Communication Diversity in Particle Swarm Optimizers
Since they were introduced, Particle Swarm Optimizers have suffered from early stagnation due to premature convergence. Assessing swarm spatial diversity might help to mitigate early stagnation but swarm spatial diversity itself emerges from the main property that essentially drives swarm optimizers towards convergence and distinctively distinguishes PSO from other optimization techniques: the social interaction between the particles. The swarm influence graph captures the structure of particle interactions by monitoring the information exchanges during the search process; such graph has been shown to provide a rich overall structure of the swarm information flow. In this paper, we define swarm communication diversity based on the component analysis of the swarm influence graph. We show how communication diversity relates to other measures of swarm spatial diversity as well as how each swarm topology leads to different communication signatures. Moreover, we argue that swarm communication diversity might potentially be a better way to understand early stagnation since it takes into account the (social) interactions between the particles instead of properties associated with individual particles.
KeywordsPSO Swarm assessment Premature convergence Early stagnation analysis Component graph analysis
Marcos Oliveira, Diego Pinheiro and Bruno Andrade would like to thank the Science Without Borders program (CAPES, Brazil) for financial support under grants 1032/13-5, 0624/14-4 and 88888.067201/2013-00.
- 1.Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: 2007 Swarm Intelligence Symposium, SIS 2007, pp. 120–127. IEEE, April 2007Google Scholar
- 2.Cheng, S., Shi, Y.: Diversity control in particle swarm optimization. In: 2011 IEEE Symposium on Swarm Intelligence (SIS), pp. 1–9, April 2011Google Scholar
- 4.Kennedy, J., Eberhart, R.: Particle swarm optimization, vol. 4, pp. 1942–1948 (1995)Google Scholar
- 5.Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 2, pp. 1671–1676 (2002)Google Scholar
- 6.Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
- 7.Krink, T., Vesterstrom, J., Riget, J.: Particle swarm optimisation with spatial particle extension. In: Proceedings of the World on Congress on Computational Intelligence, vol. 2, pp. 1474–1479 (2002)Google Scholar
- 9.Oliveira, M., Bastos-Filho, C.J.A., Menezes, R.: Towards a network-based approach to analyze particle swarm optimizers. In: 2014 IEEE Symposium on Swarm Intelligence (SIS), pp. 1–8, December 2014Google Scholar
- 12.Olorunda, O., Engelbrecht, A.P.: Measuring exploration/exploitation in particle swarms using swarm diversity. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1128–1134. IEEE, June 2008Google Scholar
- 13.Pontes, M.R., Neto, F.B.L., Bastos-Filho, C.J.: Adaptive clan particle swarm optimization. In: 2011 IEEE Symposium on Swarm Intelligence (SIS), pp. 1–6. IEEE (2011)Google Scholar
- 15.Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the CEC 2010 special session and competition on large-scale global optimization. Technical report, University of Science and Technology of China (USTC), School of Computer Science and Technology, Nature Inspired Computation and Applications Laboratory (NICAL), ChinaGoogle Scholar
- 17.Zhang, Q.L., Li, X., Tran, Q.A.: A modified particle swarm optimization algorithm. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2993–2995, August 2005Google Scholar