Algorithms

Currently, two classes of algorithms are implemented: quantum-behaved particle swarm algorithms and quantum-behaved particle multi-swarm algorithms. While the first class of algorithms has been implemented according to relevant publications [1-5], the second class of algorithms has been developed (and implemented) by the author. Each class of algorithms has different (sub)variants (referred to here as particle types). The following links will take you to the description of each algorithm:

References

[1] Y. Shi and R. Eberhart, ‘A modified particle swarm optimizer’, in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), Anchorage, AK, USA, 1998, pp. 69–73. doi: 10.1109/ICEC.1998.699146.

[2] J. Kennedy and R. Eberhart, ‘Particle swarm optimization’, in Proceedings of ICNN’95-international conference on neural networks, 1995, vol. 4, pp. 1942–1948.

[3] Jun Sun, Bin Feng, and Wenbo Xu, ‘Particle swarm optimization with particles having quantum behavior’, in Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), Portland, OR, USA, 2004, pp. 325–331. doi: 10.1109/CEC.2004.1330875.

[4] J. Sun, W. Fang, X. Wu, V. Palade, and W. Xu, ‘Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection’, Evolutionary Computation, vol. 20, no. 3, pp. 349–393, Sep. 2012, doi: 10.1162/EVCO_a_00049.

[5] J. Sun, W. Fang, V. Palade, X. Wu, and W. Xu, ‘Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point’, Applied Mathematics and Computation, vol. 218, no. 7, pp. 3763–3775, Dec. 2011, doi: 10.1016/j.amc.2011.09.021.