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)


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.


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



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.


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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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