A Performance Study of Chemo-Inspired Genetic Algorithm on Benchmark Functions
In solving non-linear optimization problems, Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from foraging behavior of E. Coli bacterium. In the other hand, Genetic algorithm (GA) has attracted increased attention from the academic and industrial communities to deal with such problems. In recent literature, it is discovered that the hybrid techniques provides the better solution with faster convergence. In this paper, a novel approach of hybridization is presented. The Chemotactic step (from BFO) is only hybridized with GA, namely CGA. The better performance of the proposed CGA than Quadratic Approximation hybridized GA, is experimentally verified through a set of 22 benchmark problems taken from recent literature.
KeywordsGenetic algorithm Quadratic approximation Bacterial foraging optimization Hybridization Benchmark problems
Unable to display preview. Download preview PDF.
- Fan, Shu-Kai., Liang, Y.C., Zahara, E. : A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search, Computers and Industrial Engineering 50, 401–425 (2006).Google Scholar
- Hwang, Shun-Fa., He, Rong Song. : A hybrid real parameter genetic algorithm for function optimization, Advance Engineering Informatics 20, 7–21(2006).Google Scholar
- Zhang, G., Lu, H. : Hybrid real coded genetic algorithm with quasi-simplex technique, International Journal of Computer Science and Network Security 6(10) 246–255(2006).Google Scholar
- Wei, L., Zhao, M. : A nitche hybrid genetic algorithm for global optimization of continuous multi modal functions, Applied Mathematics and Computations 160,649–661 (2005).Google Scholar
- Chen, T.C.,Tsai, P.W., Chu, S.C., Pan, J.S.:A novel optimization approach- bacterial GA foraging, IEEE, Proceedings of Second International Conference on Innovative Computing, Information and Control, ICICIC’07,PP.31-1391,(2007).Google Scholar
- Dong, Hwa.Kim., Abraham, Dong. Hwa.Ajith., Cho, Jae. Hoon. : A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization, Information Sciences,vol.177,no.18,pp.3918-3937,(2007).Google Scholar
- Chen, Yanhai., Lin,Weixing. : An Improved Bacterial Foraging Optimization, Proceedings of the IEEE International Conference on Robotics and Biomimetics, December 19-23,Guilin,China. (2009).Google Scholar
- Sastry, V.R.S.Gollapudia., Shyam, S.Pattnaika., Bajpaib,O.P., Devi,Swana., Bakwada, K.M. : Velocity Modulated Bacterial Foraging Optimization Technique (VMBFO), Applied Soft Computing, www.elsevier.com.
- Chen, Hanning., Zhu, Yunlong., Hu, Kunyuan.:Research Article, Adaptive Bacterial Foraging Optimization, Hindawi Publishing Corporation, Abstract and Applied Analysis, Volume 2011, Article ID 108269,27 pages doi: 10.1155/2011/10826.
- Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Systems Magazine (2002) 52-67, doi: 10.1109/MCS.2002.1004010.
- Long, Liu Xiao., Jun, Li. Rong., Ping, Yang., : A Bacterial Foraging Optimization Algorithm Based On the Particle Swarm Optimization, IEEE proceedings of International Conference on Intelligent Computing and Intelligent System held in SCUT Guangzhou, China, vol.2, pg.22-27 (2010).Google Scholar
- Biswas, Arijit., Dasgupta., Sambarta., Das, Swagatam., Abraham, Ajith.: A synergy of PSO and Bacterial Foraging Optimization- A Comparative Study on Numerical Benchmarks, in proceedings of the 2nd international symposium on Hybrid Artificial Intelligent System(HAIS), Advances Soft Computing, vol.44, pp.253-263,(2007).Google Scholar
- Biswas, Arijit., Dasgupta, Sambarta., Das, Swagatam., Abraham, Ajith.,: A synergy of differential evolution and bacterial Foraging optimization for global optimization, International Journal on Neural and Mass parallel computing and Information Systems, Neural Network world, vol.17, No.6, pp.607-626, (2007).Google Scholar
- Biswas, Arijit., Dasgupta, Sambarta., Das, Swagatam., Abraham, Ajith.,: A synergy of PSO and bacterial Foraging optimization-A Comparative Study on Numerical Benchmarks: Innovations in Hybrid Intelligent Systems, ASC 44, springerlink.com, pp.255-263, (2007).Google Scholar
- Deep, K., Das, Kedar. Nath. : Quadratic approximation based Hybrid Genetic Algorithm for Function Optimization, AMC, Elsevier, Vol. 203, pp. 86-98, (2008).Google Scholar