Dynamic Multi Objective Particle Swarm Optimization Based on a New Environment Change Detection Strategy

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


The dynamic of real-world optimization problems raises new challenges to the traditional particle swarm optimization (PSO). Responding to these challenges, the dynamic optimization has received considerable attention over the past decade. This paper introduces a new dynamic multi-objective optimization based particle swarm optimization (Dynamic-MOPSO). The main idea of this paper is to solve such dynamic problem based on a new environment change detection strategy using the advantage of the particle swarm optimization. In this way, our approach has been developed not just to obtain the optimal solution, but also to have a capability to detect the environment changes. Thereby, Dynamic-MOPSO ensures the balance between the exploration and the exploitation in dynamic research space. Our approach is tested through the most popularized dynamic benchmark’s functions to evaluate its performance as a good method.


Dynamic optimization Dynamic multi-objective problems Particle swarms optimization Dynamic environment Time varying parameters 



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-Lab.: REsearch Groups on Intelligent Machines, National Engineering School of Sfax (ENIS)University of SfaxSfaxTunisia

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