scholarly journals Imperceptible Steganography Scheme with High Payload Capacity using Genetic Algorithm and Particle Swarm Optimization

2019 ◽  
Vol 9 (1) ◽  
pp. 917-923 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Data Embedding ◽  
Secret Data ◽  
Swarm Optimization ◽  
High Data ◽  
Confidential Data ◽  
Payload Capacity ◽  
High Payload

Security is the most significant parameter in all type of confidential data transfers. Steganography is used to enhance the security of such confidential communications. Steganography is a method of covert communication in which the existence of secrecy is concealed. In image steganography, achieving high data embedding capacity and simultaneously retaining good visual quality is a very tricky and difficult objective. In this paper, a reversible, secure, extremely imperceptible and high payload capacity steganography technique in the spatial domain is proposed. The proposed method employs evolutionary computation techniques to identify the most optimum locations and arrangements for secret data embedding. The proposed technique uses Particle Swarm Optimization to find the best possible order of data hiding whereas Genetic algorithm is used to identify the best possible arrangements to modify secret data to produce least amount of change in cover-image. The result of the proposed scheme is compared with many steganography techniques and the proposed scheme outperforms the existing schemes in terms of imperceptibility. The proposed technique produces an average PSNR value of 46.40 dB at 2 bit per pixel data embedding rate.

scholarly journals Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

Open Physics ◽  
2016 ◽  
Vol 14 (1) ◽  
pp. 452-462 ◽  
Author(s):  
Duraisamy Jude Hemanth ◽  
Subramaniyan Umamaheswari ◽  
Daniela Elena Popescu ◽  
Antoanela Naaji
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Frequency Domain ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Image Steganography ◽  
Secret Data ◽  
Stego Image ◽  
Swarm Optimization ◽  
Transform Coefficients

AbstractImage steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.

scholarly journals An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Eagle Strategy

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

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