Multi Objective Particle Swarm Optimization Based Cooperative Agents with Automated Negotiation | SpringerLink
Skip to main content
Springer Nature Link
Log in

Multi Objective Particle Swarm Optimization Based Cooperative Agents with Automated Negotiation

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

Included in the following conference series:

Abstract

This paper investigates a new hybridization of multi-objective particle swarm optimization (MOPSO) and cooperative agents (MOPSO-CA) to handle the problem of stagnation encounters in MOPSO, which leads solutions to trap in local optima. The proposed approach involves a new distribution strategy based on the idea of having a set of a sub-population, each of which is processed by one agent. The number of the sub-population and agents are adjusted dynamically through the Pareto ranking. This method allocates a dynamic number of sub-population as required to improve diversity in the search space. Additionally, agents are used for better management for the exploitation within a sub-population, and for exploration among sub-populations. Furthermore, we investigate the automated negotiation within agents in order to share the best knowledge. To validate our approach, several benchmarks are performed. The results show that the introduced variant ensures the trade-off between the exploitation and exploration with respect to the comparative algorithms.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

References

  1. Ghamisi, P., Couceiro, M.S., Martins, F.M.L., Benediktsson, J.A.: Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 52(5), 2382–2394 (2014)

    Article  Google Scholar 

  2. Fdhila, R., Elloumi, W., Hamdani, T.M.: Distributed MOPSO with dynamic Pareto front driven population analysis for TSP problem. In: The 6th International Conference Soft Computing and Pattern Recognition, pp. 294–299. IEEE, Tunis (2014)

    Google Scholar 

  3. Ben Moussa, S., Zahour, A., Benabdelhafid, A., Alimi, M.A.: New features using fractal multi-dimensions for generalized Arabic font recognition. Pattern Recogn. Lett. 31(5), 361–371 (2010)

    Article  Google Scholar 

  4. Bezine, H., Alimi, M.A., Derbel, N.: Handwriting trajectory movements controlled by a Bêta-elliptic model. In: 7th International Conference on Document Analysis and Recognition, pp. 1228–1232. IEEE, Edinburgh (2003)

    Google Scholar 

  5. Alimi, M.A.: Evolutionary computation for the recognition of on-line cursive handwriting. IETE J. Res. 48(5), 385–396 (2002)

    Article  Google Scholar 

  6. Boubaker, H., Kherallah, M., Alimi, M.A.: New algorithm of straight or curved baseline detection for short arabic handwritten writing. In: 10th International Conference on Document Analysis and Recognition, pp. 778–782. IEEE, Barcelona (2009)

    Google Scholar 

  7. Slimane, F., Kanoun, S., Hennebert, J., Alimi, M.A., Ingold, R.: A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution. Pattern Recogn. Lett. 34(2), 209–218 (2013)

    Article  Google Scholar 

  8. Elbaati, A., Boubaker, H., Kherallah, M., Alimi, M.A., Ennaji, A., Abed, H.E.: Arabic handwriting recognition using restored stroke chronology. In: 10th International Conference on Document Analysis and Recognition, pp. 411–415. IEEE, Barcelona (2009)

    Google Scholar 

  9. Dhahri, H., Alimi, M.A.: The modified differential evolution and the RBF (MDE-RBF) neural network for time series prediction. In: IEEE International Conference on Neural Networks - Conference Proceedings, pp. 2938–2943. IEEE, Vancouver (2006)

    Google Scholar 

  10. Bouaziz, S., Dhahri, H., Alimi, M.A., Abraham, A.: A hybrid learning algorithm for evolving flexible Beta basis function neural tree model. Neurocomputing 117, 107–117 (2013)

    Article  Google Scholar 

  11. Baccour, L., Alimi, M.A., John, R.I.: Similarity measures for intuitionistic fuzzy sets: state of the art. J. Intell. Fuzzy Syst. 24(1), 37–49 (2013)

    MATH  MathSciNet  Google Scholar 

  12. Bahareh, N., Mohd, Z., Ahmad, N., Mohammad, N.R., Salwani, A.: A survey: particle swarm optimization based algorithms to solve premature convergence problem. J. Comput. Sci. 10(9), 1758–1765 (2014)

    Article  Google Scholar 

  13. Gong, Y.-J., et al.: Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Appl. Soft Comput. 34, 286–300 (2015)

    Article  Google Scholar 

  14. Wooldridge, M.: An Introduction to Multiagent System, 2nd edn. Wiley, Chichester (2009)

    Google Scholar 

  15. Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: prospects, methods and challenges. Int. J. Group Decis. Negot. 10(2), 199–215 (2001)

    Article  Google Scholar 

  16. Deb, K., Deb, K.: Multi-objective optimization. In: Burke, E., Kendall, G. (eds.) Search Methodologies Introductory Tutorials in Optimization and Decision Support Techniques, pp. 403–449. Springer, Boston (2014). doi:10.1007/978-1-4614-6940-7_15

    Google Scholar 

  17. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE, Perth (1995)

    Google Scholar 

  18. Fdhila, R., Hamdani, T., Alimi, M.A.: A new distributed approach for MOPSO based on population Pareto fronts analysis and dynamic. In: Systems Man and Cybernetics, pp. 947–954. IEEE, Istanbul (2010)

    Google Scholar 

  19. Fdhila, R., Hamdani, T.M., Alimi, M.A.: A new hierarchical approach for MOPSO based on dynamic subdivision of the population using Pareto fronts. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 947–954. IEEE, Istanbul (2010)

    Google Scholar 

  20. Fdhila, R., Hamdani, T.M., Alimi, M.A.: Optimization algorithms, benchmarks and performance measures: from static to dynamic environment. In: The 15th International Conference on Intelligent Systems Design and Applications, pp. 597–603. IEEE, Marrakech (2015)

    Google Scholar 

  21. Fdhila, R., Hamdani, T.M., Alimi, M.A.: Population-based distribution of MOPSO with continuous flying Pareto fronts particles. J. Inf. Process. Syst. (2016)

    Google Scholar 

  22. Fdhila, R., Hamdani, T.M., Alimi, M.A.: Distributed MOPSO with a new population subdivision technique for the feature selection. In: The 5th International Symposium Computational Intelligence and Intelligent Informatics, pp. 81–86. IEEE, Floriana (2011)

    Google Scholar 

  23. Fdhila, R., Hamdani, T.M., Alimi, M.A.: A multi objective particles swarm optimization algorithm for solving the routing pico-satellites problem. In: Systems, Man, and Cybernetics, pp. 1402–1407. IEEE, Seoul (2012)

    Google Scholar 

  24. Fdhila, R., Walha, C., Hamdani, T.M., Alimi, M.A.: Hierarchical design for distributed MOPSO using sub-swarms based on a population Pareto fronts analysis for the grasp planning problem. In: The 13th International Conference on Hybrid Intelligent Systems, pp. 203–208. IEEE, Gammarth (2013)

    Google Scholar 

  25. Fdhila, R., Ouarda, W., Alimi, M.A., Abraham, A.: A new scheme for face recognition system using a new 2-level parallelized hierarchical multi objective particle swarm optimization algorithm. J. Inf. Assur. Secur. 11(6), 385–394 (2016)

    Google Scholar 

  26. Kouka, N., Fdhila, R., Alimi, M.A.: A new architecture based distributed agents using PSO for multi objective optimization. In: 13th International Conference on Applied Computing (2016)

    Google Scholar 

  27. Ilie, S., Bădică, C.: Multi-agent approach to distributed ant colony optimization. Sci. Comput. Program. 78(6), 762–774 (2013)

    Article  Google Scholar 

  28. Takano, R., Yamazaki, D., Ichikawa,Y., Hattori, K., Takadama, K.: Multiagent-based ABC algorithm for autonomous rescue agent cooperation. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 585–590. IEEE, San Diego (2014)

    Google Scholar 

  29. Yingchun, C., Wei, W.: MAS-based distributed particle swarm optimization. In: 8th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4. IEEE, Shanghai (2012)

    Google Scholar 

  30. Godinez, A.C., Espinosa, L.E.M., Montes, E.M.: An experimental comparison of multiobjective algorithms: NSGA-II and OMOPSO. In: Conference Electronics, Robotics and Automotive Mechanics, pp. 28–33. IEEE, Morelos (2010)

    Google Scholar 

  31. Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. Technical report, CES-487 (2009)

    Google Scholar 

Download references

Acknowledgement

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

Author information

Authors and Affiliations

  1. REGIM-Laboratory: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Najwa Kouka, Raja Fdhila & Adel M. Alimi

Authors
  1. Najwa Kouka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raja Fdhila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adel M. Alimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Najwa Kouka .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kouka, N., Fdhila, R., Alimi, A.M. (2017). Multi Objective Particle Swarm Optimization Based Cooperative Agents with Automated Negotiation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70093-9_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation