A Modified Bacterial Foraging Optimization Algorithm for Global Optimization

Yan, Xiaohui; Zhang, Zhicong; Guo, Jianwen; Li, Shuai; Zhao, Shu

doi:10.1007/978-3-319-42291-6_62

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…Accordingly, the WCA was used as a comparison algorithm. A modified version of the BFO algorithm, MBFO, was also employed due to the introduction of chemotaxis in BFO 32 . In addition, the classical PSO and DE algorithms were also used for comparison.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In the HCO algorithm, the value of

{\max }{NF}

increased from 1 to 3, and the value of

{P}_{e}

decreased from 0.9 to 0.1 linearly as the algorithm proceeded. In the WCA algorithm,

{N}_{{sr}}=4

{d}_{{\max }}=1e-3.

16 In the MBFO algorithm,

{N}_{c}=50,{{N}}_{s}=4,{N}_{{re}}=4,{N}_{{ed}}=10,{{P}}_{e}=0.25,\lambda =1.04.

32 In the PSO algorithm,

\omega

decreased linearly from 0.9 to 0.4.

C1=C2=2.0

.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…A modified version of the BFO algorithm, MBFO, was also employed due to the introduction of chemotaxis in BFO. 32 In addition, the classical PSO and DE algorithms were also used for comparison. When the HCO algorithm is mentioned below, it refers to the adaptive version.…”

Section: Comparison Of Tests With Other Algorithms On Benchmark Funct...mentioning

confidence: 99%

See 2 more Smart Citations

An adaptive hydrologic cycle optimization algorithm for numerical optimization and data clustering

Yan

Niu

Chai

et al. 2022

Int J of Intelligent Sys

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The circulation and convergence of water in the hydrologic cycle process inspired us to design a new optimization algorithm, the Hydrologic Cycle Optimization (HCO) algorithm. In this study, a comprehensive demonstration of the HCO was presented. First, a simplified model of the hydrological cycle phenomenon was established. Then, the framework of HCO and its operators were discussed and verified in detail. Several experiments were done to test the optimization ability of the HCO algorithm. In the first experiment, the parameter settings were tested, and an adaptive version of the algorithm was proposed. Then the HCO was tested on 20 numeric optimization benchmark functions and eight data clustering sets, respectively, and compared with other algorithms. The experimental results showed that the HCO is superior to the compared algorithms, indicating that it is a competitive approach for numerical and engineering optimization problems.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…In the HCO algorithm, the value of

{\max }{NF}

increased from 1 to 3, and the value of

{P}_{e}

decreased from 0.9 to 0.1 linearly as the algorithm proceeded. In the WCA algorithm,

{N}_{{sr}}=4

{d}_{{\max }}=1e-3.

16 In the MBFO algorithm,

{N}_{c}=50,{{N}}_{s}=4,{N}_{{re}}=4,{N}_{{ed}}=10,{{P}}_{e}=0.25,\lambda =1.04.

32 In the PSO algorithm,

\omega

decreased linearly from 0.9 to 0.4.

C1=C2=2.0

.…”

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive hydrologic cycle optimization algorithm for numerical optimization and data clustering

Yan

Niu

Chai

et al. 2022

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the KM-MBFO algorithm, we compared it to other algorithms [21] (e.g., standard K-means, KM-HMR, BFO,PSO). Here we simulated the data set in [22] for performance analysis.…”

Section: Implementation and Analysis And Resultsmentioning

confidence: 99%

KM-MBFO: A Hybrid Hadoop Map Reduce Access for Clustering Big Data by Adopting Modified Bacterial Foraging Optimization Algorithm

2019

IJRTE

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K-Means Clustering is a very powerful and frequently used algorithm for the clustering, it has got its own limitation. The prevalent K-Means clustering algorithm used for grouping have inadequacies, for example, slow convergence rate, local optima trap, and so on. Therefore, many swarm knowledge based procedures combined with KM for clustering were presented and demonstrated their presentation, its variations and its applications in data grouping. In this paper we intend to propose a parallel organizing strategy for KM-MBFO mechanism that actualized in Hadoop Distributed File System (HDFS) for diminishing the execution time. This Mapper approach produces the populace for given data set for grouping. The Modified Bacterial Foraging Optimization (MBFO) algorithm finds the wellness of the populace to choose the optimal K values as far as execution time and classification error. Through simulated test results, we assess the demonstration of the proposed KM-BFO conspire

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“…Secondly, the parameter optimization problem has not been discussed because the design of most algorithms adopted a multilayer loop nesting structure. (17) Besides, few groups have compared or comprehensively evaluated the improved BFO algorithms on a variety of complex functions. To solve these problems, we have carried out a more detailed study.…”

Section: Introductionmentioning

confidence: 99%

Robust Bacterial Foraging Algorithms Based on Few-excellent-individuals Guidance Strategy

Gao¹,

Yu²,

Dai³

et al. 2020

Sensors and Materials

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Keywords: bacterial foraging optimization, 80/20 rule, constriction factor PSO, gradient migration probability, robustness In recent years, the novel bacterial foraging optimization has been widely applied. However, in past studies, the process of bacterial foraging lacked guidance and the structure of the algorithm was inadequate, which resulted in a low convergence speed and a large number of parameters in the algorithm, thus reducing its search accuracy and speed. Additionally, researchers only improved the algorithm for complex situations, for which a comprehensive evaluation of its robustness could not be made. Here, to resolve these issues, two improved algorithms are proposed and compared comprehensively. Our algorithms are suitable for modeling the foraging process of organisms in nature: a small number of individuals with rich resources can attract other individuals to forage locally. First, we propose a decreasing composite function and gradient migration behavior and introduce the 80/20 rule. A few excellent individuals guide the population to migrate to the optimal solution and increase the convergence speed. Second, we introduce the renewal speed of particles and propose another composite function, and the biological characteristics of Escherichia coli are also introduced to achieve the screening of excellent individuals. Finally, we show the results of numerous experiments and comprehensively evaluate the applicability of the proposed organisms.

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A Modified Bacterial Foraging Optimization Algorithm for Global Optimization

Cited by 4 publications

References 12 publications

An adaptive hydrologic cycle optimization algorithm for numerical optimization and data clustering

An adaptive hydrologic cycle optimization algorithm for numerical optimization and data clustering

KM-MBFO: A Hybrid Hadoop Map Reduce Access for Clustering Big Data by Adopting Modified Bacterial Foraging Optimization Algorithm

Robust Bacterial Foraging Algorithms Based on Few-excellent-individuals Guidance Strategy

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