Abstract
Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.
Highlights
Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm based on the foraging behavior of E
Erefore, the SCBFO algorithm can effectively solve the performance degradation caused by the drawbacks of the classical BFO and improve the search performance stability
The convergence trend and typical data such as the best value, the worst value, the mean value, and the variance can be achieved from the computed search results, which will be used in the following analysis on the performance of the SCBFO algorithm
Summary
Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm based on the foraging behavior of E. Compared with other optimization algorithms, the BFO algorithm aces in fast convergence and global search by its simple bacterial individual structure and behavior, varied group types and characteristics, and efficient life cycle [3,4,5] even though it is still in a preliminary stage of research. Erefore, the BFO algorithm has been successfully applied in many fields. Literature in [6,7,8] adopted the BFO algorithm to optimize the probabilistic planning, load dispatch, reconstruction, and loss minimization of the power energy network. Research in [12, 13] applied the BFO algorithm to the optimization of the wireless network, including the structure design and routing topology
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