A novel bacterial foraging optimization algorithm for feature selection

Chen, Yupeng; Li, Ying; Wang, Gang; Zheng, Yang; Xu, Qian; Fan, Jiahao; Cui, Xueting

doi:10.1016/j.eswa.2017.04.019

Cited by 107 publications

(41 citation statements)

References 30 publications

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“…In [18] Yu-Peng Chen et al have proposed the two novel variants of Bacterial foraging optimization (BFO) algorithms viz. Adaptive Chemotaxis Bacterial Foraging Optimization Algorithm (ACBFO) and Improved Swarming and Elimination-Dispersal Bacterial Foraging Optimization Algorithm (ISEDBFO).The proposed algorithms have been tested on 10 different benchmarks datasets and performance has been compared with PSO, GA, SA, ALO, BBA, CS.…”

Section: The Current State-of-the-art In Metaheuristic Algorithmsmentioning

confidence: 99%

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

IJIMAI

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Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.

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Section: The Current State-of-the-art In Metaheuristic Algorithmsmentioning

confidence: 99%

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

IJIMAI

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“…BFOA have been already accepted as an optimization algorithm and its efficiency has been demonstrated in several areas. For instance, its application in the electric engineering a control field [21], pattern recognition [22], PID design [23], schedule programming [24], as well as the development of applications to solve simultaneous equations [25] has even been used for the selection of characteristics in problems of dimensionality reduction of data sets [26]. In addition, BFOA has also been applied hybridly together with other methods, for example, with the Ant Colony algorithm for a scheduling problem [27] and in conjunction with the Particle Swarm Optimization algorithm for optimal design of power system stabilizers [28], and in power generation problems [29].…”

Section: Bacterial Foraging Optimization Algorithmmentioning

confidence: 99%

“…BFOA implements an imitation of the main mechanisms present in an actual bacteria E, Coli colony: chemotaxis, formation of the colony or swarming, reproduction and elimination-dispersion events, with which the problem of optimization without gradient can be solved. The way to explain what a virtual bacterium represents is that a bacterium is a test solution that moves on the functional surface to locate the global optimum [26].…”

Section: Model Optimizationmentioning

confidence: 99%

Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

Cabrera-Hernandez¹,

Aceves-Fernández²,

Ramos-Arreguín³

et al. 2019

IJIS

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The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters. parameters individually influences said results.The methodology is basically to use BFOA as optimizer of a base model generated with another technique, ANFIS. The model generated with ANFIS presents some inaccuracies since it is unstable with highly non-linear problems such as the one that is to be modeled in this work. This is why this method was devised where the accuracy of the base model is improved. Once the model optimized with BFOA is generated, it will be compared against that generated with ANFIS.The use of an algorithm that has several agents or swarm intelligence, such as BFOA, gives us the opportunity to find an optimal solution since it involves several, relatively simple agents exploring the study area, thus having a greater probability of finding the optimal global values avoiding the problem of getting stuck in a local solution as it happens with other methods such as neural networks, as well as being robust, flexible systems without central control that issues orders to system agents [1].To better understand what the problem is, it is important to define some concepts, which are presented below.

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“…The BFO is figured dependent on the microscopic organisms' natural searching conduct. This calculation proposed goes for driving bacterial searching utilizing a most extreme supplement fixation through chemotaxis, proliferation, swarming and end dispersal [3]. This denotes the bacterial tracks to be wanted edges.…”

Section: B Bacterial Foraging Optimization (Bfo) Algorithmmentioning

confidence: 99%

Hybrid Bacterial Foraging Optimization – Tabu Search for Medical Image Classification

Chitra¹,

Pugazhendi²

2019

IJITEE

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Delicate registering is presented in the preparing of restorative picture as is has a viable way to deal with the treatment of vulnerabilities that are inborn in the procured picture information. It has space that is high-dimensional which can debase the picture characterization execution principally as far as its exactness for structure an order display as it contains certain excess and unimportant highlights. Accordingly, include choice has been utilized for the decrease of the element number that is separated from pictures by methods for evacuating any excess or insignificant highlights. The meta-heuristics have been creating for a significant drawn-out period of time and are accepting an imperative employment in the fields of the administrators and money related issues. Among all the present computations, TABU chase (TS) has been appearing couple of productive applications to issues of this present reality that is demonstrate by innumerable all the world over. Picture enlistment or picture course of action is a fundamental development in remedial picture examination when various pictures are incorporated. In by far most of such applications, the selection is performed following the power based strategy, which changes IR into a complex, computationally expensive, steady progression issue

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A novel bacterial foraging optimization algorithm for feature selection

Cited by 107 publications

References 30 publications

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

Hybrid Bacterial Foraging Optimization – Tabu Search for Medical Image Classification

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