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Sustainability goals include the utilization of renewable energy resources to supply the energy needs in addition to wastewater treatment to satisfy the water demand. Moreover, hydrogen has become a promising energy carrier and green fuel to decarbonize the industrial and transportation sectors. In this context, this research investigates a wind-photovoltaic power plant to produce green hydrogen for hydrogen refueling station and to operate an electrocoagulation water treatment unit in Ostrava, Czech Republic's northeast region. The study conducts a techno-economic analysis through HOMER Pro® software for optimal sizing of the power station components and to investigate the economic indices of the plant. The power station employs photovoltaic panels and wind turbines to supply the required electricity for electrolyzers and electrocoagulation reactors. As an off-grid system, lead acid batteries are utilized to store the surplus electricity. Wind speed and solar irradiation are the key role site dependent parameters that determine the cost of hydrogen, electricity, and wastewater treatment. The simulated model considers the capital, operating, and replacement costs for system components. In the proposed system, 240 kg of hydrogen as well as 720 kWh electrical energy are daily required for the hydrogen refueling station and the electrocoagulation unit, respectively. Accordingly, the power station annually generates 6,997,990 kWh of electrical energy in addition to 85595 kg of green hydrogen. Based on the economic analysis, the project's NPC is determined to be €5.49 M and the levelized cost of Hydrogen (LCH) is 2.89 €/kg excluding compressor unit costs. This value proves the effectiveness of this power system, which encourages the utilization of green hydrogen for fuel-cell electric vehicles (FCVs). Furthermore, emerging electrocoagulation studies produce hydrogen through wastewater treatment, increasing hydrogen production and lowering LCH. Therefore, this study is able to provide practicable methodology support for optimal sizing of the power station components, which is beneficial for industrialization and economic development as well as transition toward sustainability and autonomous energy systems.

This paper addresses the pressing concern of traffic safety by focusing on the optimization of vehicle cruise control systems. While traditional control techniques have been widely employed, their design procedures can be time-consuming and suboptimal. To overcome these limitations, metaheuristic algorithms have been introduced as promising solutions for complex optimization problems. In this study, an improved Runge Kutta optimizer (IRUN) is developed and applied to enhance the control performance of a real PID plus second-order derivative (RPIDD2) controller for vehicle cruise control systems. The IRUN optimizer incorporates advanced strategies such as quadratic interpolation, Laplacian segment mutation, Levy flight, and information-sharing-based local search mechanisms. By integrating these strategies, the IRUN algorithm demonstrates enhanced optimization capabilities, making it well-suited for tuning the controller. The proposed approach utilizes a master–slave system, where the ideal reference model sets the desired response and the RPIDD2 controller adjusts its parameters accordingly. The integral of the square error is employed as the objective function to evaluate the control system's performance. Statistical analyses, convergence analyses, and stability evaluations and robustness analysis are performed to demonstrate the effectiveness of the IRUN-based RPIDD2 controller. Comparative studies are conducted against established approaches using PID, fractional-order PID (FOPID), and RPIDD2 controllers, showcasing the superiority and effectiveness of the proposed approach. Overall, this paper presents a comprehensive study on enhancing the time-domain performance and stability of vehicle cruise control systems, providing significant improvements in control accuracy and efficiency. The subsequent sections delve into the proposed approach, experimental setup, and obtained results, further emphasizing the significance and potential impact of this research.

Abstract In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.

The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles.

Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parameters such as voltage regulation, response time, stability, and efficiency. Integrating controllers with AVRs offers centralized monitoring and regulation, enhancing voltage output efficiency. This study therefore first discusses various controller types which showcase distinctive capabilities based on their tuning strategies and then introduces an intelligent optimization approach using fractional-order proportional-integral-derivative with double derivative (FOPIDD2) controller tailored for AVRs. The study adopts the artificial rabbits optimization (ARO) algorithm, fortified with an adaptive local search (ALS) mechanism and experience-based perturbed learning (EPL) strategy. The modified version (m-ARO) enhances solution diversity and navigational efficacy, resulting in improved optimization quality. The study's core objective is to demonstrate the superior performance of the m-ARO-based FOPIDD2 controller in addressing the multifaceted challenges of AVR control, outperforming conventional techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with different tuning algorithms. Results indicate that the proposed m-ARO-based FOPIDD2 controller achieves superior performance metrics, demonstrating its capability. The study also includes a wider perspective by considering nineteen different controllers, reported in the literature, for comprehensive comparison. Notably, the proposed method exhibits the best stability, further affirming its effectiveness.

Abstract In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.

Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.

Photoperiod and thermosensitive genetic male sterile (PTGMS) lines have become one of the main sources of global rice production increasing. This study was conducted to evaluate the fertility alteration and validate the male sterility genes using validation markers in novel Egyptian Indica and Japonica PTGMS lines under natural conditions. The study revealed that the new genetic male sterile lines belong to the type of photo–thermosensitive genetic male sterility (PTGMS). The fertility alteration of these lines has influenced by photoperiod and temperature interaction. The new PTGMS lines have three sensitive periods of fertility alteration; transformation, sterility, and fertility period. Furthermore, the sensitive stage of fertility transformation might be from secondary branch primordial to pollen mother cells (PMC) meiosis. Under the natural Sakha condition, the new PTGMS lines were stable sterile under the condition of day length upper 13,75 h and temperature over 25 °C, while its convert to fertile under day length under 13 h, and temperature lower than 24 °C. The co-dominant markers identified the pms3 and tms5 genes in the new PTGMS lines, indicated that the fertility alteration in these lines controlled by photoperiod and thermosensitive stages.

Hybrid dysprosium-doped borate glassy samples [B-Gly/Dy]HDG (Borate Glass/Dysprosium)Hybrid Doped Glass were prepared in this study via the melt-quenching method. Its linear/nonlinear optical, photoluminescence, hardness indentation, and micro-creep properties were analyzed. The amorphous structure for all the prepared samples was confirmed from the XRD patterns. In addition, density functional theory (DFT), optimized by TD-DFT and Crystal Sleuth, was used to study the structure and crystallinity of the [B-Gly/Dy]HDG as isolated molecules and agreed with the peaks of experimental XRD patterns. Additionally, theoretical lattice types were studied using Polymorph, a content studio software, and orthorhombic Pc21b (29) and triclinic P-1 (2) structures were provided. Both mechanical and optical properties were responses to different concentrations of Dy2O3 in the glassy borate system. It was found that the length of indentation increases by increasing the load time, and the hardness decreases by increasing the load time. The stress exponent value also increased from 4.1 to 6.3. The indentation strain increases by increasing the load time. The direct optical band gap was evaluated using the Davis–Mott relation. Urbach energy and its connection to the disorder degree in materials were studied depending on the Dy2O3 concentration. The acquired optical parameters were also analyzed to determine the nonlinear refractive index as well as the linear and third-order nonlinear optical susceptibility of the investigated glass samples. The photoluminescence emission spectra were recorded, and their attributed transitions were studied. The mechanical studies showed that the hardness values increased by increasing Dy2O3 concentrations from 4160.54 to 5631.58 Mpa. The stress exponent value also increased from 4.1 to 6.3. Therefore, the higher value of stress exponent (S) is more resistant to indentation creep.

Identifying the parameters of photovoltaic (PV) modules is significant for their design and simulation. Because of the instabilities in the weather action and land surface of the earth, which cause errors in measuring, a novel fuzzy represen-tation-based PV module is formulated and developed. In this paper, a novel locomotion-based hybrid salp swarm algorithm (LHSSA) is presented to identify the parameters of PV modules accurately and reliably. In the LHSSA, better leader salps based on particle swarm optimization (PSO) are incorporated to the traditional salp swarm algorithm (SSA) in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA. By this integration, the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region. The proposed LHSSA is investigated on different PV models, i. e., single-diode (SD), double-diode (DD), and PV module in crisp and fuzzy aspects. By comparing with different algorithms, the comprehensive results affirm that the LHSSA can achieve a highly competitive performance, especially on quality and reliability.

Motorcycles are a common mode of transport. They are used both for a conventional purpose - as a way to cover the distance from A to B, but more and more often they are a way of recreation. The choice of bikes is huge on the market: from scooters, threw small city bikes, to high-speed performance or racing machines. High speeds is closely related to high kinetic energy of movement. In case of emergency stopping, the brakes may overheat and cause fading. In this study, it was decided to check how the initial speed affects the motorcycle brake heating process. FEM was used for this purpose.

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.

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.

Hybrid rice parental lines with better combining abilities provide an efficient tool to increase rice production. In the current study, twenty hybrid combinations were generated from five aromatic cytoplasmic male sterile (CMS) lines and four restorer lines (three of them aromatic) using a line × tester mating design. The hybrids and their parental lines were evaluated under two water regimes: normal irrigation and water-stress. Ten yield-component traits were studied over a period of 2 years, and the significant differences between the parents and hybrids are reported in this investigation. Overall, all yield component traits were significantly affected by the water deficit and were governed by both additive and non-additive gene actions. More specifically, the grain yield (GY) was mainly controlled by non-additive gene action under both normal and water-stress conditions. The contribution of the additive variance (σ2 A) was more prominent in the genetic components of traits as compared to the dominance variance (σ2 D). The aromatic parental line CMS IR58025A and the restorer line PR2 were recorded as the best combiners for the GY and good combiners for many other characteristics under both growth conditions. The cross combinations Pusa12A/IR25571-31R and Pusa12A/Giza-Basmati-201 revealed significantly positive specific combining ability (SCA) effects for the GY under both normal and water-stress conditions. The inconsistent correlation between the general combining ability (GCA) and SCA manifested complex interactions among the positive and negative alleles of the genes controlling the yield traits. Generally, the findings of this investigation demonstrated the importance of the GCA and SCA for understanding the genetic components and gene actions of the yield characteristics in new aromatic hybrid rice parental lines. Therefore, we recommend considering these findings in the selection of elite parents for developing superior aromatic hybrid rice varieties under water-stress conditions.