Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

A hybrid equilibrium algorithm and pattern search technique for wind farm layout optimization problem.

The layout optimization-based model is a significant issue for increasing the utilization rate of the wind farm and minimizing its cost per unit of power. For the accurate and reliable wind farm layout optimization design, a novel algorithm based on the hybridization of equilibrium optimizer (EO) and pattern search (PS) technique, named EO-PS, is proposed in this paper. The proposed EO-PS operates in two phases. The first phase implements the EO to explore the search space and reach the promising regions by using an equilibrium pool of elite particles, which contributes to maintaining the diversity of solutions. The second phase integrates the PS to guide the searching towards better vicinities and achieve a high-quality solution by using its detecting and pattern movements to boost the exploitation ability of the proposed method in the last steps. The presented EO-PS algorithm is implemented to deal with single and multi-objective optimization aspects of wind farm layout optimization using different wind speed scenarios. Furthermore, the proposed algorithm is investigated on irregular land space in the Gulf of Suez-Red Sea in Egypt to achieve the optimal layout configuration of the wind farm, which is vital for possible practical planning trends. The comprehensive results and analyses have affirmed that the proposed EO-PS can achieve competitive performance compared to the other state-of-the-state methods, especially in terms of the solution' quality and reliability.

A Novel Binary Hybrid PSO-EO Algorithm for Cryptanalysis of Internal State of RC4 Cipher.

Cryptography protects privacy and confidentiality. So, it is necessary to guarantee that the ciphers used are secure and cryptanalysis-resistant. In this paper, a new state recovery attack against the RC4 stream cipher is revealed. A plaintext attack is used in which the attacker has both the plaintext and the ciphertext, so they can calculate the keystream and reveal the cipher's internal state. To increase the quality of answers to practical and recent real-world global optimization difficulties, researchers are increasingly combining two or more variations. PSO and EO are combined in a hybrid PSOEO in an uncertain environment. We may also convert this method to its binary form to cryptanalyze the internal state of the RC4 cipher. When solving the cryptanalysis issue with HBPSOEO, we discover that it is more accurate and quicker than utilizing both PSO and EO independently. Experiments reveal that our proposed fitness function, in combination with HBPSOEO, requires checking 104 possible internal states; however, brute force attacks require checking 2128 states.

Chaos-opposition-enhanced slime mould algorithm for minimizing the cost of energy for the wind turbines on high-altitude sites.

This paper presents a chaos-opposition-enhanced slime mould algorithm (CO-SMA) to minimize energy (COE) cost for the wind turbines on high-altitude sites. The COE model is established based on rotor radius, rated power, and hub height needed to achieve an optimal design model. In this context, an improved variant of SMA, named CO-SMA, is proposed based on a chaotic search strategy (CSS) and crossover-opposition strategy (COS) to cope with the potential weaknesses classical SMA while dealing with nonlinear tasks. First, the COS is introduced to enhance the diversity of solutions and thus improves the exploratory tendencies. The CSS is incorporated into the basic SMA to improve the exploitative abilities and thus avoids the premature convergence dilemma. The proposed CO-SMA is validated on the design of wind turbines with high-altitude sites. Furthermore, the sensitivity analysis based on the Taguchi method is developed to exhibit the impact of the COE model's optimized parameters. The influence of uncertainty based on the fuzziness scheme of wind resource statistics is also explored to depict a real scheme for the changes that occurred by seasonal time, atmospheric conditions, and topographic conditions. The proposed CO-SMA is compared with the PSO, WOA, GWO, MDWA, and SMA, where the COE values are recorded as 0.052408, 0.052462, 0.052435, 0.052409, 0.052413, and 0.052915, respectively. Furthermore, the proposed CO-SMA records the faster convergence than the others. On the other hand, the Taguchi method reveals that the rated power is the most significant parameter on the COE model. Also, the impact of the fuzziness scheme on COE is exhibited, where the increasing interval of vagueness can increase the value of COE.