Coupling of Optimization Algorithms Based on Swarm Intelligence

2018 ◽  
pp. 27-50 ◽  
Author(s):  
Kamalanand Krishnamurthy ◽  
Mannar Jawahar Ponnuswamy
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Computational Intelligence ◽  
Biomedical Engineering ◽  
Optimization Algorithms ◽  
Bacterial Foraging Optimization ◽  
Complex Problems ◽  
Bacterial Foraging

Swarm intelligence is a branch of computational intelligence where algorithms are developed based on the biological examples of swarming and flocking phenomena of social organisms such as a flock of birds. Such algorithms have been widely utilized for solving computationally complex problems in fields of biomedical engineering and sociology. In this chapter, two different swarm intelligence algorithms, namely the jumping frogs optimization (JFO) and bacterial foraging optimization (BFO), are explained in detail. Further, a synergetic algorithm, namely the coupled bacterial foraging/jumping frogs optimization algorithm (BFJFO), is described and utilized as a tool for control of the heroin epidemic problem.

A Novel BFO Optimization Algorithm with Neighborhood Learning

2014 ◽  
Vol 543-547 ◽  
pp. 1888-1891
Author(s):  
Li Jing Tan ◽  
Fu Yong Lin ◽  
Ben Niu ◽  
Qi Qi Duan ◽  
Kai Yin
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Real World ◽  
Convergence Speed ◽  
Local Minima ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Simulation Results ◽  
Swarm Intelligence Algorithm ◽  
Real World Problems

Bacterial foraging optimization is a relatively new bio-inspired swarm intelligence algorithm and has been successfully applied to solve many real-world problems. However, similar to other swarm intelligence algorithms, BFO also faces up to some challenging problems, such as low convergence speed and easily to be trapped into local minima. To deal with these issues, we incorporate the concept of neighbor topology and the idea of neighbor learning to improve the performance of BFO, called bacterial foraging optimization with neighborhood learning (BFO-NL). Simulation results demonstrated the good performance of our proposed BFO-NL when compared with original BFO.

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.

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

2016 ◽  
Vol 10 (1) ◽  
pp. 45-65 ◽  
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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