scholarly journals Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
N. Sri Madhava Raja ◽  
V. Rajinikanth ◽  
K. Latha
Keyword(s):  
Objective Function ◽  
Firefly Algorithm ◽  
Search Time ◽  
Structural Similarity ◽  
Threshold Level ◽  
Random Operator ◽  
Multilevel Thresholding ◽  
Search Technique ◽  
Signal Ratio ◽  
Cpu Time

Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF) guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

scholarly journals Modified Firefly Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Surafel Luleseged Tilahun ◽  
Hong Choon Ong
Keyword(s):  
Objective Function ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Current Position ◽  
Random Movement ◽  
Cpu Time ◽  
Modified Firefly Algorithm ◽  
Better Than

Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.

Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

2019 ◽  
Vol 10 (3) ◽  
pp. 91-106
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Anisotropic Diffusion ◽  
Particle Swarm ◽  
Structural Similarity ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Mr Image ◽  
Swarm Optimization ◽  
Mr Image Segmentation

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

scholarly journals Firefly algorithm based multivariable PID controller design for MIMO process

2018 ◽  
Vol 7 (2.31) ◽  
pp. 41
Author(s):  
J Ramyashree ◽  
M R. Roja ◽  
G Sivagurunathan ◽  
R Kotteeswaran
Keyword(s):  
Objective Function ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Distillation Column ◽  
Chemical Engineering ◽  
Controller Design ◽  
Liquid Mixture ◽  
Matlab Simulink ◽  
Decision Variables ◽  
Pid Controller Design

Distillation is the process of separating the components or substances from a liquid mixture by selective boiling and condensation. It is one of the most underestimated fields of chemical engineering and has been around for well over hundred years. This paper deals with the tuning of centralized and decentralized Multivariable PID controller for Wood and Berry distillation column using Firefly algorithm (FA). FA uses controller parameters as decision variables and minimization of IAE as objective function. At the end of the search, optimum solutions for controller parameters are obtained which upon implementation provides challenging results for both top and bottom products. Simulation has been carried out using Matlab/Simulink platform.

scholarly journals A Soft-Threshold Filtering Approach for Tomography Reconstruction from a Limited Number of Projections with Bilateral Edge Preservation

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2346
Author(s):  
Tiago Wirtti ◽  
Evandro Salles
Keyword(s):  
Objective Function ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Edge Preservation ◽  
Edge Preserving ◽  
Reconstruction Process ◽  
Regularization Term ◽  
Computational Performance

In X-ray tomography image reconstruction, one of the most successful approaches involves a statistical approach with l 2 norm for fidelity function and some regularization function with l p norm, 1 < p < 2 . Among them stands out, both for its results and the computational performance, a technique that involves the alternating minimization of an objective function with l 2 norm for fidelity and a regularization term that uses discrete gradient transform (DGT) sparse transformation minimized by total variation (TV). This work proposes an improvement to the reconstruction process by adding a bilateral edge-preserving (BEP) regularization term to the objective function. BEP is a noise reduction method and has the purpose of adaptively eliminating noise in the initial phase of reconstruction. The addition of BEP improves optimization of the fidelity term and, as a consequence, improves the result of DGT minimization by total variation. For reconstructions with a limited number of projections (low-dose reconstruction), the proposed method can achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) results because it can better control the noise in the initial processing phase.

Geometrical Similarity Error Function-Innovative Adaptive Algorithm methodology in path generation synthesis of the four-bar mechanism using metaheuristic algorithms

2021 ◽  
pp. 095440622110157
Author(s):  
A Sardashti ◽  
HM Daniali ◽  
SM Varedi-Koulaei
Keyword(s):  
Objective Function ◽  
Adaptive Algorithm ◽  
Error Function ◽  
The Other ◽  
Synthesis Methods ◽  
Computer Memory ◽  
Path Generation ◽  
Cpu Time ◽  
Geometrical Similarity ◽  
Design Variables

This paper presents a novel methodology for path generation synthesis of the four-bar mechanism. A new objective function for the path generation synthesis problem, namely, the Geometrical Similarity Error Function (GSEF), is introduced. Indeed, GSEF assesses the similarity between generated and desired paths, and its number of design variables is less than those in the other synthesis methods. Then, using an Innovative Adaptive Algorithm (IAA), some operators are utilized for matching two similar paths. GSEF-IAA methodology has some significant advantages over the reported synthesis methods. The method is fast, takes much less CPU time, and saves a large amount of computer memory. Four path generation problems are solved using GSEF-IAA, and the results are compared with those of previous methods using some well-known optimization algorithms to demonstrate the efficiency of GSEF-IAA methodology.

scholarly journals Joint Channel Assignment and Bandwidth Reservation using Improved FireFly Algorithm (IFA) in WMN

2021 ◽  
Author(s):  
narayanarao narayanarao ◽  
A. Rajasekhar Reddy
Keyword(s):  
Objective Function ◽  
Channel Assignment ◽  
Firefly Algorithm ◽  
Path Selection ◽  
Bandwidth Reservation ◽  
Probability Metrics ◽  
Channel Efficiency ◽  
Improved Firefly Algorithm ◽  
Flow Conservation ◽  
Joint Channel

Abstract In WMN, at the time of network consignment and bandwidth registration, the active network consignment method did not take into consideration the intrusion, congestion load and bandwidth necessities as a whole. The significance centred bandwidth registration methods result in famishment of slightest significance congestion. Hence in this paper, we propose a Joint Channel Assignment and Bandwidth Reservation using Improved FireFly Algorithm (IFA) in WMN. Initially the priority of each node is determined based on the channel usage, future interference and link congestion probability metrics. The bandwidth is allocated straight, comparative to the nodule significance and entire quantity of congestion movements incomplete on the demanded nodule. For channel assignment and path selection, the improved FireFly Algorithm (IFA) is used. The objective function of IFA is determined in terms of link capacity, interference and flow conservation constraints. Then the channels and the path which minimize the objective function are selected by applying IFA. By simulation results we show that the proposed technique minimizes the traffic and enhances the channel efficiency.

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