A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization

  • Authors:

  • Danial Yazdani;Babak Nasiri;Alireza Sepas-Moghaddam;Mohammad Reza Meybodi

  • Affiliations:

  • Young Researchers Club and Elites, Mashhad Branch, Islamic Azad University, Mashhad, Iran;Department of Computer Engineering and Information Technology, Islamic Azad University, Qazvin Branch, Qazvin, Iran;Department of Computer Engineering and Information Technology, Islamic Azad University, Qazvin Branch, Qazvin, Iran;Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran and Institute for Studies in Theoretical Physics and Mathematics (IPM), School of Co ...

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2013

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Abstract

Optimization in dynamic environment is considered among prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization approach, in which several mechanisms were employed to face the challenges in this domain. In this algorithm, an improved multi-swarm approach has been used for finding peaks in the problem space and tracking them after an environment change in an appropriate time. Moreover, a novel method based on change in velocity vector and particle positions was proposed to increase the diversity of swarms. For improving the efficiency of the algorithm, a local search based on adaptive exploiter particle around the best found position as well as a novel awakening-sleeping mechanism were utilized. The experiments were conducted on Moving Peak Benchmark which is the most well-known benchmark in this domain and results have been compared with those of the state-of-the art methods. The results show the superiority of the proposed method.