An Effective Bacterial Foraging Optimization Based on Conjugation and Novel Step-Size Strategies

2020 ◽  
pp. 362-374
Author(s):  
Ming Chen ◽  
Yikun Ou ◽  
Xiaojun Qiu ◽  
Hong Wang
Keyword(s):  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging

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.

A new dynamic bacterial foraging optimization and its application on model reduction

2015 ◽  
Vol 06 (02) ◽  
pp. 1550018
Author(s):  
Shenli Wu ◽  
Sun'an Wang ◽  
Xiaohu Li
Keyword(s):  
Model Reduction ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Poor Performance ◽  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Bacterial Populations ◽  
E Coli ◽  
Bacterial Foraging ◽  
Complex Optimization

Inspired by the foraging behavior of E. coli bacteria, bacterial foraging optimization (BFO) has emerged as a powerful technique for solving optimization problems. However, BFO shows poor performance on complex and high-dimensional optimization problems. In order to improve the performance of BFO, a new dynamic bacterial foraging optimization based on clonal selection (DBFO-CS) is proposed. Instead of fixed step size in the chemotaxis operator, a new piecewise strategy adjusts the step size dynamically by regulatory factor in order to balance between exploration and exploitation during optimization process, which can improve convergence speed. Furthermore, reproduction operator based on clonal selection can add excellent genes to bacterial populations in order to improve bacterial natural selection and help good individuals to be protected, which can enhance convergence precision. Then, a set of benchmark functions have been used to test the proposed algorithm. The results show that DBFO-CS offers significant improvements than BFO on convergence, accuracy and robustness. A complex optimization problem of model reduction on stable and unstable linear systems based on DBFO-CS is presented. Results show that the proposed algorithm can efficiently approximate the systems.

scholarly journals Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Huang Chen ◽  
Lide Wang ◽  
Jun Di ◽  
Shen Ping
Keyword(s):  
Information Exchange ◽  
Global Optimum ◽  
The Self ◽  
Local Optimum ◽  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Wide Range ◽  
Swarm Intelligence Optimization ◽  
Self Adaptive

Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.

Economic Load Dispatch Using Bacterial Foraging Optimization Algorithm Based on Evolution Strategies

2013 ◽  
Vol 860-863 ◽  
pp. 2040-2045 ◽  
Author(s):  
Xiao Hua Feng ◽  
Yu Yao He ◽  
Juan Yu
Keyword(s):  
Search Space ◽  
Complex Problem ◽  
Evolution Strategies ◽  
Power Losses ◽  
Bacterial Foraging Optimization ◽  
Thermal Generation ◽  
Bacterial Foraging ◽  
Global And Local ◽  
Generation Test ◽  
Bacterial Foraging Optimization Algorithm

This paper presents a novel modified bacterial foraging optimization(BFO) to solve economic loaddispatch (ELD) problems. BFO isalready successfully employed to solve variousoptimization problems. However original BFOfor small problems with moderate dimensionand searching space is satisfactory. As searchspace and complexity growexponentially in scalable ELD problems, it shows poorconvergence properties. To tackle this complex problem considering itshigh-dimensioned search space, the Evolution Strategies is introduced to thebasic BFO. The chemotactic step is adjusted to have a dynamic non-linearbehavior in order to improve balancing the global and local search. Theproposed algorithm is validated using several thermal generation test systems.The results are compared with those obtained by other algorithms previouslyapplied to solve the problem considering valve-point effects and power losses.

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