Autosoft Journal

Online Manuscript Access

Binary-State Bacterial Foraging Optimization Based on Network Topology and its Application



Bacterial foraging optimization (BFO) inspired by the foraging behavior of E.coli has been used to solve optimization problems. This paper presents a novel binary-state bacterial foraging optimization based on network topology (BBFO-NT). In the proposed BBFO-NT, a binary-state bacterial foraging strategy, which makes the bacteria to have mutual learning mechanism, is introduced. The two behavioral states include an explorative state based on Von Neumann topology and an exploitative state based on small-world networks. The bacteria co-evolve during the optimization process under the two states. Experiments on a set of benchmark functions validate the effectiveness of the improved algorithm. BFO and some other intelligent optimization algorithms are employed for comparison. The simulations show that the proposed BBFO-NT offers significant improvements than BFO. On this basis, the new algorithm has been successfully applied to the docking control. The experiments indicate that the improved algorithm is effective in controller design.



Total Pages: 14
Pages: 271-284


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Volume: 23
Issue: 2
Year: 2016

Cite this document


Agrawal, S., S. Swain, and L. Dora. "BFO-ICA Based Multi Focus Image Fusion." 2013 IEEE Symposium on Swarm Intelligence (SIS) (2013): n. pag. Crossref. Web.

Barrat, A., and M. Weigt. "On the Properties of Small-World Network Models." The European Physical Journal B 13.3 (2000): 547-560. Crossref. Web.

Niu, Ben, Jingwen Wang, and Hong Wang. "Bacterial-Inspired Algorithms for Solving Constrained Optimization Problems." Neurocomputing 148 (2015): 54-62. Crossref. Web.

Niu, Ben, Jingwen Wang, and Hong Wang. "Bacterial-Inspired Algorithms for Solving Constrained Optimization Problems." Neurocomputing 148 (2015): 54-62. Crossref. Web.

Du H. Journal of Xi’an Jiaotong University

Georgiou, G., G. A. Vio, and J. E. Cooper. "Aeroelastic Tailoring and Scaling Using Bacterial Foraging Optimisation." Structural and Multidisciplinary Optimization 50.1 (2014): 81-99. Crossref. Web.

Gu Q.W. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

He, S et al. "Analysis of Premalignant Pancreatic Cancer Mass Spectrometry Data for Biomarker Selection Using a Group Search Optimizer." Transactions of the Institute of Measurement and Control 34.6 (2011): 668-676. Crossref. Web.

Barrat, A., and M. Weigt. "On the Properties of Small-World Network Models." The European Physical Journal B 13.3 (2000): 547-560. Crossref. Web.

Chen, Hanning et al. "Bacterial Colony Foraging Optimization." Neurocomputing 137 (2014): 268-284. Crossref. Web.

Jahjouh, M. M., M. H. Arafa, and M. A. Alqedra. "Artificial Bee Colony (ABC) Algorithm in the Design Optimization of RC Continuous Beams." Structural and Multidisciplinary Optimization 47.6 (2013): 963-979. Crossref. Web.

Kennedy, J., and R. Mendes. "Population Structure and Particle Swarm Performance." Proceedings of the 2002 Congress on Evolutionary Computation. CEC”02 (Cat. No.02TH8600) n. pag. Crossref. Web.

Karaboga, Dervis, and Bahriye Basturk. "A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm." Journal of Global Optimization 39.3 (2007): 459-471. Crossref. Web.

Lei X. Swarm intelligent optimization algorithm and their applications

Li, X.H. (2009). Research on key technologies of direct drive variable displacement electro-hydraulic position system for control . Xi’an: Xi’an Jiaotong University, 47–48.

Vaisakh, K. et al. "Solving Dynamic Economic Dispatch Problem with Security Constraints Using Bacterial Foraging PSO-DE Algorithm." International Journal of Electrical Power & Energy Systems 39.1 (2012): 56-67. Crossref. Web.

Liu J. Advanced PID control and MATLAB simulation

Mai, Xiong Fa, and Ling Li. "Bacterial Foraging Algorithm Based on PSO with Adaptive Inertia Weigh for Solving Nonlinear Equations Systems." Advanced Materials Research 655-657 (2013): 940-947. Crossref. Web.

McGill R. The American Statistician

Milgram S. Psychology Today

Nasir, A.N.K., and M.O. Tokhi. "A Novel Hybrid Bacteria-Chemotaxis Spiral-Dynamic Algorithm with Application to Modelling of Flexible Systems." Engineering Applications of Artificial Intelligence 33 (2014): 31-46. Crossref. Web.

Okaeme, Nnamdi A., and Pericle Zanchetta. "Hybrid Bacterial Foraging Optimization Strategy for Automated Experimental Control Design in Electrical Drives." IEEE Transactions on Industrial Informatics 9.2 (2013): 668-678. Crossref. Web.

Panda, Rutuparna, Manoj Kumar Naik, and Niranjan Mishra. "Design of Two-Dimensional Recursive Filters Using Bacteria Foraging Optimization." 2013 IEEE Symposium on Swarm Intelligence (SIS) (2013): n. pag. Crossref. Web.

"Biomimicry of Bacterial Foraging for Distributed Optimization and Control." IEEE Control Systems 22.3 (2002): 52-67. Crossref. Web.

Raja, N. Sri Madhava, and V. Rajinikanth. "Brownian Distribution Guided Bacterial Foraging Algorithm for Controller Design Problem." Advances in Intelligent Systems and Computing (2014): 141-148. Crossref. Web.

Tan, Lijing et al. "Bacterial Foraging Optimization with Neighborhood Learning for Dynamic Portfolio Selection." Lecture Notes in Computer Science (2014): 413-423. Crossref. Web.

Tang, Qirong, and Peter Eberhard. "A PSO-Based Algorithm Designed for a Swarm of Mobile Robots." Structural and Multidisciplinary Optimization 44.4 (2011): 483-498. Crossref. Web.

vandenBergh, F., and A.P. Engelbrecht. "A Cooperative Approach to Particle Swarm Optimization." IEEE Transactions on Evolutionary Computation 8.3 (2004): 225-239. Crossref. Web.

Wang, X.F. (2008). Research on dynamic topology of particle swarm algorithms . Chongqing: Southwest university, 54–55.

Wang P. Systems Engineering and Electronics

Wang X.F. Complex networks theory and application

Watts, Duncan J., and Steven H. Strogatz. "Collective Dynamics of ‘small-World” Networks." Nature 393.6684 (1998): 440-442. Crossref. Web.

Xu, Xin, and Hui-ling Chen. "Adaptive Computational Chemotaxis Based on Field in Bacterial Foraging Optimization." Soft Computing 18.4 (2013): 797-807. Crossref. Web.

YI, Jun. "Bacterial Foraging Optimization Algorithm Based on Variable Neighborhood for Job-Shop Scheduling Problem." Journal of Mechanical Engineering 48.12 (2012): 178. Crossref. Web.

Parpinelli, Rafael Stubs, and Heitor Silvério Lopes. "A Hierarchical Clustering Strategy to Improve the Biological Plausibility of an Ecology-Based Evolutionary Algorithm." Advances in Artificial Intelligence - IBERAMIA 2012 (2012): 310-319. Crossref. Web.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048