Adaptive Structure-Redesigned-Based Bacterial Foraging Optimization

Tan, Lijing; Yi, Wenjie; Yang, Chen; Feng, Yuanyue

doi:10.1007/978-3-319-42294-7_80

Cited by 4 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To exam the performance of ARBFO, this article choose ASRBFO [ 21 ], BFOLDC [ 22 ] and BFONDC [ 23 ] to conduct the simulated contrast experiment, and record the minimum value, maximum value, average value and standard deviation. Each algorithm independently runs 10 times.…”

Section: Adaptive Bfo Based On Roulette Strategymentioning

confidence: 99%

Adaptive Bacterial Foraging Optimization Based on Roulette Strategy

Cao

Tan

Huang

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Bacterial foraging optimization has drawn great attention and has been applied widely in various fields. However, BFO performs poorly in convergence when coping with more complex optimization problems, especially multimodal and high dimensional tasks. Aiming to address these issues, we therefore seek to propose a hybrid strategy to improve the BFO algorithm in each stage of the bacteria’s’ foraging behavior. Firstly, a non-linear descending strategy of step size is adopted in the process of flipping, where a larger step size is given to the particle at the very beginning of the iteration, promoting the rapid convergence of the algorithm while later on a smaller step size is given, helping enhance the particles’ global search ability. Secondly, an adaptive adjustment strategy of particle aggregation is introduced when calculating step size of the bacteria’s swimming behavior. In this way, the particles will adjust the step size according to the degree of crowding to achieve efficient swimming. Thirdly, a roulette strategy is applied to enable the excellent particles to enjoy higher replication probability in the replication step. A linear descent elimination strategy is adopted finally in the elimination process. The experimental results demonstrate that the improved algorithm performs well in both single-peak function and multi-peak function, having strong convergence ability and search ability.

show abstract

Section: Adaptive Bfo Based On Roulette Strategymentioning

confidence: 99%

Adaptive Bacterial Foraging Optimization Based on Roulette Strategy

Cao

Tan

Huang

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Niu et al [5] proposed nonlinearly decreasing and exponentially nonlinearly decreasing step sizes by verifying the effect of step size on global convergence. Tan et al [6] calculated the current bacterial convergence step size based on the current optimal position of bacteria, the historical optimal position and the average position of all bacteria in the population. Zhen L et al [7] proposed in an improved bacterial foraging optimization algorithm based on nonlinearly decreasing cosine adaptive step size.…”

Section: Introductionmentioning

confidence: 99%

Bacterial Foraging Algorithm With Potential Field Guidance Mechanism

Wang

2021

Preprint

View full text Add to dashboard Cite

To address the problems of weak quorum sensing ability and slow convergence speed in bacterial foraging algorithm, a bacterial foraging algorithm with potential field guidance mechanism is proposed. The algorithm combines the sampling guidance mechanism in the artificial potential field algorithm to provide the optimization direction for each bacterium; The original swimming operation of bacterial foraging algorithm is used to realize the local optimization strategy, and the local dimension update is added after swimming, so that the search range of bacteria in chemotaxis operation is wider; In the elimination and dispersal operation of bacterial foraging algorithm, double Gaussian function is introduced to re initialize the location of bacteria, so as to better avoid the algorithm falling into local extremum and improve the optimization ability of the algorithm. The experimental results show that the improved bacterial foraging algorithm has better optimization ability than the basic bacterial foraging algorithm.

show abstract