MOPSO: a proposal for multiple objective particle swarm optimization. This paper introduces a proposal to extend the heuristic called ”particle swarm optimization” (PSO) to deal with multiobjective optimization problems. Our approach uses the concept of Pareto dominance to determine the flight direction of a particle and it maintains previously found nondominated vectors in a global repository that is later used by other particles to guide their own flight. The approach is validated using several standard test functions from the specialized literature. Our results indicate that our approach is highly competitive with current evolutionary multiobjective optimization techniques.

References in zbMATH (referenced in 59 articles )

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  1. Wang, Yan; Zeng, Jian-chao: A multi-objective artificial physics optimization algorithm based on ranks of individuals (2013) ioport
  2. Xu, Jiuping; Tu, Yan; Zeng, Ziqiang: A nonlinear multiobjective bilevel model for minimum cost network flow problem in a large-scale construction project (2012)
  3. Chakraborty, Prithwish; Das, Swagatam; Roy, Gourab Ghosh; Abraham, Ajith: On convergence of the multi-objective particle swarm optimizers (2011)
  4. Cooren, Yann; Clerc, Maurice; Siarry, Patrick: MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm (2011)
  5. Zou, Wenping; Zhu, Yunlong; Chen, Hanning; Zhang, Beiwei: Solving multiobjective optimization problems using artificial bee colony algorithm (2011)
  6. Abido, M. A.: Multiobjective particle swarm optimization with nondominated local and global sets (2010)
  7. Krichen, Saoussen; Dahmani, Nadia: A particle swarm optimization approach for the bi-objective load balancing problem (2010)
  8. Ferreira de Carvalho, Danilo; Albanez Bastos-Filho, Carmelo José: Clan particle swarm optimization (2009)
  9. Tsou, Ching-Shih: Evolutionary Pareto optimizers for continuous review stochastic inventory systems (2009)
  10. Zheng, Xiangwei; Liu, Hong: A hybrid vertical mutation and self-adaptation based MOPSO (2009)
  11. Armellin, Roberto; Lavagna, Michèle: Multidisciplinary optimization of aerocapture maneuvers (2008) ioport
  12. Banks, Alec; Vincent, Jonathan; Anyakoha, Chukwudi: A review of particle swarm optimization. II: Hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008)
  13. Dioşan, Laura; Oltean, Mihai: What else is the evolution of PSO telling us? (2008) ioport
  14. Jin, Nanbo; Rahmat-Samii, Yahya: Particle swarm optimization for antenna designs in engineering electromagnetics (2008) ioport
  15. Brits, R.; Engelbrecht, A. P.; Van Den Bergh, F.: Locating multiple optima using particle swarm optimization (2007)
  16. Rahimi-Vahed, A. R.; Mirghorbani, S. M.: A multi-objective particle swarm for a flow shop scheduling problem (2007)
  17. Rahimi-Vahed, A. R.; Mirghorbani, S. M.; Rabbani, M.: A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem (2007) ioport
  18. Tripathi, Praveen Kumar; Bandyopadhyay, Sanghamitra; Pal, Sankar Kumar: Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients (2007)
  19. Yapicioglu, Haluk; Smith, Alice E.; Dozier, Gerry: Solving the semi-desirable facility location problem using bi-objective particle swarm (2007)