An enhanced image encryption technique combining genetic algorithm and particle swarm optimization with chaotic function

Ferdush, Jannatul; Mondol, Greetashree; Prapti, Amrita Pritom; Begum, Mahbuba; Sheikh, Mohammad Nowsin Amin; Galib, Syed Md.

doi:10.1080/1206212x.2019.1662170

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…The vigorousness of the optimizing principles is additionally supplemented with a chaotic map notion for improved safety accomplishments. Ferdush et al [ 26 ] proposed a technique that utilizes the Genetic Algorithm (GA) for an improved pixel value encryption and PSO to enhance the process of optimization. This technique is segmented into two sectors.…”

Section: Related Workmentioning

confidence: 99%

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security

Almasoud,

Alabduallah,

Alqahtani

et al. 2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security

Almasoud,

Alabduallah,

Alqahtani

et al. 2024

Heliyon

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“…In addition to the GA and ACO algorithms, particle swarm optimization (PSO) and simulated annealing (SA) algorithms are also suitable for TSP. Therefore, the first layer can also use the GA [37], SA [38], ACO [39,40], and PSO algorithms [41][42][43][44]. Four intelligent two-layer algorithms (GA-GA, SA-GA, ACO-GA, and PSO-GA) are compared with the proposed GAACO-GA algorithm.…”

Section: Dispatch Route Planning Algorithm For Multiple Forest Areasmentioning

confidence: 99%

Route Planning of Helicopters Spraying Operations in Multiple Forest Areas

Fang

Liu

et al. 2021

Forests

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It is of great value to research the problem of forest pest and disease control. Currently, helicopters play an important role in dealing with this problem. However, the spraying route planning still depends on the pilot’s driving experience, which leads to low efficiency and less accurate coverage. For this reason, this paper attempts to use intelligent algorithms to plan the pesticide spraying route for helicopters. When the helicopter is conducting spraying operations in multiple forest areas, the routes are divided into two parts: pesticide spraying routes for individual forest areas and dispatch routes between multiple forest areas. First, the shorter spraying route with fewer turnarounds for individual forest areas was determined. Then a two-layer intelligent algorithm, a combination of a genetic algorithm (GA) and ant colony optimization algorithm (ACO), was designed to determine the dispatch route between multiple forest areas, which is referred to as GAACO-GA. The performance was evaluated in self-created multiple forest areas and compared with other two-layer intelligent algorithms. The results show that the GAACO-GA algorithm found the shortest dispatch route (5032.75 m), which was 5.60%, 5.45%, 6.54%, and 4.07% shorter than that of GA-GA algorithm, simulated annealing-GA (SA-GA) algorithm, ACO-GA algorithm, and particle swarm optimization-GA (PSO-GA) algorithm, respectively. A spraying experiment with a helicopter was conducted near Pigzui Mountain, Huai’an City, Jiangsu Province, China. It was found that the flight path obtained from the proposed algorithm was 5.43% shorter than that derived from a manual planning method. The dispatch route length was reduced by 16.93%, the number of turnarounds was reduced by 11 times, and the redundant coverage was reduced by 17.87%. Moreover, helicopter fuel consumption and pesticide consumption decreased by 10.56% and 5.43%, respectively. The proposed algorithm can shorten the application route, reduce the number of turnarounds and the cost of spraying operations, and has the potential for use in spraying operations in smart forestry and agriculture.

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“…Many other genetic algorithm techniques have also been used for image encryption [23,30,32] where different operators of the genetic algorithm were applied on image pixels and a key was generated as a result to decrypt the image back to its original form. Genetic algorithm has also been used along with other techniques to improve image encryption results [11].…”

Section: Genetic Algorithm (Ga) In Cryptographymentioning

confidence: 99%

Improving Cloud Data Encryption Using Customized Genetic Algorithm

Arshad¹,

Umair²,

Munawar³

et al. 2020

IJISA

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Data Encryption is widely utilized for ensuring data privacy, integrity, and confidentiality. Nowadays, a large volume of data is uploaded to the cloud, which increases its vulnerability and adds to security breaches. These security breaches include circumstances where sensitive information is being exposed to third parties or any access to sensitive information by unauthorized personnel. The objective of this research is to propose a method for improving encryption by customizing the genetic algorithm (GA) with added steps of encryption. These added steps of encryption include the data being processed with local information (chromosome's value calculated with computer-generated random bits without human intervention). The improvement in the randomness of the key generated is based on altering the population size, number of generations, and mutation rate. The first step of encrypting is to convert sample data into binary form. Once the encryption process is complete, this binary result is converted back to get the encrypted data or cipher-text. Foremost, the GA operators (population size, number of generations, and mutation rate) are changed to determine the optimal values of each operator to bring forth a random key in the minimum possible time, then local intelligence is headed in the algorithm to further improve the outcomes. Local Intelligence consists of local information and a random bit generated in each iteration. Local Information is the current value of a parent in each iteration at the gene level. Both local information and random bit are then applied in a mathematical pattern to generate a randomized key. The local intelligence-based algorithm can operate better in terms of time with the same degree of randomness that is generated with the conventional GA technique. The result showed that the proposed method is at least 80% more efficient in terms of time while generating the secret key with the same randomness level as generated by a conventional GA. Therefore, when large data are intended to be encrypted, then using local intelligence can demonstrate to be better utilized time.

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An enhanced image encryption technique combining genetic algorithm and particle swarm optimization with chaotic function

Cited by 12 publications

References 14 publications

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security

Route Planning of Helicopters Spraying Operations in Multiple Forest Areas

Improving Cloud Data Encryption Using Customized Genetic Algorithm

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