Chaotic Particle Swarm Optimization Based on Meeting Room Approach for Designing Bijective S-Boxes

Alhadawi, Hussam S.; Salih, Sinan Q.; Salman, Yasir Dawood

doi:10.1007/978-3-030-85990-9_28

Cited by 7 publications

(9 citation statements)

References 30 publications

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“…The AAHC dynamically partitions its population into heroes and cowards using exponential functions. An improved multi-swarm PSO (MPSO) method based on the meeting room approach was proposed by Alhadawi et al [49] for the creation of strong 8 × 8 Sboxes. Despite the existing number of methods for S-box design, none of these methods could create an S-box that exhibits similar levels of security to AES based on relevant metrics.…”

Section: Related Workmentioning

confidence: 99%

“…To put a current work into perspective, there is a need for a through benchmarking evaluation of the proposed approach against the existing metaheuristic algorithms with chaotic map integration, such as Globalized Firefly Algorithm (GFA) [20], Teaching Learning Based Optimization (TLBO) [42], Standard Firefly Algorithm (SFA) [20], Chaotic Firefly Algorithm (CFA) [44], Genetic Algorithm (GA) [16], Ant Colony Optimization (ACO) [43], Bacterial Foraging Optimization (BFO) [40], Artificial Bee Colony (ABC) [13], Simulated Annealing (SA) [39], Cuckoo Search (CS) [46], Jaya Algorithm (JA) [41], Particle Swarm Optimization (PSO) [49]. The comparative performance of various metaheuristic algorithms with chaotic maps is presented in Table 15, and by examining each major column of the table, several notable observations can be highlighted.…”

Section: Comparison Analysismentioning

confidence: 99%

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Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization

Lawah

İbrahim

Salih³

et al. 2023

IEEE Access

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A metaheuristic approach based on the nature-inspired and well-known Grey Wolf Optimization algorithm (GWO) was employed in this study to design an approach for retrieving strong designs of 8 × 8 substitution boxes (S-boxes). The GWO was developed as a novel metaheuristic based on inspiration from grey wolves and how they hunt. The ability of the GWO to quickly explore the search space for the near/optimal feature subsets that maximize any given fitness function (in consideration of its distinctive hierarchical structure) aids in the construction of strong S-boxes that can satisfy the required criteria. However, when tackling optimization problems, GWO may experience the problem of premature convergence. Therefore, a variant of GWO called Crossover Grey Wolf Optimizer (XGWO) has been proposed in this study. The performance of the proposed novel approach was evaluated using numerous cryptographic performance metrics, including bijective property, bit independence, strict avalanche, linear probability, and I/O XOR distribution and the result was contrasted with a couple of existing S-box creation techniques. Overall, the results of the experiment showed that the suggested S-box design had adequate cryptographic features.

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Section: Related Workmentioning

confidence: 99%

Section: Comparison Analysismentioning

confidence: 99%

Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization

Lawah

İbrahim

Salih³

et al. 2023

IEEE Access

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“…Most of them inspired from a biological organism or social life, such as artificial bee colony (ABC), ant colony optimizer (ACO), FA, grey wolf optimizer (GWO), ant lioner optimizer (ALO), and nomadic people optimizer (NPO) [15]- [20]. NIAs have played a great role for solving different types of optimization problems, medical case studies [21]- [23], engineering [24]- [36], energy [37]- [48], and information security [49]- [51]. Salp swarm algorithm (SSA) is a recent nature inspired algorithm, which is inspired from the cylindrical jellyfishes-like creatures which are belong to Salpidae [52]- [55].…”

Section: Nature-inspired Algorithms and Salp Swam Algorithmmentioning

confidence: 99%

A missing data imputation method based on salp swarm algorithm for diabetes disease

Hassan

Ali²,

Abdulsahib

et al. 2023

Bulletin EEI

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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayesian classifier (NBC) have been enhanced as compared to the dataset before applying the proposed method. Moreover, the results indicated that issa was performed better than the statistical imputation techniques such as deleting the samples with missing values, replacing the missing values with zeros, mean, or random values.

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“…The last three decades have seen wide utilization of metaheuristics and nature-inspired algorithms for solving various kinds of NP optimization problems; such metaheuristics include firefly algorithm (FA), grey wolf optimizer (GWO), particle swarm optimization (PSO), nomadic people optimizer (NPO), and teaching-learning based optimization (TLBO) [11]- [14]. These algorithms were developed to handle problems in different engineering fields, information security, and machine learning [15]- [22].…”

Section: Introductionmentioning

confidence: 99%

An optimized deep learning model for optical character recognition applications

Salih

Khalaf

Mohsin

et al. 2023

IJECE

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The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recognition applications; the proposed method was evaluated for performance in terms of computational accuracy, convergence analysis, and cost.

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Chaotic Particle Swarm Optimization Based on Meeting Room Approach for Designing Bijective S-Boxes

Cited by 7 publications

References 30 publications

Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization

Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization

A missing data imputation method based on salp swarm algorithm for diabetes disease

An optimized deep learning model for optical character recognition applications

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