Bacterial Foraging Algorithm with Adaptable Chemotactic Step Size

2010 ◽  
Author(s):  
H. Supriyono ◽  
M.O. Tokhi
Keyword(s):  
Step Size ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

Self-adaptive bacterial foraging algorithm based on estimation of distribution

2020 ◽  
pp. 1-13
Author(s):  
Na Ni ◽  
Yuanguo Zhu
Keyword(s):  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Step Size ◽  
Bacterial Foraging ◽  
Fitness Value ◽  
New Locations ◽  
Estimation Of Distribution ◽  
Local Optimal Solution ◽  
Bacterial Foraging Algorithm ◽  
Self Adaptive

Bacteria foraging optimization (BFO) algorithm is easy to fall into the local optimal solution and slow in convergence. In this paper, we have come up with a self-adaptive bacterial foraging algorithm based on estimation of distribution to overcome the mentioned shortages. First, in the chemotactic operator, the swimming step size of bacterium is adaptively adjusted by its fitness value and bacteria move in a random direction. Second, the bacteria obtain the probability of replication based on the fitness value. We choose half of the population for replication by the roulette wheel method. Finally, the possibility of elimination-dispersal is adjusted by the fitness value. Selected bacteria are dispersed to the new locations produced by BOX-Muller formula. Compared with some relative heuristic algorithms on finding the optimal value of ten benchmark functions, the proposed algorithm shows higher convergence speed and accuracy.

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