Skip to main content
SpringerLink
Log in

A Comprehensive Study of Particle Swarm Based Multi-objective Optimization

  • Conference paper
Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 132))

Abstract

Recently there has been a growing interest in evolutionary multiobjective optimization algorithms which combines two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. This paper presents a comprehensive study of Multi-Objective Optimization (MOO) with Particle Swarm Optimization (PSO). Different suggestions of various researchers have been compiled to give a first-hand information of PSO based MOO. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements and the availability of software.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway (1995)

    Chapter  Google Scholar 

  2. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. 6th Int. Symp. Micro Machine and Human Science (MHS), pp. 39–43 (October 1995)

    Google Scholar 

  3. Kennedy, J., Eberhart, R.C.: A Discrete Binary Version of the Particle Swarm Algorithm. In: Proceedings of the 1997 IEEE Conference on Systems, Man, and Cybernetics, pp. 4104–4109. IEEE Service Center, Piscataway (1997)

    Google Scholar 

  4. Shi, Y., Eberhart, R.C.: Parameter Selection in Particle Swarm Optimization. In: Porto, V.W., Sarava-nan, N., Waagen, D., Eibe, A. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  5. Eberhart, R., Shi, Y.: Comparison between Genetic Algorithms and Particle Swarm Optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eibe, A. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–619. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  6. Moore, J., Chapman, R.: Application of Particle Swarm to Multiobjective Optimization. In: Department of Computer Science and Software Engineering, Auburn University, (unpublished manuscript) (1999)

    Google Scholar 

  7. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  8. Ray, T., Liew, K.: A Swarm Metaphor for Multiobjective Design Optimization. Engineering Optimization 34(2), 141–153 (2002)

    Article  Google Scholar 

  9. Whitley, D., Goldberg, D., Cantú-Paz, E., Spector, L., Parmee, I., Beyer, H.-G.: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 771–777. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  10. Parsopoulos, K., Vrahatis, M.: Particle Swarm Optimization Method in Multiobjective Problems. In: SAC 2002, pp. 603–607. ACM Press (2002)

    Google Scholar 

  11. Jin, Y., Okabe, T., Sendhoff, B.: Dynamic Weighted Aggregation for Evolutionary Multi-Objective Op-timization: Why Does It Work and How? In: Spector, L., Goodman, E.D., Wu, A., Langdon, W., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 1042–1049. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  12. Parsopoulos, K., Tasoulis, D., Vrahatis, M.: Multiobjective Optimization Using Parallel Vector Evaluated Particle Swarm Optimization. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2004), vol. 2, pp. 823–828. ACTA Press, Innsbruck (2004)

    Google Scholar 

  13. Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Genetic Al-gorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100. Lawrence Erlbaum, Hillsdale (1985)

    Google Scholar 

  14. Hu, X., Eberhart, R.: Multiobjective Optimization Using Dynamic Neighborhood Particle Swarm Optimization. In: Congress on Evolutionary Computation (CEC 2002), Piscata-way, New Jersey, vol. 2, pp. 1677–1681. IEEE Service Center (May 2002)

    Google Scholar 

  15. Hu, X., Eberhart, R.C., Shi, Y.: Particle Swarm with Extended Memory for Multiobjective Optimization. In: 2003 IEEE Swarm Intelligence Symposium Proceedings, Indianapolis, Indiana, USA, pp. 193–197. IEEE Service Center (April 2003)

    Google Scholar 

  16. Fieldsend, J.E., Singh, S.: A Multi-Objective Algorithm based upon Particle Swarm Optimisation, an Efficient Data Structure and Turbulence. In: Proceedings of the 2002 U.K. Workshop on Computational Intelligence, Birmingham, UK, pp. 37–44 (September 2002)

    Google Scholar 

  17. Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multi-objective Optimization. Evolutionary Computation 10(3), 263–282 (2002)

    Article  Google Scholar 

  18. Toscano Pulido, G., Coello Coello, C.A.: Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer. In: Deb, K., et al. (eds.) GECCO 2004, Part-I. LNCS, vol. 3102, pp. 225–237. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  19. Coello Coello, C.A., Toscano Pulido, G., Salazar Lechuga, M.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8(3), 256–279 (2004)

    Article  Google Scholar 

  20. Baltar, A.M., Fontane, D.G.: A generalized multiobjective particle swarm optimization solver for spreadsheet models: application to water quality. In: Hydrology Days 2006, Fort Collins, Colorado, USA (March 2006)

    Google Scholar 

  21. Tayal, M.: Particle Swarm Optimization for Mechanical Design. Master’s thesis, The University of Texas at Arlington, Arlington, Texas, USA (December 2003)

    Google Scholar 

  22. Mostaghim, S., Teich, J.: Strategies for Finding Good Local Guides in Multi-objective Particle Swarm Optimization (MOPSO). In: 2003 IEEE Swarm Intelligence Symposium Proceedings, pp. 26–33. IEEE Service Center, Indianapolis (2003)

    Google Scholar 

  23. Mostaghim, S., Teich, J.: Covering Pareto-optimal Fronts by Subswarms in Multi-objective Particle Swarm Optimization. In: 2004 Congress on Evolutionary Computation (CEC 2004), vol. 2, pp. 1404–1411. IEEE Service Center, Portland (2004)

    Google Scholar 

  24. Li, X.: A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003, Part-I. LNCS, vol. 2723, pp. 37–48. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  25. Li, X.: Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function. In: Deb, K., et al. (eds.) GECCO 2004, Part-I. LNCS, vol. 3102, pp. 117–128. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  26. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  27. Balling, R.: The Maximin Fitness Function; Multi-objective City and Regional Planning. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 1–15. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  28. Srinivasan, D., Seow, T.H.: Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multiobjective Optimization Problem. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), vol. 4, pp. 2292–2297. IEEE Press, Canberra (2003)

    Chapter  Google Scholar 

  29. Srinivasan, D., Seow, T.H.: Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-Criteria Optimization Problems. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances And Applications, pp. 147–165. Springer, London (2005) ISBN 1-85233-787-7

    Chapter  Google Scholar 

  30. Coello Coello, C.A., Salazar Lechuga, M.: MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. In: Congress on Evolutionary Computation (CEC 2002), vol. 2, pp. 1051–1056. IEEE Service Center, Piscataway (2002)

    Google Scholar 

  31. Zhang, L., Zhou, C., Liu, X., Ma, Z., Liang, Y.: Solving Multi Objective Optimization Problems Using Particle Swarm Optimization. In: Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), vol. 4, pp. 2400–2405. IEEE Press, Canberra (2003)

    Chapter  Google Scholar 

  32. Baumgartner, U., Magele, C., Renhart, W.: Pareto Optimality and Particle Swarm Optimization. IEEE Transactions on Magnetics 40(2), 1172–1175 (2004)

    Article  Google Scholar 

  33. Chow, C., Tsui, H.: Autonomous Agent Response Learning by a Multi-Species Particle Swarm Optimization. In: 2004 Congress on Evolutionary Computation (CEC 2004), vol. 1, pp. 778–785. IEEE Service Center, Portland (2004)

    Google Scholar 

  34. Ho, S., Yang, S., Ni, G., Lo, E.W., Wong, H.: A Particle Swarm Optimization-Based Method for Multiobjective Design Optimizations. IEEE Transactions on Magnetics 41(5), 1756–1759 (2005)

    Article  Google Scholar 

  35. Mahfouf, M., Chen, M.-Y., Linkens, D.A.: Adaptive Weighted Particle Swarm Optimisation for Multi-objective Optimal Design of Alloy Steels. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004, Part-VIII. LNCS, vol. 3242, pp. 762–771. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  36. Zhao, B., Cao, Y.J.: Multiple objective particle swarm optimization technique for economic load dispatch. Journal of Zhejiang University Science 6A(5), 420–427 (2005)

    Article  Google Scholar 

  37. Dehuria, S., Chob, S.B.: Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: A review and state-of-the-art computer science review. Elsevier (2008)

    Google Scholar 

  38. Alam, S., Dobbie, G., Riddle, P.: An Evolutionary Particle Swarm Optimization Algorithm for Data Clustering. In: 2008 IEEE Swarm Intelligence Symposium, St. Louis MO USA, September 21-23 (2008)

    Google Scholar 

  39. Juan, Jos, Nebro, Coello Coello, C.A.: Multi-Objective Particle Swarm Optimizers: An Experimental Comparison (2009)

    Google Scholar 

  40. Guliashki, V., Toshev, H., Korsemov, C.: Survey of Evolutionary Algorithms Used in Multiobjective Optimization bulgarian academy of sciences problems of engineering cybernetics and robotics, sofia (2009)

    Google Scholar 

  41. Krami, N., El-Sharkawi, M.A., Akherraz, M.: Multi Objective Particle Swarm Optimization Technique for Reactive Power Planning. In: 2006 Swarm Intelligence Symposium (SIS 2006), pp. 170–174. IEEE Press, Indianapolis (2006)

    Google Scholar 

  42. Li, Ransikarn, Esraa: A Novel Diversity Guided Particle Swarm Multi-objective Optimization Algorithm International Journal of Digital Content Technology and its Applications 5(1) (January 2011)

    Google Scholar 

  43. Nor, Mohamad, Ammar: Particle Swarm Optimization for Constrained and Multiobjective Problems: A Brief Review. In: 2011 International Conference on Management and Artificial Intelligence IPEDR, vol. 6. IACSIT Press, Bali (2011)

    Google Scholar 

  44. Benameur, L., Alami, J., El Imrani, A.: A New Hybrid Particle Swarm Optimization Algorithm for Handling Multiobjective Problem Using Fuzzy Clustering Technique 2009. In: IEEE 2009 International Conference on Computational Intelligence, Modelling and Simulation (2009)

    Google Scholar 

  45. Xu, B., Yu, J., Zhu, Y.: Multi-Objective PSO Algorithm Based on Escalating Strategy. IEEE (2010)

    Google Scholar 

  46. Pei, Y.: AMOPSO Approach to Grid Workflow Scheduling Asia-Pacific Conference on Wearable Computing Systems (2010)

    Google Scholar 

  47. Daneshyari, M., Yen, G.G.: Cultural-Based Multiobjective Particle Swarm Optimization. IEEE Transactions on Systems, Man, and Cybernetics—PART B: Cybernetics 41(2) (April 2011)

    Google Scholar 

  48. Liu, J., Chen, Y.: Dynamic Biclustering of Microarray Data with MOPSO. In: IEEE International Conference on Granular Computing (2010)

    Google Scholar 

  49. Li, Z., Zhu, Z., Zhang, H.: DSMOPSO: A Distance Sorting based Multiobjective Particle Swarm Optimization Algorithm. In: Sixth International Conference on Natural Computation, ICNC 2010 (2010)

    Google Scholar 

  50. Lin, H.H.: A Multi-objective Particle Swarm Optimization for Openshop Scheduling Problems. In: Sixth International Conference on Natural Computation, ICNC 2010 (2010)

    Google Scholar 

  51. Yen, G.G., Leong, W.F.: Constraint Handling Procedure for Multiobjective Particle Swarm Optimization. IEEE (2010)

    Google Scholar 

  52. Yao, J., Yang, B., Zhang, M., Kong, Y.: Multiobjective Particle Swarm Optimization with Predatory Escaping Behavior (2011)

    Google Scholar 

  53. Pontes, M.R., Lima Neto, F.B., Carmelo, J.A.: Bastos-Filho Adaptive Clan Particle Swarm Optimization

    Google Scholar 

  54. Bastos-Filho, C.J.A., Miranda, P.B.C.: Multi-Objective Particle Swarm Optimization using Speciation. IEEE (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept of IT, GIT, Gitam University, India

    Samantula Mohankrishna

  2. IME Sahibabad-Ghaziabad, India

    Divya Maheshwari

  3. Vizag Steel, Vishakapatnam, India

    P. Satyanarayana

  4. Dept of CSE, Anil Neerukonda Institute of Technology and Science, India

    Suresh Chandra Satapathy

Authors
  1. Samantula Mohankrishna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Divya Maheshwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Satyanarayana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suresh Chandra Satapathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept of Computer Science and Engineering ANITS, Andhra University, Sangivalasa, 530003, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. College of Engineering Dept. of CS&SE ANITS, Andhra University, Sangivalasa, 530003, Vishakapatnam, India

    P. S. Avadhani

  3. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mohankrishna, S., Maheshwari, D., Satyanarayana, P., Satapathy, S.C. (2012). A Comprehensive Study of Particle Swarm Based Multi-objective Optimization. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds) Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27443-5_79

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27443-5_79

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27442-8

  • Online ISBN: 978-3-642-27443-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation