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The Behavious of Ant Colony

The Behavious of Ant Colony

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Information security and confidentiality are the prime concern of any type of communication. The techniques that utilizing inconspicuous digital media such as text, audio, video and image for hiding confidential data in it are collectively called Steganography. The key challenge of steganographic system design is to maintain a fair trade-off betwee...

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Steganography is an interesting science to be studied and researched at this time, because steganography is the science of hiding messages on other digital media so that other parties are not aware of the existence of information in the digital media. Steganography is very effective in maintaining information security, because the existence of this...

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... Histogram reveals each pixel's precise occurrence in the picture. The remarkable resemblance between the histograms of the host and the stego shows the minimum distortion after the secret picture has been integrated into the host image [13]. ...
... The optimization process goes through crossover, mutation and then the fittest gen will be selected [5]. Particle Swarm Optimization (PSO) technique that is depends on the swarm conduct of both fish as well as birds, the system which is described as system of multi-agent may include features of this intelligence of swarm group [6], Firefly Algorithm (FA) that is depends on the features of flashing ideal conduct of fireflies in the tropic regions [7] [8] whereas another algorithm depending on the echolocation conduct of micro bats called a Bat Algorithm (BA) [9]. Also, there are some recent nature inspired algorithms; for example, Harris Hawks Optimizer (HHO) is an optimization algorithm inspired by the conducts of cooperative as well as chasing patterns of predacious birds [10], Another algorithm which is simulates the conduct of slaps swarming during transportation when foraging in oceans called the Salp Swarm Algorithm (SSA) [11], also the technique of Beetle Antennae Search (BAS) which is inspired by the behavior of searching of long-horn bug [12], Finally, Black Widow Optimization (BWO) is an optimization technique that is inspired by the single conduct of black widow spiders mating [13]. ...
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Nature-inspired algorithms are often used by several diverse areas of engineering and science due to their easiness and versatility. Because metaheuristics operate by structurally changing and improving an established problem, they can often be extended to any optimization issues. The recent creation of meta-heuristic algorithms has rendered them effective tools for solving NP problems. This paper presents a hybrid meta-heuristic method based on the Differential Evolution and Bird Mating Optimizer techniques to solve problems of global optimization. Bird Mating Optimizer is a novel method and is inspired by mating behavior of birds. Bird Mating Optimizer has some drawbacks such as producing poor results, trapping into local optima and slow convergence speed. Therefore, to conquer these insufficient it is hybridized with Differential Evolution approach. Differential Evolution technique is utilized to retain a preferable balance between both searches local and global. The performance and effectiveness of new Differential Evolution and Bird Mating Optimizer algorithm is tested and evaluated on 15 different functions of benchmark. The results of the experiment have shown the proposed technique possesses excellent performance in convergence speed, stability, and robustness, as compared to the well-known algorithms. It is proved that the Differential Evolution and Bird Mating Optimizer algorithm is very effective and superior to solve problems of global optimization. Experimental results indicate that the proposed hybrid Differential Evolution and Bird Mating Optimizer method is superior to previous existing state-of-the-art metaheuristic algorithms.
... The optimization process goes through crossover, mutation and then the fittest gen will be selected [5]. Particle Swarm Optimization (PSO) technique that is depends on the swarm conduct of both fish as well as birds, the system which is described as system of multi-agent may include features of this intelligence of swarm group [6], Firefly Algorithm (FA) that is depends on the features of flashing ideal conduct of fireflies in the tropic regions [7] [8] whereas another algorithm depending on the echolocation conduct of micro bats called a Bat Algorithm (BA) [9]. Also, there are some recent nature inspired algorithms; for example, Harris Hawks Optimizer (HHO) is an optimization algorithm inspired by the conducts of cooperative as well as chasing patterns of predacious birds [10], Another algorithm which is simulates the conduct of slaps swarming during transportation when foraging in oceans called the Salp Swarm Algorithm (SSA) [11], also the technique of Beetle Antennae Search (BAS) which is inspired by the behavior of searching of long-horn bug [12], Finally, Black Widow Optimization (BWO) is an optimization technique that is inspired by the single conduct of black widow spiders mating [13]. ...
... In terms of classification accuracy, Particle Swarm Optimization (PSO) is effectively selected compared with other existing selection methods. From the review [28,29,30,31,32] PSO's potential benefits for the feature selection are as shown in: ...
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... The stego-secret key is shared with the authorized recipient that enables the decoding of the stego-object and the extraction of the message at the receiver side. Steganography can be classified into 3 types according to its approach [7]. The first type is Pure Steganography, which depends only on a steganography technique without the combination of any other techniques as in [8]. ...
... Histogram reveals each pixel's precise occurrence in the picture. The remarkable resemblance between the histograms of the host and the stego shows the minimum distortion after the secret picture has been integrated into the host image [13]. ...
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Steganography includes hiding text, image, or any sentient information inside another image, video, or audio. It aims to increase individuals' use of social media, the internet and web networks to securely transmit information between sender and receiver and an attacker will not be able to detect its information. The current article deals with steganography that can be used as machine learning method, it suggests a new method to hide data by using unsupervised machine learning (clustering k-mean algorithm). This system uses hidden data into the cover image, it consists of three steps: the first step divides the cover image into three clusterings that more contrast by using k-means cluster, the selects a text or image to be converted to binary by using ASCII code, the third step hides a binary message or binary image in the cover image by using sequential LSB method. After that, the system is implemented. The objective of the suggested system is obtained, using Unsupervised Machine Learning (K-mean technique) to securely send sensitive information without worrying about man-in-the-middle attack was proposed. Such a method is characterized by high security and capacity. Through evaluation, the system uses PSNR, MSE, Entropy, and Histogram to hide the secret message and secret image in the spatial domain in the cover image.
... The optimization process goes through crossover, mutation and then the fittest gen will be selected [5]. Particle Swarm Optimization (PSO) technique that is depends on the swarm conduct of both fish as well as birds, the system which is described as system of multi-agent may include features of this intelligence of swarm group [6], Firefly Algorithm (FA) that is depends on the features of flashing ideal conduct of fireflies in the tropic regions [7] [8] whereas another algorithm depending on the echolocation conduct of micro bats called a Bat Algorithm (BA) [9]. Also, there are some recent nature inspired algorithms; for example, Harris Hawks Optimizer (HHO) is an optimization algorithm inspired by the conducts of cooperative as well as chasing patterns of predacious birds [10], Another algorithm which is simulates the conduct of slaps swarming during transportation when foraging in oceans called the Salp Swarm Algorithm (SSA) [11], also the technique of Beetle Antennae Search (BAS) which is inspired by the behavior of searching of long-horn bug [12], Finally, Black Widow Optimization (BWO) is an optimization technique that is inspired by the single conduct of black widow spiders mating [13]. ...
Thesis
Mimicking natural phenomenon of social insects, such as fish shoals and schools, bird flocks and insect colonies by merging randomness facility and some other simulations rules, are the core tasks of the artificial meta-heuristic algorithms. Modern meta-heuristic algorithms are the most efficient and powerful techniques used to solve complicated real-world optimization problems. Recently Developed Firefly optimization algorithm which belongs to nature inspired meta-heuristics algorithms is inspired by mating and flashing behavior or the phenomenon of bioluminescent communication of fireflies in the nature. In this thesis, Firefly optimization algorithm has been demonstrated in details with the flowchart of the Algorithm and it has been programmed first in C/C++ language using Rosenbrock benchmark test function, considering it as software part. Then the hardware design for the proposed algorithm has been described by very high speed integrated circuit Hardware Description Language (VHDL) and simulated by using Xilinx ISE program V. 10. 1. Firefly algorithm is executing sequentially as all meta-heuristic algorithms, due to the nature of algorithm. Therefore sequential hardware design for the algorithm using Finite State Machine (FSM) has been proposed. The hardware design architecture has been implemented on reconfigurable platforms FPGAs (SPARTAN 3×S1600). This confirms that the proposed design has a minimum hardware resource, where only 21% of the chip resources are used with realizable operating clock frequently of 10 MHz considering it as hardware part.
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The complexity in real-world problems motivated researchers to innovate efficient problem-solving techniques. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and Non-deterministic polynomial hard (NP-Hard) problems because of their ability to adjust to variety of conditions. This paper shows an overview for swarm based algorithm that based on ant behavior. The first algorithm that inspired ant behavior in search for food source was developed in 1992 and was tested in solving TSP problem Ant Colony Optimization (ACO) is a metaheuristic inspired by some ant species' pheromone trail laying and following behavior. Artificial ants in ACO are stochastic solution construction processes that use (artificial) pheromone information that is modified depending on the ants' search experience and possibly accessible heuristic information to generate candidate solutions for the problem instance under consideration. Many notable research achievements have been gained since the proposal of the Ant System, the first ACO algorithm. These contributions concentrated on the creation of high-performance algorithmic variants, the creation of a generic algorithmic framework for ACO algorithms, the successful application of ACO algorithms to a wide range of computationally difficult problems, and the theoretical understanding of ACO algorithm properties. Since the appearance of ACO many modifications for improving the performance of the algorithm and has been applied to various Applications in several fields. At the end of this paper, the improvements are listed
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Steganography has been used massively in numerous fields to maintain the privacy and integrity of messages transferred via the internet. The need to secure the information has augmented with the increase in e-governance usage. The wide adoption of e-governance services also opens the doors to cybercriminals for fraudulent activities in cyberspace. To deal with these cybercrimes we need optimized and advanced steganographic techniques. Various advanced optimization techniques can be applied to steganography to obtain better results for the security of information. Various optimization techniques like particle swarm optimization and genetic algorithms with cryptography can be used to protect information for e-governance services. In this study, a comprehensive review of steganographic algorithms using optimization techniques is presented. A new perspective on using this technique to protect the information for e-governance is also presented. Deep Learning might be the area that can be used to automate the steganography process in combination with other methods
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Digital watermarking is getting more research and industry attention. Digital multimedia data allows for robust and simple data editing and modification. However, the spread of digital media presents concerns for digital content owners. It is important to note that digital data can be copied without quality or content loss. This has a considerable impact on copyright holders' ability to safeguard their intellectual property rights. The method of transmitting information by imperceptibly embedding it into digital media is digital watermarking. There are various methods in literature, such as DWT and DCT, which take full energy, are seen and integrated. New strategies and procedures for optimization are required. The present study proposes a novel design and computation technique based on the discrete wavelet and discrete cosine transforms. Watermarking techniques have been progressing to shield media content such as text, audio, video, etc. From copyright. The proposed hybrid DWT-DCT Bacterial Foraging Optimization (BFO) technique improves the efficiency of watermarking digital images by 97%. Bacterial foraging optimization (BFO) is an innovative technique for intelligent optimization. It is a widely used optimization algorithm in a wide variety of applications. However, when compared to other optimizers, the BFO performs poorly in terms of convergence. This technique uses a high-frequency image region. A variety of techniques are compared with the (NCC) Normalized Cross Correlations, (PSNR) Peak Noise Signal Ratio and IF (Image Fidelity). The highest performance is seen in DWT-DCT-BFO watermarking.