MCPSO: a multi-swarm cooperative particle swarm optimizer.

*(English)*Zbl 1112.65055Summary: 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.

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.

##### Keywords:

master-slave model; numerical examples; comparison of methods; genetic algorithm; algorithms##### Software:

MCPSO
PDF
BibTeX
XML
Cite

\textit{B. Niu} et al., Appl. Math. Comput. 185, No. 2, 1050--1062 (2007; Zbl 1112.65055)

Full Text:
DOI

##### References:

[1] | R.C. Eberchart, J. Kennedy, A new optimizer using particle swarm theory, in: Proceedings of the 6th International Symposium on Micromachine and Human Science, Nagoya, Japan, 1995, pp. 39-43. |

[2] | J. Kennedy, R.C. Eberchart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942-1948. |

[3] | Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, in: Proceedings of Congress on Evolutionary Computation, Washington, DC, 1999, pp. 1945-1949. |

[4] | Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, AK, May 1998, pp. 69-73. |

[5] | Kennedy, J.; Eberchart, R.C.; Shi, Y., Swarm intelligence, (2001), Morgan Kaufmann Publishers San Francisco |

[6] | R. Mendes, P. Cortez, M. Rocha, J. Neves, Particle swarms for feedforward neural network training, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), 2002, pp. 1895-1899. |

[7] | G.K. Venayagamoorthy, S. Doctor, Navigation of mobile sensors using PSO and embedded PSO in a fuzzy logic controller, in: Proceedings of the 39th IEEE IAS Annual Meeting on Industry Applications, Seattle, USA, 2004, pp. 1200-1206. |

[8] | K.E. Parsopoulos, E.I. Papageorgiou, P.P. Groumpos, M.N Vrahatis, A first study of fuzzy cognitive maps learning using particle swarm optimization, in: Proceedings of IEEE Congress on Evolutionary Computation 2003 (CEC 2003), Canbella, Australia, 2003, pp. 1440-1447. |

[9] | Abido, M.A., Optimal power flow using particle swarm optimization, Int. J. elect. power energy syst., 24, 7, 563-571, (2002) |

[10] | Tandon, V.; El-Mounayri, H.; Kishawy, H., NC end milling optimization using evolutionary computation, Int. J. Mach. tools manuf., 42, 595-605, (2002) |

[11] | P.J. Angeline, Evolutionary optimization versus particle swarm optimization and philosophy and performance difference, in: Proceedings of 7th Annual Conference on Evolutionary Programming, San Diego, USA, 1998, pp. 601-610. |

[12] | Shi, X.H.; Liang, Y.C.; Lee, H.P.; Lu, C.; Wang, L.M., An improved GA and a novel PSO-GA-based hybrid algorithm, Inform. process. lett., 93, 255-261, (2005) · Zbl 1173.68828 |

[13] | W.J. Zhang, X.F. Xie, DEPSO: hybrid particle swarm with differential evolution operator, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Washington, DC, USA, 2003, pp. 3816-3821. |

[14] | He, S.; Wu, Q.H.; Wen, J.Y.; Saunders, J.R.; Paton, R.C., A particle swarm optimizer with passive congregation, Biosystems, 78, 135-147, (2004) |

[15] | Niu, B.; Zhu, Y.L.; He, X.X., Multi-population cooperative particle swarm optimization, Lect. notes art. intell., 3630, 874-883, (2005) |

[16] | R.C. Eberhart, Y. Shi, Tracking and optimizing dynamic systems with particle swarms, in: Proceedings of IEEE Congress on Evolutionary Computation, Seoul, Korea, 2001, pp. 94-97. |

[17] | Clerc, M.; Kennedy, J., The particle swarm: explosion, stability, and convergence in a multidimensional complex space, IEEE trans. evolut. comput., 6, 58-73, (2002) |

[18] | Ahmadjian, V.; Paracer, S., Symbiosis: an introduction to biological associations, (2000), Oxford University Press New York |

[19] | Sapp, J., The dynamics of symbiosis: an historical overview, Canadian J. bot., 82, 1-11, (2004) |

[20] | Douglas, A.E., Symbiotic interactions, (1994), Oxford University Press Oxford |

[21] | J. VesterstrĂ¸m, R. Thomsen, A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, in: Proceedings of the 2004 Congress on Evolutionary Computation, Portland, Oregon, USA, 2004, pp. 1980-1987. |

[22] | P.J. Angeline, Using selection to improve particle swarm optimization, in: Proceedings of IEEE International Conference on Computational Intelligence, 1998, pp. 84-89. |

[23] | R.C. Eberhart, Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization, in: Proceedings of IEEE International Congress on Evolutionary Computation, San Diego, CA, 2000, pp. 84-88. |

[24] | Y. Shi, R.C. Eberhart, Fuzzy adaptive particle swarm optimization, in: Proceedings of IEEE International Congress on Evolutionary Computation, Seoul, Korea, 2001, pp. 101-106. |

[25] | P.N. Suganthan, Particle swarm optimizer with neighborhood operator, in: Proceedings of IEEE International Congress on Evolutionary Computation, Washington, DC, USA, 1999, pp. 1958-1962. |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.