Intelligent Particle Swarm Optimization in Multi-objective Problems

  • Shinn-Jang Ho
  • Wen-Yuan Ku
  • Jun-Wun Jou
  • Ming-Hao Hung
  • Shinn-Ying Ho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

In this paper, we proposes a novel intelligent multi-objective particle swarm optimization (IMOPSO) to solve multi-objective optimization problems. High performance of IMOPSO mainly arises from two parts: one is using generalized Pareto-based scale-independent fitness function (GPSISF) can efficiently given all candidate solutions a score, and then decided candidate solutions level. The other one is replacing the conventional particle move process of PSO with an intelligent move mechanism (IMM) based on orthogonal experimental design to enhance the search ability. IMM can evenly sample and analyze from the best experience of an individual particle and group particles by using a systematic reasoning method, and then efficiently generate a good candidate solution for the next move of the particle. Some benchmark functions are used to evaluate the performance of IMOPSO, and compared with some existing multi-objective evolution algorithms. According to experimental results and analysis, they show that IMOPSO performs well.

Keywords

Particle Swarm Optimization Pareto Front Partial Vector Orthogonal Experimental Design True Pareto Front 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Deb, K.: Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. MIT Evolutionary Computation 7(3), 205–230 (Fall 1999)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Srinivas, N., Deb, K.: Multiobjective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)CrossRefGoogle Scholar
  3. 3.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithms: NSGA-II. IEEE Trans. Evol. Comput., 6(2), 182–197 (2002)CrossRefGoogle Scholar
  4. 4.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. MIT Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  5. 5.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and strengthen Pareto approach. IEEE Trans. on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar
  6. 6.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich (May 2001)Google Scholar
  7. 7.
    Ho, S.-Y., Shu, L.-S., Chen, J.-H.: Intelligent Evolutionary Algorithms for Large Parameter Optimization Problems. IEEE Trans. Evolutionary Computation 8(6), 522–541 (2004)CrossRefGoogle Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proc. IEEE Conf. Neural Networks IV 4, 1942–1948 (1995)Google Scholar
  9. 9.
    Coello Coello, C.A., Lechuga, M.S.: MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. In: Proc. Congress on Evolutionary Computation(CEC), pp. 1051–1056 (2002)Google Scholar
  10. 10.
    Coello Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Trans. Evolutionary Computation 8(3), 256–279 (2004)CrossRefGoogle Scholar
  11. 11.
    Hu, X., Eberhart, R.C.: Multiobjective Optimization Using Dynamic Neighborhood Particle Swarm Optimization. In: Proc. Congress on Evolutionary Computation (CEC), May 2002, pp. 1677–1681 (2002)Google Scholar
  12. 12.
    Hu, X., Eberhart, R.C., Shi, Y.: Particle Swarm with Extended Memory for Multiobjective Optimization. In: Proc. of IEEE International Conference on Swarm Intelligence Symposium (SIS), April 2003, pp. 193–197 (2003)Google Scholar
  13. 13.
    Wu, Q.: On the optimality of orthogonal experimental design. Acta Math. Appl. Sinica 1(4), 283–299 (1978)MathSciNetGoogle Scholar
  14. 14.
    Dey, A.: Orthogonal Fractional Factorial Designs. Wiley, New York (1985)MATHGoogle Scholar
  15. 15.
    Hedayat, A.S., Sloane, N.J.A., Stufken, J.: Orthogonal Arrays: Theory and Applications. Springer, Heidelberg (1999)CrossRefMATHGoogle Scholar
  16. 16.
    Bagchi, T.-P.: Taguchi Methods Explained: Practical Steps to Robust Design. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  17. 17.
    Tsai, J.T., Liu, T.K., Chou, J.H.: Hybrid aguchi-genetic algorithm for bloal numerical optimization. IEEE Trans. Evolutionary Computation 8(4), 365–377 (2004)CrossRefGoogle Scholar
  18. 18.
    Tanaka, H.: A comparative study of GA and orthogonal experimental design. In: Proc. IEEE Int. Conf. Evolutionary Computation, pp. 143–146 (1997)Google Scholar
  19. 19.
    Zhang, Q., Leung, Y.–W.: An orthogonal genetic algorithm for multimedia multicast routing. IEEE Trans. Evolutionary Computation 3(1), 53–62 (1999)CrossRefGoogle Scholar
  20. 20.
    Leung, Y.-W., Wang, Y.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans. Evolutionary Computation 5(1), 41–53 (2001)CrossRefGoogle Scholar
  21. 21.
    Ho, S.-Y., Chen, H.-M., Chen, S.-J.: Design of accurate classifiers with a compact fuzzyrule base using an evolutionary scatter partition of feature space. IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 1031–1044 (2004)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Huang, H.-L., Ho, S.-Y.: Mesh optimization for surface approximation using an efficient coarse-to-fine evolutionary algorithm. Pattern Recognition 36(5), 1065–1081 (2003)CrossRefMATHGoogle Scholar
  23. 23.
    Ho, S.-Y., Ho, S.-J., Lin, Y.-K., Chu, W.-C.: An orthogonal simulated annealing algorithm for large floorplanning problems. IEEE trans. VLSI system 12(8), 874–886 (2004)CrossRefGoogle Scholar
  24. 24.
    Ho, S.-J., Ho, S.-Y., Shu, L.-S.: OSA: Orthogonal Simulated Annealing Algorithm and Its Application to Designing Mixed H2/H Optimal Controllers. IEEE Trans. System, Man, and Cybernetics-Part A 34(5), 588–600 (2004)CrossRefGoogle Scholar
  25. 25.
    Michalewicz, Z., Dasgupta, D., Le Riche, R.G., Schoenauer, M.: Evolutionary algorithms for constrained engineering problems. Computers & Industrial Engineering 30(4), 851–870 (1996)CrossRefGoogle Scholar
  26. 26.
    Ho, S.-Y., Chen, Y.-C.: An efficient evolutionary algorithm for accurate polygonal approximation. Pattern Recognition 34(12), 2305–2317 (2001)CrossRefMATHGoogle Scholar
  27. 27.
    Test Problems and Test Data for Multiobjective Optimizers, http://www.tik.ee.ethz.ch/~zitzler/testdata.html/

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shinn-Jang Ho
    • 1
  • Wen-Yuan Ku
    • 2
  • Jun-Wun Jou
    • 2
  • Ming-Hao Hung
    • 2
  • Shinn-Ying Ho
    • 3
  1. 1.Department of Automation EngineeringNational Formosa UniversityHuwei, YunlinTaiwan
  2. 2.Department of Information Engineering and Computer ScienceFeng Chia UniversityTaichungTaiwan
  3. 3.Department of Biological Science and Technology and Institute of BioinformaticsNational Chiao Tung UniversityHsinchuTaiwan

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