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.

The Improved of BFOA for Ensuring the Sustainable Economic Dispatch

2015 ◽  
Vol 785 ◽  
pp. 83-87 ◽  
Author(s):  
Elia Erwani Hassan ◽  
Titik Khawa Abdul Rahman ◽  
Zuhaina Zakaria ◽  
Nazrulazhar Bahaman
Keyword(s):  
Optimization Algorithm ◽  
Economic Dispatch ◽  
Heuristic Method ◽  
Optimization Approach ◽  
The Novel ◽  
Local Minima ◽  
Bacterial Foraging Optimization ◽  
Reliable Technique ◽  
Bacterial Foraging ◽  
Bacterial Foraging Optimization Algorithm

This paper introduced a new heuristic method the Improved to Bacterial Foraging Optimization Algorithm or IBFO to provide minimize objective functions in Secured Environmental Economic Dispatch (SEED) problems. An optimization problem may involve the highly non linear, non convex and non differentiable tends the solutions observed from a multiple local minima. The limitation faced by conventional methods are being trapped at any this local minima and prevent to reach the global minima. For that reason, this approach IBFO is tested under IEEE 118 bus system to obtain the minimum total cost function with less emission involved. Additionally, the proposed optimization approach is compared to original Bacterial Foraging Optimization Algorithm (BFO). As a result, all findings supported the novel IBFO as the competent and reliable technique.

scholarly journals An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Xiaohui Yan ◽  
Yunlong Zhu ◽  
Hao Zhang ◽  
Hanning Chen ◽  
Ben Niu
Keyword(s):  
Social Learning ◽  
Population Size ◽  
Optimization Algorithm ◽  
Statistic Analysis ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Adaptive Step ◽  
Swarm Intelligence Algorithm ◽  
Bacterial Foraging Optimization Algorithm

Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.

scholarly journals HARMONIC COMPONENTS ESTIMATION IN POWER SYSTEM USING BACTERIAL FORAGING OPTIMIZATION ALGORITHM AND STOCHASTIC GRADIENT ALGORITHM WITH VARIABLE FORGETTING FACTOR

2016 ◽  
Vol 17 (1) ◽  
pp. 127-146
Author(s):  
Ahmad Mohammadzadeh ◽  
Jalil Sadati ◽  
Behrooz Rezaie
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Convergence Speed ◽  
Stochastic Gradient ◽  
Recursive Least Squares ◽  
Forgetting Factor ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Variable Forgetting Factor ◽  
Bacterial Foraging Optimization Algorithm

In this paper, a hybrid configuration algorithm called stochastic gradient method with variable forgetting factor (SGVFF) is proposed to better estimate unknown parameters in a power system such as amplitude and phase of harmonics using variable forgetting factor following the bacterial foraging optimization algorithm (BFO). It must be mentioned that harmonic estimation is a nonlinear problem and using linear optimization algorithms for solving this problem reduces the convergence speed. Thus, BFO algorithm is used for initial estimation. In this paper, first, using little information and by applying BFO algorithm in an off-line procedure initial value for SGVFF algorithm is achieved and then SGVFF algorithm is gained in an on-line procedure. In the hybrid algorithm applied in this paper, amplitudes and phases are estimated simultaneously. Simulation results indicate that the proposed method has faster convergence speed, better performance and higher accuracy in a noisy system in comparison with recursive least squares variable forgetting factors algorithm (RLSVFF). This proves the superiority of the proposed method.KEYWORDS:  Power system harmonic; BFO algorithm; SGVFF method; RLSVFF method

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.

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.

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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