, 1998.
29. K. C. Tan, T. H. Lee, and E. F. Khor, Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization, vol. 5, pp. 565-599, 2001.
30. D. A. Van Veldhuizen, Multiobjective evolutionary algorithms: Classifications, analyzes, and new innovations, Ph.D. dissertation, Dept. Elec. Comput. Eng., Graduate School of Eng., Air Force Inst. Technol., Wright-Patterson AFB, OH, 1999.