Image Segmentation Based on Bacterial Foraging and FCM Algorithm

2011 ◽  
Vol 2 (3) ◽  
pp. 16-28
Author(s):  
Hongwei Mo ◽  
Yujing Yin
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Algorithm ◽  
Data Sets ◽  
Criterion Function ◽  
Bacterial Foraging Optimization ◽  
Objective Criterion ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Search Capability

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possible best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired effects. All the experiment results show that BF-FCM has better performance than that of standard FCM.

Image Segmentation Based on Bacterial Foraging and FCM Algorithm

2013 ◽  
pp. 209-222
Author(s):  
Hongwei Mo ◽  
Yujing Yin
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Algorithm ◽  
Data Sets ◽  
Bacterial Foraging Optimization ◽  
Objective Criterion ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Search Capability ◽  
Bacterial Foraging Optimization Algorithm

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possible best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired effects. All the experiment results show that BF-FCM has better performance than that of standard FCM.

Image Segmentation Based on Bio-Inspired Optimization Algorithms

2015 ◽  
pp. 259-284
Author(s):  
Hongwei Mo ◽  
Lifang Xu ◽  
Mengjiao Geng
Keyword(s):  
Image Processing ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimization Algorithms ◽  
Objective Criterion ◽  
Swarm Optimization ◽  
Fish School ◽  
Bacterial Foraging Algorithm ◽  
Search Capability

This chapter addresses the issue of image segmentation by clustering in the domain of image processing. Fuzzy C-Means is a widely adopted clustering algorithm. Bio-inspired optimization algorithms are optimal methods inspired by the principles or behaviors of biology. For the purpose of reinforcing the global search capability of FCM, five Bio-Inspired Optimization Algorithms (BIOA) including Biogeography-Based Optimization (BBO), Artificial Fish School Algorithm (AFSA), Artificial Bees Colony (ABC), Particle Swarm Optimization (PSO), and Bacterial Foraging Algorithm (BFA) are used to optimize the objective criterion function, which is interrelated to centroids in FCM. The optimized FCMs by the five algorithms are used for image segmentation, respectively. They have different effects on the results.

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 A Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm for Optimized Clustering in Wireless Sensor Network

2019 ◽  
Vol 8 (10) ◽  
pp. 2186-2190
Keyword(s):  
Sensor Networks ◽  
Network Lifetime ◽  
Artificial Bee Colony ◽  
Weather Forecasting ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bee Colony ◽  
Bacterial Foraging Algorithm

The emerging ubiquitous nature of wireless sensor networks has made it suitable and applicable to a diversified number of vital applications that include environment surveillance, health monitoring using implantable sensors, weather forecasting and other plethora of contexts. The critical issues such as computation time, limited memory and energy are more common due to the tiny sized hundred and thousands of sensor nodes existing in the networks. In this context, the network lifetime completely depends on the potential use of available resources. The process of organizing closely located sensor nodes into clusters is convenient for effective management of cluster and improving the lifetime of the complete network. At this juncture, swarm intelligent and evolutionary algorithms the pertains to the problem of NP-complete is determined to achieve a superior optimal solution. In this paper, a Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm-based Optimized Clustering (HABC-BFA-OC) is proposed for achieving enhanced network lifetime in sensor networks. In this proposed HABC-BFA-OC technique, the benefits of Bacterial Foraging Optimization is included for improving the local search potential of ABC algorithm in order to attain maximum exploitation and exploration over the parameters considered for cluster head selection. The simulation experiments of the proposed HABC-BFA-OC technique confirmed an enhanced network lifetime with minimized energy consumptions during its investigation with a different number of sensor nodes.

scholarly journals Diminution of factual power loss by enhanced bacterial foraging optimization algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 245
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Foraging Behaviour ◽  
Reactive Power ◽  
Rotation Angle ◽  
Test System ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Power Problem ◽  
Search Capability ◽  
Quantum Rotation Gate

<div data-canvas-width="126.37004132231402">This paper presents an enhanced bacterial foraging optimization (EBFO) algorithm for solving the optimal reactive power problem. Bacterial foraging optimization is based on foraging behaviour of <em>Escherichia coli</em> bacteria which present in the human intestine. Bacteria have inclination to congregate the nutrient-rich areas by an action called as Chemo taxis. The bacterial foraging process consists of four chronological methods i.e. chemo taxis, swarming and reproduction and elimination-dispersal. In this work rotation angle adaptively and incessantly modernized, which augment the diversity of the population and progress the global search capability. The quantum rotation gate is utilized for chemo taxis to modernize the state of chromosome projected EBFO algorithm has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.</div>

Bacterial Foraging Algorithm Based on Quantum-Behaved Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 948-954 ◽  
Author(s):  
Ling Li ◽  
Xiong Fa Mai
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Local Minimum ◽  
Random Search ◽  
Particle Swarm ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Simulation Results

Bacterial Foraging Optimization(BFA) algorithm has recently emerged as a very powerful technique for real parameter optimization,but the E.coli algorithm depends on random search directions which may lead to delay in reaching the global solution.The quantum-behaved particle swarm optimization (QPSO) algorithm may lead to possible entrapment in local minimum solutions. In order to overcome the delay in optimization and to further enhance the performance of BFA,a bacterial foraging algorithm based on QPSO(QPSO-BFA) is presented.The new algorithm is proposed to combines both algorithms’ advantages in order to get better optimization values. Simulation results on eight benchmark functions show that the proposed algorithm is superior to the BFA,QPSO and BF-PSO.

scholarly journals An Interval Type-2 Possibilistic C-Means Clustering Algorithm and Its Application

2021 ◽  
Vol 2132 (1) ◽  
pp. 012016
Author(s):  
Haihua Xing ◽  
Huannan Chen ◽  
Hongyan Lin ◽  
Xinghui Wu
Keyword(s):  
Image Segmentation ◽  
Fuzzy Sets ◽  
Clustering Algorithm ◽  
High Order ◽  
Data Sets ◽  
Fuzzy Uncertainty ◽  
Penalty Factor ◽  
Interval Type

Abstract In this paper, we aim at the fuzzy uncertainty caused by noise in pattern data. The advantages of PCM algorithm to deal with noise and interval type-2 fuzzy sets to deal with high-order uncertainties are used, respectively. An interval type-2 probability C-means clustering (IT2-PCM) based on penalty factor is proposed. The performance of the algorithm is evaluated by two sets of data sets and two groups of images segmentation experiments. The results show that IT2-PCM algorithm can assign proper membership degrees to clustering samples with noise, and it can detect noise points effectively, and it has good performance in image segmentation.

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