An improved multi-objective particle swarm optimizer for multi-objective problems

Expert Systems with Applications

Volumn 37, Issue 8, 2010, Pages 5872-5886

  Tsai, Shang Jeng ^a   Sun, Tsung Ying ^a   Liu, Chan Cheng ^b   Hsieh, Sheng Ta ^c   Wu, Wun Ci ^a   Chiu, Shih Yuan ^a  

^a NATIONAL DONG HWA UNIVERSITY   (Taiwan)

^b ACADEMIA SINICA   (Taiwan)

^c ORIENTAL INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Cluster; Disturbance; Global best particle; Jump improved operation; Multi objective optimization; Particle swarm optimizer; Proportional distribution

Indexed keywords

BENCH-MARK PROBLEMS; LOCAL OPTIMA; MULTI OBJECTIVE; MULTI-OBJECTIVE PROBLEM; NONDOMINATED SOLUTIONS; PARTICLE SWARM OPTIMIZERS; PERFORMANCE METRICS; SEARCHING ABILITY;

MULTIOBJECTIVE OPTIMIZATION;

EID: 77951208570     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.02.018     Document Type: Article

References (46)

1. Avigad, G. (2007). A simultaneous EMO for the solution of the multi-multi-objective optimization problem. In Proceedings of the IEEE World Congress on Evolutionary Computation (CEC'07) (pp. 2117-2124).
2. Bartz-Beielstein, T., Limbourg, P., Mehnen, J., Schmitt, K., Parsopoulos, K. E., Vrahatis, M. N., et al. (2003). Particle swarm optimizers for Pareto optimization with enhanced archiving techniques. In Proceedings of the IEEE congress on evolutionary computation (CEC'03) (Vol. 3, pp. 1780-1787).
3. Chiu, S. Y., Sun, T. Y., Hsieh, S. T., & Lin, C. W. (2007). Cross-searching strategy for multi-objective particle swarm optimization. In Proceedings of the IEEE world congress on evolutionary computation (CEC'07) (pp. 3135-3141).
4. Coello Coello, C. A., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the IEEE world congress on computational intelligence (CEC'02) (pp. 1051-1056).
5. Coello Coello C.A., Pulido G.T., and Lechuga M.S. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary computation 8 (2004) 256-279
6. Coello Coello C.A., Van Veldhuizen D.A., and Lamont G.B. Evolutionary algorithms for solving multi-objective problems (2002), Kluwer
7. Deb, K., & Kumar, A. (2007). Light beam search based multi-objective optimization using evolutionary algorithms. In Proceedings of the IEEE world congress on evolutionary computation (CEC'07) (pp. 2125-2132).
8. Deb, K., Agrawal, S., Pratab, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Marc Schoenauer et al. (Eds.), Proceedings of the parallel problem solving from nature VI conference, Lecture notes in computer science (Vol. 1917, pp. 849-858). Springer.
9. Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 2 (2002) 182-197
10. Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the 6th international symposium on micro machine and human science, Nagoya, Japan (pp. 39-43).
11. El-Abd, M., & Kamel, M. S. (2006). A hierarchal cooperative particle swarm optimizer. In Proceedings of the swarm intelligence symposium (pp. 43-47).
12. Fieldsend, J. E., & Singh, S. (2002). A multi-objective algorithm based upon particle swarm optimization and efficient data structure and turbulence. In Workshop on computational intelligence (pp. 34-44).
13. Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multiobjective optimization: formulation discussion and generalization. In S. Forrest (Ed.), Proceedings of the fifth international conference on genetic algorithms. Morgan Kauffman Publishers (pp. 416-423).
14. Goh C.K., and Tan K.C. A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation 13 1 (2009) 103-127
15. Gudise, V. G., & Venayagamoorthy, G. K. (2003). Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In Proceedings of the IEEE swarm intelligence symposium (pp. 110-117).
16. Hajela P., and Lin C.-Y. Genetic search strategies in multi-criterion optimal design. Structural and Multidisciplinary Optimization 4 2 (1992) 99-107
17. Heo J.S., Lee K.Y., and Garduno-Ramirez R. Multiobjective control of power plants using particle swarm optimization techniques. IEEE Transactions on Energy Conversion 21 2 (2006) 552-561
18. Hsieh S.T., Sun T.Y., Lin C.L., and Liu C.C. Effective learning rate adjustment of blind source separation based on an improved particle swarm optimizer. IEEE Transactions on Evolutionary Computation 12 2 (2008) 242-251
19. Hsieh S.T., Sun T.Y., Liu C.C., and Tsai S.J. Efficient population utilization strategy for particle swarm optimizer. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 39 2 (2009) 444-456
20. Hu, X., & Eberhart, R. (2002). Multiobjective optimization using dynamic neighborhood particle swarm optimization. In Proceedings of the IEEE world congress on computational intelligence (CEC'02) (pp. 1677-1681).
21. Hughes, E. J. (2007). MSOPS-II: A general-purpose many-objective optimiser. In Proceedings IEEE world congress on evolutionary computation (CEC'07) (pp. 3944-3951).
22. Kennedy, J., & Mendes, R. (2002). Population structure and particle swarm performance. In Proceedings of the IEEE world congress on evolutionary computation (CEC'02) (pp. 1671-1676).
23. Kitamura, S., Mori, K., Shindo, S., Izui, Y., & Ozaki, Y. (2005). Multiobjective energy management system using modified MOPSO. In Proceedings of the IEEE international conference on Systems, man and cybernetics (Vol. 4, pp. 3497-3503).
24. Knowles J.D., and Corne D.W. Approximating the non-dominated front using the Pareto archived evolution strategy. Evolutionary Computation 8 2 (2000) 149-172
25. Liang J.J., Qin A.K., Suganthan P.N., and Baskar S. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10 (2006) 281-296
26. Min, H.-Q., Zhu, J.-H., & Zheng, X.-J. (2005). Obstacle avoidance with multi-objective optimization by PSO in dynamic environment. In Proceedings of the international conference on machine learning and cybernetics (Vol. 5, pp. 2950-2956).
27. Mostaghim, S., & Teich, J. (2003). Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In Proceedings of the IEEE swarm intelligence symposium (pp. 26-33).
28. Ong, Y., Keane, A., & Nair, P. (2002). Surrogate-assisted coevolutionary search. In Proceedings of the 9th international conference on neural information processing (pp. 1140-1145).
29. Parsopoulos, K. E., & Vrahatis, M. N. (2002). Particle swarm optimization method in multiobjective. In Proceedings of ACM symposium on applied computing (SAC'02) (pp. 603-607).
30. Parsopoulos, K. E., & Vrahatis, M. N. (2004). UPSO-A unified particle swarm optimization scheme. In Lecture series on computational sciences (pp. 868-873).
31. Parsopoulos, K. E., Tasoulis, D. K., & Vrahatis, M. N. (2004). Multiobjective optimization using parallel vector evaluated particle swarm optimization. In Proceedings of the IASTED international conference on artificial intelligence and applications.
32. Peram, T., Veeramachaneni, K., & Mohan, C. K. (2003). Fitness-distance-ratio based particle swarm optimization. In Proceedings of the swarm intelligence symposium (pp. 174-181).
33. Ren, P., Gao, L.-Q., Li, N., Li, Y., & Lin, Z.-L. (2005). Transmission network optimal planning using the particle swarm optimization method. In Proceedings of the international conference on machine learning and cybernetics (Vol. 5, pp. 4006-4011).
34. Schaffer, J. D. (1984). Multiple objective optimization with vector evaluated genetic algorithms. PhD thesis, Vanderbilt University, 1984.
35. Schaffer, J. D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In Proceedings of the 1st IEEE international conference on genetic algorithms (pp. 93-100).
36. Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In Proceedings of the IEEE world congress on computational intelligence (pp. 69-73).
37. Srinivas N., and Deb K. Multiobjective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation 2 3 (1994) 221-248
38. Sun, T. Y., Wu, W. C., Tsai, S. J., Hsieh, S. T., Liu, C. C., & Chiu, S. Y. (2008). Particle swarm optimizer for multi-objective problems based on proportional distribution and cross-over operation. In Proceedings of the IEEE international conference on systems man and cybernetics (pp. 2658-2663).
39. van den Bergh F., and Engelbrecht A.P. A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8 (2004) 225-239
40. Van Veldhuizen, D. A. (1999). 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.
41. Van Veldhuizen, D. A., & Lamont, G. B. (1998). 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.
42. Yang, L., Hu, W., Yang, S., & Pu, S. (2006). Application of particle swarm optimization in multi-sensor multi-target tracking. In Proceedings of the 1st international symposium on systems and control in aerospace and astronautics (ISSCAA) (pp. 715-719).
43. Zhang, Y., Ji, C., Yuan, P., Li, M., Wang, C., Wang, G., et al. (2004). Particle swarm optimization for base station placement in mobile communication. In Proceedings of the IEEE international conference on networking sensing and control (Vol. 1, pp. 428-432).
44. Zitzler E., Deb K., and Thiele L. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8 2 (2000) 173-195
45. Zitzler, E., Laumanns, M., & Thiele, L. (2002). SPEA2: Improving the strength Pareto evolutionary algorithm. In K. Giannakoglou et al. (Eds.), EUROGEN 2001: Evolutionary methods for design, optimization and control with applications to industrial problems (pp. 95-100).
46. Zitzler E., and Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. Transactions on Evolutionary Computation 3 4 (1999) 257-271