A image encryption algorithm based on chaotic Lorenz system and novel primitive polynomial S-boxes by Temadher Alassiry Al-Maadeed (14147847)

“…<p>We have proposed a robust, secure and efficient image encryption algorithm based on chaotic maps and algebraic structure. …”

A Novel Encryption Method for Dorsal Hand Vein Images on a Microcomputer by M. Z. Yildiz (16855476)

“…Second, the pre- and post-processed images were encrypted with a new encryption algorithm in the microcomputer environment. …”

A New Parallel Genetic Algorithm Model by Al-Somani, Turki F.

“…This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. …”
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A novel encryption algorithm using multiple semifield S-boxes based on permutation of symmetric group by Iqtadar Hussain (14147850)

“…To ensure the appropriate utilization of these chaotic maps, we carry out in-depth analyses of their behavior in the context of secure communication and apply the pseudo-random sequences of chaotic maps in the proposed image encryption algorithm accordingly.</p><h2>Other Information</h2><p dir="ltr">Published in: Computational and Applied Mathematics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s40314-023-02208-x" target="_blank">https://dx.doi.org/10.1007/s40314-023-02208-x</a></p>…”

A new genetic algorithm approach for unit commitment by Mantawy, A.H.

“…This paper presents a new genetic algorithm approach to solve the unit commitment problem in electric power systems. …”
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Genetic Algorithm Based Simultaneous Eigenvalue Placement Of Power Systems by Abdel Magid, Y.L.

“…The task of selecting the output feedback gains is converted to a simple optimization problem with an eigenvaluebased objective function, which is solved by a genetic algorithm. An objective function is presented allowing the selection of the output feedback gains to place the closed-loop eigenvalues in the left-hand side of a vertical line in the complex s-plane while shifting a specific mode of oscillation to a vertical strip and with bounds on the damping ratio. …”
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A genetic-based algorithm for fuzzy unit commitment model by Mantawy, A.H.

“…The model takes the uncertainties in the forecasted load demand and the spinning reserve constraints in a fuzzy frame. The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. …”
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Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach by Sarif, Bambang

“…The search space is too large to be explored by deterministic algorithms. In this paper, a Genetic Algorithm based algorithm for synthesis of MVL functions is proposed. …”
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Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem by Mantawy, A.H.

“…The core of the proposed algorithm is based on genetic algorithms. …”
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A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models by Azar, Danielle

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Design of PSS and STATCOM-based damping stabilizers using genetic algorithms by Abido, M.A.

“…The design problem of STATCOM-based stabilizers is formulated as an optimization problem. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. …”
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Integrating Genetic Algorithms, Tabu Search, And Simulated Annealing For The Unit Commitment Problem by Montawy, A.H.

“…The core of the proposed algorithm is based on genetic algorithms. …”
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On-site workshop investment problem: A novel mathematical approach and solution procedure by Nima Moradi (19418821)

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Robust Coordinated Design of Excitation and TCSC-Based Stabilizers Using genetic algorithms by Abdel-Magid, Y. L.

“…The coordinated design problem of robust excitation and TCSC-based controllers over a wide range of loading conditions and system configurations is formulated as an optimization problem with an eigenvalue-based objective function. The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. …”
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Optimal multiobjective design of robust power system stabilizers using genetic algorithms by Abdel-Magid, Y.L.

“…A multiobjective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The problem of robustly selecting the parameters of the power system stabilizers is converted to an optimization problem which is solved by a genetic algorithm with the eigenvalue-based multiobjective function. …”
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A New Genetic-Based Tabu Search Algorithm For Unit Commitment Problem by Mantawy, A. H.

“…In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. …”
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Power-constrained system-on-a-chip test scheduling using a genetic algorithm by Harmanani, Haidar M.

“…This paper presents an efficient approach for the test scheduling problem of core-based systems based on a genetic algorithm. …”
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Optimizing Energy Consumption In Cloud Datacenters by Khan, Mueez Ahmad

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Logistics Optimization Using Hybrid Genetic Algorithm (HGA): A Solution to the Vehicle Routing Problem With Time Windows (VRPTW) by Ayesha Maroof (17984053)

“…This research introduces a cutting-edge Hybrid Genetic Algorithm-Solomon Insertion Heuristic (HGA-SIH) solution, reinforced by the powerful Solomon Insertion constructive heuristic to solve the VRPTW as an NP-hard problem. …”

Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars by Abathar Al-Hamrani (16494884)

“…Finally, a design equation that can predict the shear capacity of one-way BFRC-BFRP slabs was proposed based on genetic algorithm. The proposed model showed the best prediction accuracy compared to the available design codes and guidelines with a mean of predicted to experimental shear capacities (V_pred/V_exp) ratio of 0.97 and a coefficient of variation of 17.91%.…”