Face Recognition Based on SVM Optimized by the Improved Bacterial Foraging Optimization Algorithm

2019 ◽  
Vol 33 (07) ◽  
pp. 1956007
Author(s):  
Zhigao Zeng ◽  
Lianghua Guan ◽  
Wenqiu Zhu ◽  
Jing Dong ◽  
Jun Li
Keyword(s):  
Face Recognition ◽  
Kernel Function ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Function Selection ◽  
Dispersal Probability ◽  
Bacterial Foraging Optimization Algorithm

Support vector machine (SVM) is always used for face recognition. However, kernel function selection (kernel selection and its parameters selection) is a key problem for SVMs, and it is difficult. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function. Bacterial foraging optimization algorithm, inspired by the social foraging behavior of Escherichia coli, has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Therefore, we proposed to optimize the parameters in SVM by an improved bacterial foraging optimization algorithm (IBFOA). In the improved version of bacterial foraging optimization algorithm, a dynamical elimination-dispersal probability in the elimination-dispersal step and a dynamical step size in the chemotactic step are used to improve the performance of bacterial foraging optimization algorithm. Then the optimized SVM is used for face recognition. Simultaneously, an improved local binary pattern is proposed to extract features of face images in this paper to improve the accuracy rate of face recognition. Numerical results show the advantage of our algorithm over a range of existing algorithms.

Support Vector Machine Optimized Using the Improved Fish Swarm Optimization Algorithm and Its Application to Face Recognition

2019 ◽  
Vol 33 (14) ◽  
pp. 1956010
Author(s):  
Wenqiu Zhu ◽  
Haixing Bao ◽  
Zhigao Zeng ◽  
Zhiqiang Wen ◽  
Yanhui Zhu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Kernel Function ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Step Size ◽  
Swarm Optimization ◽  
Face Images ◽  
Visual Distance ◽  
Artificial Fish Swarm

Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.

scholarly journals An Hour Ahead Electricity Price Forecasting with Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm

2018 ◽  
Vol 10 (2) ◽  
pp. 748
Author(s):  
Intan Azmira Wan Abdul Razak ◽  
Izham Zainal Abidin ◽  
Yap Keem Siah ◽  
Aidil Azwin Zainul Abidin ◽  
Titik Khawa Abdul Rahman ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Optimization Algorithm ◽  
Forecast Accuracy ◽  
Electricity Price ◽  
Least Square ◽  
Support Vector ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Optimization Algorithm

<span lang="EN-US">Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.</span>

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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