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Multi Objective Particle Swarm Optimization Based Cooperative Agents with Automated Negotiation

  • Najwa Kouka
  • Raja Fdhila
  • Adel M. Alimi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

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.

Keywords

Multi objective optimization problems Particle swarm optimization Multi agent system Distributed architecture Automated negotiation 

Notes

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.

References

  1. 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)CrossRefGoogle Scholar
  2. 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. 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)CrossRefGoogle Scholar
  4. 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. 5.
    Alimi, M.A.: Evolutionary computation for the recognition of on-line cursive handwriting. IETE J. Res. 48(5), 385–396 (2002)CrossRefGoogle Scholar
  6. 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. 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)CrossRefGoogle Scholar
  8. 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. 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. 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)CrossRefGoogle Scholar
  11. 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)zbMATHMathSciNetGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Wooldridge, M.: An Introduction to Multiagent System, 2nd edn. Wiley, Chichester (2009)Google Scholar
  15. 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)CrossRefGoogle Scholar
  16. 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. 17.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE, Perth (1995)Google Scholar
  18. 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. 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. 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. 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. 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. 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. 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. 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. 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. 27.
    Ilie, S., Bădică, C.: Multi-agent approach to distributed ant colony optimization. Sci. Comput. Program. 78(6), 762–774 (2013)CrossRefGoogle Scholar
  28. 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. 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. 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. 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

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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