MCPSO: a multi-swarm cooperative particle swarm optimizer. A new optimization algorithm -- MCPSO, multi-swarm cooperative particle swarm optimizer, inspired by the phenomenon of symbiosis in natural ecosystems. MCPSO is based on a master-slave model, in which a population consists of one master swarm and several slave swarms. The slave swarms execute a single PSO or its variants independently to maintain the diversity of particles, while the master swarm evolves based on its own knowledge and also the knowledge of the slave swarms. According to the co-evolutionary relationship between master swarm and slave swarms, two versions of MCPSO are proposed, namely the competitive version of MCPSO (COM-MCPSO) and the collaborative version of MCPSO (COL-MCPSO), where the master swarm enhances its particles based on an antagonistic scenario or a synergistic scenario, respectively. In the simulation studies, several benchmark functions are performed, and the performances of the proposed algorithms are compared with the standard PSO (SPSO) and its variants to demonstrate the superiority of MCPSO.

References in zbMATH (referenced in 17 articles , 1 standard article )

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  1. Sobehart, Lionel; Harada, Hiroyuki: High performance rigid body simulation of modularized robots using constraint-based models (2018)
  2. Marinakis, Yannis; Migdalas, Athanasios; Sifaleras, Angelo: A hybrid particle swarm optimization -- variable neighborhood search algorithm for constrained shortest path problems (2017)
  3. Jakubcová, Michala; Máca, Petr; Pech, Pavel: Parameter estimation in rainfall-runoff modelling using distributed versions of particle swarm optimization algorithm (2015)
  4. Masegosa, Antonio David; Pelta, David Alejandro; Verdegay, José Luis: A centralised cooperative strategy for continuous optimisation: the influence of cooperation in performance and behaviour (2013)
  5. Sessa, Salvatore (ed.); Di Martino, Ferdinando (ed.); Perfilieva, Irina G. (ed.): Fuzzy functions, relations, and fuzzy transforms 2013 (2013)
  6. El Dor, Abbas; Clerc, Maurice; Siarry, Patrick: A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization (2012)
  7. Gan, Xiaobing; Wang, Yan; Li, Shuhai; Niu, Ben: Vehicle routing problem with time windows and simultaneous delivery and pick-up service based on MCPSO (2012)
  8. Li, Chunshien; Chiang, Tai-Wei: Intelligent financial time series forecasting: a complex neuro-fuzzy approach with multi-swarm intelligence (2012)
  9. Cooren, Yann; Clerc, Maurice; Siarry, Patrick: MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm (2011)
  10. Kanović, Željko; Rapaić, Milan R.; Jeličić, Zoran D.: Generalized particle swarm optimization algorithm - theoretical and empirical analysis with application in fault detection (2011)
  11. Marinaki, Magdalene; Marinakis, Yannis; Stavroulakis, Georgios E.: Fuzzy control optimized by a multi-objective partial swarm optimization algorithm for vibration suppression of smart structures (2011)
  12. Vanneschi, Leonardo; Codecasa, Daniele; Mauri, Giancarlo: A comparative study of four parallel and distributed PSO methods (2011) ioport
  13. Chen, Hanning; Zhu, Yunlong; Hu, Kunyuan: Discrete and continuous optimization based on multi-swarm coevolution (2010)
  14. Marinakis, Yannis; Marinaki, Magdalene: A hybrid multi-swarm particle swarm optimization algorithm for the probabilistic traveling salesman problem (2010)
  15. Chen, Hanning; Zhu, Yunlong: Optimization based on symbiotic multi-species coevolution (2008)
  16. Han, Kai; Zhao, Jun; Xu, Zu-Hua; Qian, Ji-Xin: A closed-loop particle swarm optimizer for multivariable process controller design (2008)
  17. Niu, Ben; Zhu, Yunlong; He, Xiaoxian; Wu, Henry: MCPSO: a multi-swarm cooperative particle swarm optimizer (2007)