A Novel BFO Optimization Algorithm with Neighborhood Learning

Abstract:

Bacterial foraging optimization is a relatively new bio-inspired swarm intelligence algorithm and has been successfully applied to solve many real-world problems. However, similar to other swarm intelligence algorithms, BFO also faces up to some challenging problems, such as low convergence speed and easily to be trapped into local minima. To deal with these issues, we incorporate the concept of neighbor topology and the idea of neighbor learning to improve the performance of BFO, called bacterial foraging optimization with neighborhood learning (BFO-NL). Simulation results demonstrated the good performance of our proposed BFO-NL when compared with original BFO.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 1888-1891
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.1888
• Online since: March 2014
• Authors: Li Jing Tan*, Fu Yong Lin, Ben Niu, Qi Qi Duan, Kai Yin
• Keywords: BFO Optimization, Neighborhood Learning, Swarm Intelligence

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