Diversity rate of change measurement for particle swarm optimisers

被引：14
作者：
机构：
[1] Department of Computer Science, University of Pretoria, Pretoria
来源：
| 1600年 / Springer Verlag卷 / 8667期
关键词：
Economic and social effects - Piecewise linear techniques;
D O I：
10.1007/978-3-319-09952-1_8
中图分类号：
学科分类号：
摘要：
The diversity of a particle swarm can reflect the swarm’s explorative/exploitative behaviour at a given time step. This paper proposes a diversity rate of change measure to quantify the rate at which particle swarms decrease their diversity over time. The proposed measure is based on a two-piecewise linear approximation of diversity measurements sampled at regular time steps. The proposed measure is the slope of the first of the two lines. It is shown that, when comparing the measure among different algorithms, the measure reflects the differences in the behaviour of algorithms in terms of their exploration-exploitation trade-off. The measure can potentially be used to characterise and classify different algorithms based on algorithm behaviour. © Springer International Publishing Switzerland 2014.
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页码：86 / 97
页数：11
相关论文
共 22 条
  • [1] Van Den Bergh F., Engelbrecht A.P., A new locally convergent particle swarm optimizer, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 96-101, (2002)
  • [2] Van Den Bergh F., Engelbrecht A.P., A study of particle swarm optimization particle trajectories, Information Sciences, 176, 8, pp. 937-971, (2006)
  • [3] Chen M.R., Li X., Zhang X., Lu Y.Z., A novel particle swarm optimizer hybridized with extremal optimization, Applied Soft Computing, 10, 2, pp. 367-373, (2010)
  • [4] De Jong K.A., Analysis of the Behavior of a Class of Genetic Adaptive Systems, (1975)
  • [5] Eberhart R.C., Kennedy J., A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1, pp. 39-43, (1995)
  • [6] Engelbrecht A.P., Computational Intelligence: An Introduction, (2007)
  • [7] Engelbrecht A.P., Scalability of a heterogeneous particle swarm optimizer, Proceedings of the 2011 IEEE Symposium on Swarm Intelligence, pp. 1-8, (2011)
  • [8] Fan S., Chang J.M., Dynamic multi-swarm particle swarm optimizer using parallel PC cluster systems for global optimization of large-scale multimodal functions, Engineering Optimization, 42, 5, pp. 431-451, (2010)
  • [9] Kennedy J., Bare bones particle swarms, Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp. 80-87, (2003)
  • [10] Kennedy J., Eberhart R., Et al., Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, 4, pp. 1942-1948, (1995)
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