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 Cooperative Bacterial Foraging Optimization

2009 ◽  
Vol 2009 ◽  
pp. 1-17 ◽  
Author(s):  
Hanning Chen ◽  
Yunlong Zhu ◽  
Kunyuan Hu
Keyword(s):  
Marked Improvement ◽  
Optimization Problems ◽  
Social Foraging ◽  
Decomposition Level ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Space Decomposition ◽  
Bacterial Foraging ◽  
The Social ◽  
Complex Optimization

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior ofE. colibacteria. This paper presents a variation on the original BFO algorithm, namely, the Cooperative Bacterial Foraging Optimization (CBFO), which significantly improve the original BFO in solving complex optimization problems. This significant improvement is achieved by applying two cooperative approaches to the original BFO, namely, the serial heterogeneous cooperation on the implicit space decomposition level and the serial heterogeneous cooperation on the hybrid space decomposition level. The experiments compare the performance of two CBFO variants with the original BFO, the standard PSO and a real-coded GA on four widely used benchmark functions. The new method shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

scholarly journals Bacterial Foraging-Tabu Search Metaheuristics for Identification of Nonlinear Friction Model

2012 ◽  
Vol 2012 ◽  
pp. 1-23 ◽  
Author(s):  
Nuapett Sarasiri ◽  
Kittiwong Suthamno ◽  
Sarawut Sujitjorn
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Optimization Problems ◽  
Identification Problem ◽  
Friction Model ◽  
Model Parameters ◽  
Experimental Setup ◽  
Nonlinear Friction ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging

This paper proposes new metaheuristic algorithms for an identification problem of nonlinear friction model. The proposed cooperative algorithms are formed from the bacterial foraging optimization (BFO) algorithm and the tabu search (TS). The paper reports the search comparison studies of the BFO, the TS, the genetic algorithm (GA), and the proposed metaheuristics. Search performances are assessed by using surface optimization problems. The proposed algorithms show superiority among them. A real-world identification problem of the Stribeck friction model parameters is presented. Experimental setup and results are elaborated.

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

2021 ◽  
pp. 148-180
Author(s):  
Rajashree Mishra ◽  
Kedar Nath Das
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Function Optimization ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Application Problem ◽  
Active Interest ◽  
Nature Inspired Algorithms

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

Optimal Placement of Distributed Generation Using Bacterial Foraging Optimization Algorithm

2015 ◽  
Vol 781 ◽  
pp. 329-332
Author(s):  
Parichart Sodsri ◽  
Bongkoj Sookananta ◽  
Mongkol Pusayatanont
Keyword(s):  
Distributed Generation ◽  
Optimization Algorithm ◽  
Distribution System ◽  
Optimal Placement ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Bacterial Foraging ◽  
Bus Voltage ◽  
Bacterial Foraging Optimization Algorithm

This paper presents the determination of the optimal distributed generation (DG) placement using bacterial foraging optimization algorithm (BFOA). The BFO mimics the seeking-nutrient behavior of the E. coli bacteria. It is utilized here to find the location and size of the DG installation in radial distribution system in order to obtain minimum system losses. The operation constraints include bus voltage limits, distribution line thermal limits, system power balance and generation power limits. The algorithm is tested on the IEEE 33 bus system. The result shows that the algorithm could be used as an alternative to the other techniques and improvement of the algorithm is required for acceleration and better accuracy of the calculation.

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.

scholarly journals Bacterial foraging optimization algorithm with mutation to solve constrained problems

2019 ◽  
Vol 29 ◽  
pp. 1-16
Author(s):  
Betania Hernández-Ocaña ◽  
José Hernández-Torruco ◽  
Oscar Chávez-Bosquez ◽  
Juana Canul-Reich ◽  
Luis Gerardo Montané-Jiménez
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Mutation Operator ◽  
Bacterial Foraging Optimization ◽  
Previous Version ◽  
Bacterial Foraging ◽  
Constrained Problems ◽  
Swarm Intelligence Algorithm ◽  
Bacterial Foraging Optimization Algorithm

A simple version of a Swarm Intelligence algorithm called bacterial foraging optimization algorithm with mutation and dynamic stepsize (BFOAM-DS) is proposed. The bacterial foraging algorithm has the ability to explore and exploit the search space through its chemotactic operator. However, premature convergence is a disadvantage. This proposal uses a mutation operator in a swim, similar to evolutionary algorithms, combined with a dynamic stepsize operator to improve its performance and allows a better balance between the exploration and exploitation of the search space. BFOAM-DS was tested in three well-known engineering design optimization problems. Results were analyzed with basic statistics and common measures for nature-inspired constrained optimization problems to evaluate the behavior of the swim with a mutation operator and the dynamic stepsize operator. Results were compared against a previous version of the proposed algorithm to conclude that BFOAM-DS is competitive and better than a previous version of the algorithm.

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