This paper proposes a hybrid metaheuristic Q-learning Runge–Kutta Optimization (QLRUN) for solving complex, high-dimensional, rotated, and constrained engineering optimization problems. The proposed framework integrates the gradient-guided search mechanism of the Runge–Kutta Optimization (RUN) algorithm with the adaptive decision-making capability of Q-learning to achieve dynamic regulation of exploration and exploitation behaviors during the optimization process. In QLRUN, the optimization state is characterized using convergence and population-diversity indicators, while the Q-learning agent adaptively selects search operators according to a fitness-improvement-driven reward policy that is iteratively updated throughout the search process. To further enhance rotational robustness and maintain population diversity, a lightweight Covariance Matrix Learning (CML) mechanism is incorporated to construct adaptive eigen-coordinate systems aligned with the fitness landscape's geometry. Unlike the full covariance adaptation employed in CMA-ES, the proposed CML strategy acts as an efficient coordinate-transformation mechanism with low computational overhead. The effectiveness of QLRUN is comprehensively validated using the 23 classic standards, the CEC2020, and CEC2022 benchmark suites, which include unimodal, multimodal, hybrid, composition, shifted, and rotated functions, as well as seven constrained engineering design problems. Comparative evaluations against the original RUN and nine state-of-the-art metaheuristics demonstrate that QLRUN consistently achieves competitive convergence accuracy, improved solution stability, and faster convergence characteristics. Quantitatively, QLRUN achieves the top Friedman mean rank across all three benchmark suites, attaining scores of 1.70 on 23 classic standards, 1.40 on CEC2020, and 1.17 on CEC2022, thereby substantially outperforming the original RUN and the competing optimization methods. Moreover, on constrained engineering design problems, QLRUN obtains highly competitive best-known solutions, including a pressure vessel design cost of 5.88536×103 and a tension/compression spring weight of 1.26660×10−2. Beyond p-value-based significance, practical superiority is further confirmed through consistent top-one Friedman rankings and Wilcoxon Rank-Sum win/tie/loss results against RUN of 17/5/1, 8/1/1, and 9/2/1 for the 23 classic standards, CEC2020, and CEC2022 benchmark suites, respectively. Statistical analyses using the Wilcoxon Rank-Sum, Friedman, ANOVA, and Kruskal–Wallis tests confirm the robustness and practical significance of the proposed approach. The results demonstrate that QLRUN constitutes a robust, computationally efficient optimization framework suitable for challenging applications in power systems, energy engineering, and multidisciplinary design optimization.
This paper introduces the use of modern metaheuristic optimization techniques: Gorilla Troops Optimizer (GTO), inspired by the social behavior of gorilla troops in the wild, and Artificial rabbits optimization (ARO), inspired by the survival strategies of rabbits in nature, including detour foraging and random hiding. These are combined to form a hybrid algorithm called hybrid Artificial rabbits Gorilla Troops Optimizer (ARGTO). The proposed ARGTO algorithm aims to reach exploration-exploitation balance to improve search efficiency. The presented algorithms were tested on seven mathematical optimization problems, and in order to make a more accurate comparison, the average optimization results and corresponding standard deviation results are calculated by running these algorithms 20 times for each optimization problem. A comparative analysis of the presented algorithms was conducted, showing that ARGTO achieved better performance. Initially, ARGTO was evaluated against its constituent algorithms, Artificial Rabbit Optimization (ARO) and Gorilla Troop Optimization (GTO). Subsequently, its efficacy was benchmarked against alternative optimization techniques, including Northern Goshawk Optimization (NGO), manta ray foraging optimization (MRFO), Dung Beetle Optimizer (DBO), and Runge Kutta optimizer (RUN). The principal aim of this research is to resolve power Economic Load Dispatch (ELD) problems, incorporating a comprehensive set of operational constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. Notably, the presented algorithm incorporates a self-adaptation mechanism, effectively mitigating the complexities associated with parameter tuning for diverse optimization problem characteristics. The efficacy of the ARGTO approach in addressing ELD with substantial nonlinearities is substantiated through experiments conducted on five well-established test power systems that comprise 6, 10, 11, 15, and 110 generation units. These results are compared with the outcomes produced by various other optimization methods proposed in recent literature. The performance of the ARGTO technique is demonstrated by its ability to minimize total costs, achieve rapid convergence, and maintain solution consistency.
The outstanding physicochemical properties of MoS2 make it an excellent candidate for energy conversion and optoelectronic applications. Despite extensive research on MoS2, there is no focus on the investigation of MoS2-based glasses. The main goal is the enhancement of electrical, optical, and photoconductive features of sodium phosphate glasses through the reinforcement of molybdenum disulfide. The amorphous nature of the synthesized MoS2-based phosphate glasses is confirmed by the XRD analysis. The optical absorption spectra demonstrate the promotion of non-bridging oxygens and reduction in optical band gap with the MoS2 addition. The dc conductivity exhibits a reducing trend with MoS2 addition, while the activation energy shows an increasing behavior. This indicates that the molybdenum ions cause a blocking effect by hindering the mobility of ions, reducing the conductivity. The photoconductivity and photosensitivity of MoS2-based glasses increase with increasing the concentration of MoS2 up to 4 mol
The performance of a solar photovoltaic (PV) module or array is significantly affected by partial shading conditions (PSC), which reduce the output power and shorten the lifespan of the PV system due to the formation of hotspots on the PV cells. These conditions lead to multiple peaks on the power-voltage (P–V) curve, which cannot be accurately tracked using classical maximum power point tracking (MPPT) methods. Consequently, metaheuristic-based optimization techniques are employed to locate the global maximum power point (GMPP). This paper addresses the challenges posed by PSC in a standalone PV system by implementing a recent optimization algorithm known as the tumoral angiogenesis optimizer (TAO). The proposed MPPT approach is validated under various partial shading scenarios and benchmarked against existing MPPT algorithms, including particle swarm optimization (PSO), horse herd optimization algorithm (HOA), salp swarm algorithm (SSA), and osprey optimization algorithm (OOA). Simulations are conducted using MATLAB R2021. The performance is evaluated in terms of output power, voltage, and duty cycle, demonstrating the TAO algorithm’s capability to rapidly and accurately track the GMPP. Moreover, the tracking efficiency, convergence time, and power oscillations of each MPPT method are analyzed. The proposed method achieves an average tracking efficiency of 99.5
Abstract Objectives This study aimed to characterize drug-resistant Streptococcus spp. isolated from food and human sources in Upper Egypt, focusing on their virulence and antimicrobial resistance profiles, and to evaluate the in vitro antibacterial activity of an oregano essential oil nanoemulsion. Materials and methods A total of 440 food and human samples were collected in Aswan, Upper Egypt, and screened using conventional microbiological methods. Molecular confirmation was performed by detecting the tuf gene, followed by species identification using the 16 S rRNA and spn9802 genes. Selected virulence (scpB, Rib, Lmb, and cylE) and resistance (aac6-aph2, pbp1A, and tetO) genes were detected by PCR. Antimicrobial susceptibility testing and the multiple antibiotic resistance (MAR) index were determined. The antibacterial activity of the oregano nanoemulsion was assessed using inhibition zone assays at varying concentrations. Results Streptococcus spp. were detected in 22% of samples, with higher prevalence in throat swabs (34%) and raw milk (21.9%). Predominant species included S. agalactiae, S. pyogenes, S. dysgalactiae, S. pneumoniae, and S. uberis. Virulence genes were variably distributed, with scpB (33%) and cylE (31.8%) most frequent. Resistance genes were moderately detected, particularly tetO (21.2%). Most isolates were resistant to β-lactams but fully susceptible to ceftaroline and fosfomycin; the mean MAR index was 0.477. The oregano nanoemulsion showed concentration-dependent antibacterial activity. Conclusion Drug-resistant Streptococcus spp. from food and human sources in Upper Egypt carry important virulence and resistance determinants. The oregano essential oil nanoemulsion demonstrated promising in vitro antibacterial activity, suggesting potential as a natural antimicrobial; however, further in vivo and safety studies are required.
