科研之友
微信
新浪微博
Facebook
分享链接
摘要
Bacterial Foraging Optimization (BFO) is a new optimization algorithm based on the social foraging behavior of E.coli bacteria. However, the original BFO algorithm possesses a poor convergence behavior and search efficiency compared to the other successful nature-inspired algorithms. To improve the performance of BFO algorithm on complex optimization problems, an improved BFO algorithm is proposed by incorporating fuzzy interface system called Fuzzy Bacterial Foraging Optimization (FBFO), in which the next chemotactic step size is adaptively adjusted in term of the present step size and the cost function value, it can efficiently balance the global exploration and local exploitation. Finally, computer simulations over four numerical benchmarks indicate that the proposed algorithm shows highly competitive for its better general convergence performance, as compared to its classical counterparts.
