V. Rajinikanth

Otsu based optimal multilevel image thresholding using firefly algorithm

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. Otsus 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 Levy 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.

Controller parameter optimization for nonlinear systems using enhanced bacteria foraging algorithm

An enhanced bacteria foraging optimization (EBFO) algorithm-based Proportional + integral + derivative (PID) controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.

I-PD controller tuning for unstable system using bacterial foraging algorithm: a study based on various error criterion

This paper proposes a novel method to tune the I-PD controller structure for the time-delayed unstable process (TDUP) using Bacterial Foraging Optimization (BFO) algorithm. The tuning process is focussed to search the optimal controller parameters (Kp, Ki, Kd) by minimising the multiple objective performance criterion. A comparative study on various cost functions like Integral of Squared Error (ISE), Integral of Absolute Error (IAE), Integral of Time-weighted Squared Error (ITSE), and Integral of Time weighted Absolute Error (ITAE) have been attempted for a class of TDUP. A simulation study for BFO-based I-PD tuning has been done to validate the performance of the proposed method. The results show that the tuning approach is a model independent approach and provides enhanced performance for the setpoint tracking with improved time domain specifications.

Multi-level image thresholding using Otsu and chaotic bat algorithm

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu’s thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu’s between-class variance and a novel chaotic bat algorithm (CBA). Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images. The proposed procedure is applied on a standard test images set of sizes (512xa0×xa0512) and (481xa0×xa0321). Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm. The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search. The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives. Therefore, it can be applied in complex image processing such as automatic target recognition.

PSO-Based PID Controller Design for a Class of Stable and Unstable Systems

Nonlinear processes are very common in process industries, and designing a stabilizing controller is always preferred to maximize the production rate. In this paper, tuning of PID controller for a class of time delayed stable and unstable process models using Particle Swarm Optimization (PSO) algorithm is discussed. The dimension of the search space is only three (, , and ); hence, a fixed weight is assigned for the inertia parameter. A comparative study is presented between various inertia weights such as 0.5, 0.75, and 1. From the result, it is evident that the proposed method helps to attain better controller settings with reduced iteration number. The efficacy of the proposed scheme has been validated through a comparative study with classical controller tuning methods and heuristic methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Finally, a real-time implementation of the proposed method is carried on a nonlinear spherical tank system. From the simulation and real-time results, it is evident that the PSO algorithm performs well on the stable and unstable process models considered in this work. The PSO tuned controller offers enhanced process characteristics such as better time domain specifications, smooth reference tracking, supply disturbance rejection, and error minimization.

Entropy based segmentation of tumor from brain MR images a study with teaching learning based optimization

This work proposes the meta-heuristic approach assisted segmentation and analysis of glioma from brain MRI dataset.A novel two stage approach is implemented based on tri-level thresholding and level set segmentation.A detailed analysis of well known entropy approaches, such as Kapur, Tsallis and Shannon are presented.A comparative study between level set and active contour segmentation is presented. Image processing plays an important role in various medical applications to support the computerized disease examination. Brain tumor, such as glioma is one of the life threatening cancers in humans and the premature diagnosis will improve the survival rate. Magnetic Resonance Image (MRI) is the widely considered imaging practice to record the glioma for the clinical study. Due to its complexity and varied modality, brain MRI needs the automated assessment technique. In this paper, a novel methodology based on meta-heuristic optimization approach is proposed to assist the brain MRI examination. This approach enhances and extracts the tumor core and edema sector from the brain MRI integrating the Teaching Learning Based Optimization (TLBO), entropy value, and level set / active contour based segmentation. The proposed method is tested on the images acquired using the Flair, T1C and T2 modalities. The experimental work is implemented and is evaluated using the CEREBRIX and BRAINIX dataset. Further, TLBO assisted approach is validated on the MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and achieved better values of Jaccard index, dice co-efficient, precision, sensitivity, specificity and accuracy. Hence the proposed segmentation approach is clinically significant. Display Omitted

Gray-Level Histogram based Multilevel Threshold Selection with Bat Algorithm

thresholding is a well known image segmentation procedure extensively attempted to obtain binary image from the gray level image. In this article, histogram based bi-level and multi-level segmentation is proposed for gray scale images using Bat Algorithm (BA). The optimal thresholds are attained by maximizing Otsus between class variance function. The performance of BA is demonstrated by considering five benchmark (512 x 512) images and compared it with the existing algorithms such as Particle Swarm Optimization (PSO), and Bacterial Foraging Optimization (BFO) existing in the literature. The performance assessment between algorithms is carried out using prevailing parameters such as objective function, Peak Signal to Noise Ratio (PSNR), and Structural Dissimilarity (SSIM) index. The results evident that BA provides better objective function, PSNR and SSIM compared to PSO, and BFO considered in this study.

Firefly Algorithm with Various Randomization Parameters: An Analysis

In recent years, metaheuristic algorithms are widely employed to provide optimal solutions for engineering optimization problems. In this work, a recent metaheuristic Firefly Algorithm FA is adopted to find optimal solution for a class of global benchmark problems and a PID controller design problem. Until now, few research works have been commenced with FA. The updated position in a firefly algorithm mainly depends on parameters such as attraction between fireflies due to luminance and randomization operator. In this paper, FA is analyzed with various randomization search strategies such as Levy Flight LF and Brownian Distribution BD. The proposed method is also compared with the other randomization operator existing in the literature. The performance assessment between LF and BD based FA are carried using prevailing parameters such as search time and accuracy in optimal parameters. The result evident that BD based FA provides better optimization accuracy, whereas LF based FA provides faster convergence.

Tuning and Retuning of PID Controller for Unstable Systems Using Evolutionary Algorithm

Proportional + integral + derivative (PID) controllers are widely used in industrial applications to provide optimal and robust performance for stable, unstable, and nonlinear processes. In this paper, particle swarm optimization (PSO) algorithm is proposed to tune and retune the PID controller parameter for a class of time-delayed unstable systems. The proposal is to search the optimal controller parameters like 𝐾 𝑝 , 𝐾 𝑖 , and 𝐾 𝑑 by minimising the cost function. The integral of squared error (ISE) criterion is considered as the cost function, which guides the PSO algorithm to get the optimised controller parameters. The procedure for PID parameter tuning and retuning is presented in detail. A comparative study is done with the conventional PID tuning methods proposed in the literature. The simulation results show that the PSO-based PID controller tuning approach provides improved performance for the setpoint tracking, load disturbance rejection, error minimization, and measurement noise attenuation for a class of unstable systems.

Robust Color Image Multi-thresholding Using Between-Class Variance and Cuckoo Search Algorithm

Multi-level image thresholding is a well known pre-processing procedure, commonly used in variety of image related domains. Segmentation process classifies the pixels of the image into various group based on the threshold level and intensity value. In this paper, colour image segmentation is proposed using Cuckoo Search (CS) algorithm. The performance of the proposed technique is validated with the Bacterial Forage Optimization (BFO) and Particle Swarm Optimization (PSO). The qualitative and quantitative investigation is carried out using the parameters, such as CPU time, between-class variance value and image quality measures, such as Mean Structural Similarity Index Matrix (MSSIM), Normalized Absolute Error (NAE), Structural Content (SC) and PSNR. The robustness of the implemented segmentation procedure is also verified using the image dataset smeared with the Gaussian Noise (GN) and Speckle Noise (SN). The study shows that, CS algorithm based multi-level segmentation offers better result compared with BFO and PSO.