Synergy of PSO and Bacterial Foraging Optimization — A Comparative Study on Numerical Benchmarks
Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. Until now, very little research work has been undertaken to improve the convergence speed and accuracy of the basic BFOA over multi-modal fitness landscapes. This article comes up with a hybrid approach involving Particle Swarm Optimization (PSO) and BFOA algorithm for optimizing multi-modal and high dimensional functions. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm, the classical g_best PSO algorithm and a state of the art version of the PSO. The new method is shown to be statistically significantly better on a five-function test-bed and one difficult engineering optimization problem of spread spectrum radar poly-phase code design.
KeywordsBacterial Foraging hybrid optimization particle swarm optimization Radar poly-phase code design
Unable to display preview. Download preview PDF.
- 1.Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization and Control, IEEE Control Systems Magazine, 52–67, (2002).Google Scholar
- 3.Tripathy, M., Mishra, S., Lai, L.L. and Zhang, Q.P.: Transmission Loss Reduction Based on FACTS and Bacteria Foraging Algorithm. PPSN, 222–231, (2006).Google Scholar
- 4.Kim, D.H., Cho, C. H.: Bacterial Foraging Based Neural Network Fuzzy Learning. IICAI 2005, 2030–2036.Google Scholar
- 5.Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Harbor (1975).Google Scholar
- 6.Kennedy, J, Eberhart, R.: Particle swarm optimization, In Proceedings of IEEE International Conference on Neural Networks, (1995) 1942–1948.Google Scholar
- 10.Stephens, D.W., Krebs, J.R., Foraging Theory, Princeton University Press, Princeton, New Jersey, (1986).Google Scholar
- 12.Angeline, P. J.: Evolutionary optimization versus particle swarm optimization: Philosophy and the performance difference, Lecture Notes in Computer Science (vol. 1447), Proceedings of 7th International Conference on. Evolutionary Programming-Evolutionary Programming VII (1998) 84–89.Google Scholar