Computer
Sanaa Ali Jabber; Soukaena H. Hashem; Shatha H Jafer
Abstract
Finding an optimal solution to some problem, like minimizing andmaximizing the objective function, is the goal of Single-Objective Optimization (SOP).Real-world problems, on the other hand, are more complicated and involve a widerrange of objectives, several objectives should be maximized in such problems. ...
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Finding an optimal solution to some problem, like minimizing andmaximizing the objective function, is the goal of Single-Objective Optimization (SOP).Real-world problems, on the other hand, are more complicated and involve a widerrange of objectives, several objectives should be maximized in such problems. No singlesolution could be enhanced in all objectives without deteriorating at least one othergoal, which is the definition of Pareto-optimality. Understanding the idea of MultiObjective Optimization (MOP) is thus necessary to find the optimum solution. Multiobjective evolutionary algorithm (MOEA) are made to simultaneously assess manyobjectives and find Pareto-optimal solutions, MOEA can resolve multi-objective andsingle-objective optimization problems.This paper aims to introduce a survey study for optimization problem solutions bycomparing techniques, advantages, and disadvantages of SOP and MOP withmetaheuristics and evolutionary algorithms. From this study, we conduct that theefficiency of MOP lies in the present more than one SOP, but it takes a longer time toprocess and train and is not suitable for all applications, While SOP is faster and moreuseful in stock and profit maximization applications. And the posterior techniques areconsidered the dominant approach to solving multi-objective problems by the use of thefield of metaheuristics.
Communication
Ghada K. Emad; Soukaena Hassan Hashiem
Abstract
Recently, the growth of data transmission through various networks has the necessity for an elevated level of security. Encryption is one of the essential technologies for protecting and ensuring the integrity of IoT devices. Secure communication among constrained devices is critical during data transmission ...
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Recently, the growth of data transmission through various networks has the necessity for an elevated level of security. Encryption is one of the essential technologies for protecting and ensuring the integrity of IoT devices. Secure communication among constrained devices is critical during data transmission from the client to the server devices. Lightweight cipher algorithms are defined as a secure solution for devices with limited computational functions and memory. On the other hand, most lightweight algorithms suffer from a trade-off between complexity and speed to produce a robust cipher algorithm. This paper evaluates the effectiveness of an image encryption technique that uses a Lightweight GIFT algorithm and logistic map equation-based dynamic DNA coding to create a secure, lightweight cipher algorithm for IoT devices. When we employed dynamic DNA coding by the binary bit method, we observed that the developed approach is more secure and has a high level of randomness due to the results. Additionally, the correlation between nearby pixels is approximately zero; there is no association between the two images. Therefore, the developed approach achieves a higher encryption efficiency when compared to the original algorithm.
