Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

2021 ◽  
Author(s):  
Chen Guo ◽  
Heng Tang ◽  
Ben Niu
Keyword(s):  
Optimization Algorithm ◽  
Data Clustering ◽  
Bacterial Foraging Optimization ◽  
Multi Objective ◽  
Bacterial Foraging ◽  
Evolutionary State ◽  
Bacterial Foraging Optimization Algorithm

Bacterial Foraging Optimization Algorithm with Quorum Sensing Mechanism

2014 ◽  
Vol 556-562 ◽  
pp. 3844-3848
Author(s):  
Hai Shen ◽  
Mo Zhang
Keyword(s):  
Quorum Sensing ◽  
Optimization Algorithm ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Cell Communication ◽  
Bacterial Foraging Optimization ◽  
Sensing Mechanism ◽  
Bacterial Foraging ◽  
Bacterial Quorum ◽  
Bacterial Foraging Optimization Algorithm

Quorum sensing is widely distributed in bacteria and make bacteria are similar to complex adaptive systems, with intelligent features such as emerging and non-linear, the ultimate expression of the adaptive to changes in the environment. Based on the phenomenon of bacterial quorum sensing and Bacterial Foraging Optimization Algorithm, some new optimization algorithms have been proposed. In this paper, it presents research situations, such as environment-dependent quorum sensing mechanism, quorum sensing mechanism with quantum behavior, cell-to-cell communication, multi-colony communication, density perception mechanism. Areas of future emphasis and direction in development were also pointed out.

A Clustering Approach Using Fractional Calculus-Bacterial Foraging Optimization Algorithm for k-Anonymization in Privacy Preserving Data Mining

2019 ◽  
pp. 587-608
Author(s):  
Pawan R. Bhaladhare ◽  
Devesh C. Jinwala
Keyword(s):  
Data Mining ◽  
Fractional Calculus ◽  
Optimization Algorithm ◽  
Information Loss ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Individual Privacy ◽  
Clustering Approach ◽  
Bacterial Foraging Optimization Algorithm ◽  
Optimal Cluster

A tremendous amount of personal data of an individual is being collected and analyzed using data mining techniques. Such collected data, however, may also contain sensitive data about an individual. Thus, when analyzing such data, individual privacy can be breached. Therefore, to preserve individual privacy, one can find numerous approaches proposed for the same in the literature. One of the solutions proposed in the literature is k-anonymity which is used along with the clustering approach. During the investigation, the authors observed that the k-anonymization based clustering approaches all the times result in the loss of information. This paper presents a fractional calculus-based bacterial foraging optimization algorithm (FC-BFO) to generate an optimal cluster. In addition to this, the authors utilize the concept of fractional calculus (FC) in the chemotaxis step of a bacterial foraging optimization (BFO) algorithm. The main objective is to improve the optimization ability of the BFO algorithm. The authors also evaluate their proposed FC-BFO algorithm, empirically, focusing on information loss and execution time as a vital metric. The experimental evaluations show that our proposed FC-BFO algorithm generates an optimal cluster with lesser information loss as compared with the existing clustering approaches.

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