Improved Bacterial Foraging Optimization

• Main Authors: Kuo-Wei Lee, 李國維
• Other Authors: Prof. Yu-Cheng Kao
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/22851452298832117486

碩士 === 大同大學 === 資訊經營學系(所) === 101 === This paper proposes an improved approach involving bacterial foraging optimization algorithm (BFOA) behavior. The new algorithm is called improved bacterial foraging optimization (IBFO). BFOA is a new swarm intelligence technique. Three main BFOA operation are chemotaxis, reproduction and elimination-dispersal, which are applied to global and local random searches. This powerful and effective algorithm has been used to solve various real-world optimization problem. However , BFOA has several shortages: many parameters needed to be set ; tumble angles are generated randomly and a fixed chemotactic step size causing poor convergence. In this paper, we try to improve these shortages of BFOA base on reduce setting parameters. Finally, we compare the performance of IBFO with the classical BFOA, testing them on seven widely-used benchmark functions. The experimental result shows that the IBFO is very competitive and outperforms the BFOA.