Optimization of Fidelity with Adaptive Genetic Watermarking Algorithm Using Roulette-Wheel

Goyal, Sachin; Gupta, Roopam

doi:10.1109/cicn.2010.117

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…Roulette-wheel and Tournament selection strategy [9][10] is used to find out the desired locations for watermark insertion for fidelity optimization. In our experiments, the cover images used is Lena size (512 X 512) shown in figure 1and the binary two dimensional matrix with varying length in different experiments as the watermark.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

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Comparison of Genetic Algorithm based Watermarking Techniques using Tournament Selection Approach and Roulette Wheel Approach for Fidelity Optimization

Goyal¹,

Gupta²

2015

IJSIA

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To protect digital content from illegal copy or reproduction, digital watermarking techniques are used which embed watermark into digital content and extract the same from the digital content to help in digital media protection. As techniques based on spatial and frequency domain are reported to have several limitations due to unsatisfactory values of fidelity by various researchers, new techniques based on genetic algorithm have been developed with an objective to optimize the values of fidelity of watermarked image. Genetic algorithms are used to find suitable locations for watermark insertion within a cover image, using either tournament selection approach or roulette wheel selection approach to provide optimization of fidelity. This paper is an attempt to provide a comparative study of the results obtained with genetic based watermarking techniques using roulette wheel selection approach and tournament selection approach . The variation of maximum fitness with respect to changing embedding strength, number of genes, mutation probability. Crossover probability and varying payloads has been compared and discussed for both the selection strategies.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

“…S. Goyal et al [9 10] use a roulette-wheel and tournament selection strategy in genetic algorithm and optimize the fidelity in spatial domain. This paper provides comparative study of the results obtained with genetic based watermarking techniques using roulette wheel selection [9] approach and tournament selection approach [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Genetic Algorithm based Watermarking Techniques using Tournament Selection Approach and Roulette Wheel Approach for Fidelity Optimization

Goyal¹,

Gupta²

2015

IJSIA

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“…After obtaining the fitness of population, selection operation will be carried out to choose the individual according to its fitness. Roulette Algorithm (RA) [14], [15] is used in our algorithm. First, ( ) is calculated by the following equation to represent the probability of th individual being selected.…”

Section: Selectionmentioning

confidence: 99%

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

Pan

Yang

2021

IEEE Access

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To collect data of distributed sensors located at different areas in challenging scenarios through artificial way is obviously inefficient, due to the numerous labor and time. Unmanned Aerial Vehicle (UAV) emerges as a promising solution, which enables multi-UAV collect data automatically with the preassigned path. However, without a well-planned path, the required number and consumed energy of UAVs will increase dramatically. Thus, minimizing the required number and optimizing the path of UAVs, referred as multi-UAV path planning, are essential to achieve the efficient data collection. Therefore, some heuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Algorithm (ACA) which works well for multi-UAV path planning have been proposed. Nevertheless, in challenging scenarios with high requirement for timeliness, the performance of convergence speed of above algorithms is imperfect, which will lead to an inefficient optimization process and delay the data collection. Deep learning (DL), once trained by enough datasets, has high solving speed without worries about convergence problems. Thus, in this paper, we propose an algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA), which combines the advantages of DL and GA. GA will collect states and paths from various scenarios and then use them to train the deep neural network so that while facing the familiar scenarios, it can rapidly give the optimized path, which can satisfy high timeliness requirements. Numerous experiments demonstrate that the solving speed of DL-GA is much faster than GA almost without loss of optimization capacity and even can outperform GA under some specific conditions.

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“…To charge electric vehicles relatively at a low price, Genetic Algorithm is used to find the time zone to charge [6] [7]. The rate data to refer for optimization is automatically selected by Genetic Algorithm.…”

Section: Optimal Electricity Charging With Gamentioning

confidence: 99%

Optimal Electricity Charging for Smart Grid

Baek¹,

Jang²,

Han³

et al. 2017

Advanced Science and Technology Letters

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Abstract. The amount of energy consumption has been increasing over the years and the peak usage of energy is approaching to limited capacity of a power grid system. To prevent large scale black-out power outage in worst case, the consumer can control their own energy consumption, and electric vehicles are good means to control the demand of electricity. The time when the users charge the electric vehicles is one of the important factors to avoid peak usage of energy. Even though the electric rates are decided depending on the demand which is decided according to the weather, temperature, and a variety of regional factors, there are regular patterns in the past and current rates, which means the current electric rates can be decided by referring the past data. Therefore, we proposed a method to optimally charge electric vehicles based on past real-time price information with Genetic Algorithm. For this, the past date to use and the time zone to charge electric vehicles are encoded as a binary string in a gene. With a real data set collected by an electricity power company, we tested and evaluated the proposed approach. We could effectively charge electric vehicles at a low cost with Genetic Algorithm, which means we can effectively avoid the peak usage of energy.

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Optimization of Fidelity with Adaptive Genetic Watermarking Algorithm Using Roulette-Wheel

Cited by 8 publications

References 15 publications

Comparison of Genetic Algorithm based Watermarking Techniques using Tournament Selection Approach and Roulette Wheel Approach for Fidelity Optimization

Comparison of Genetic Algorithm based Watermarking Techniques using Tournament Selection Approach and Roulette Wheel Approach for Fidelity Optimization

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

Optimal Electricity Charging for Smart Grid

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