The United States presidential elections receive a substantial attention not only from American voters, but also from news agencies, politicians, and international governments due to the local and global impact of the outcome. Therefore, different parties strive to predict the election’s results ahead of time, and opinion polls remain the predominant prediction method despite their bias and flaws. Online political communication has immensely evolved in recent years, especially on social media websites like Reddit, which has become a key platform in political discourse offering a valuable resource for studying public opinions on key issues. This study aims to utilize advanced machine learning methods to predict the outcome of the upcoming 2024 U.S. presidential election with a focus on the two primary candidates, former President Trump and Vice President Harris. Employing deep learning techniques to analyze more than 25 thousand online posts on Reddit, the results indicate that on the national level, Harris has more favorable sentiment in comparison to Trump among online users. However, analyzing the data associated with the battleground states, our model predicts that Trump has an edge over Harris, which may result in Trump winning the majority of the electoral votes in these states. This study underscores the importance of integrating social media data with machine learning capabilities for enhanced data-driven forecasts in upcoming elections and major public events.

Today, reliable power delivery and increasing demand are important issues in modern power systems. Flexible Alternating Current Transmission Systems (FACTS) devices are used to control transmission line parameters to increase power transfer and stability. Nevertheless, the problem of determining the optimal placement and sizing of these devices is still challenging, as the placement and sizing of the devices affects generation costs, power losses, voltage stability, and system reliability. This study proposes the Fata Morgana Algorithm (FATA), an optimization algorithm inspired by the natural process of mirage formation to optimize placement and sizing of FACTS devices in an IEEE 30 bus system with wind turbine integration. The FATA algorithm is evaluated against recently developed and improved optimization techniques, such as rime-ice formation phenomenon based Improved RIME (IRIME) Algorithm, Newton–Raphson-Based Optimization (NRBO), Resistance Capacitance Algorithm (RCA), Krill Optimization Algorithm (KOA), and Grey Wolf Optimizer (GWO), across multiple optimization objectives: reduction in generation cost, reduction in power loss and combined generation cost plus power loss, termed as Gross cost function. Results obtained show that FATA consistently outperforms the other algorithms in terms of convergence and solution quality, offering a robust approach to solving single objective optimization problems. FATA theoretically provides a good balance between exploration and exploitation, and produces better global solutions. It practically increases power system efficiency by lowering operational costs and losses and improving stability. Results indicate that the FATA algorithm produced minimum generation cost of 807.0405 $/h, which is 0.088–0.426% less than the competing algorithms. It also reduced power losses to 5.5917 MW, which is 1.095–6.781% less than other methods. For gross cost minimization, the FATA algorithm achieved a minimum gross cost of 1366.3727 $/h, which is 0.4799% better than the next best algorithm and 3.2261% better than the worst. The results also show that FATA is robust in solving complex optimization problems in power systems, and it provides significant improvements in run time and convergence efficiency. The main advantage for readers is that FATA provides a reliable and efficient way to optimize power systems. Future work could also investigate the application of FATA in real time, as well as in larger power networks with more renewable energy sources.

This paper presents a novel hybrid metaheuristic, the Hybrid Coati Optimization Algorithm with Differential Evolution (HCOADE), developed to address complex global optimization tasks and constrained engineering design problems. HCOADE integrates the exploration-driven behaviour of the Coati Optimization Algorithm (COA)-inspired by the social foraging and predation strategies of coatis-with the powerful mutation and crossover mechanisms of Differential Evolution (DE), thereby achieving a balanced and adaptive search process. The hybridization enhances global exploration and local exploitation, enabling the algorithm to efficiently navigate diverse and challenging optimization landscapes. To rigorously evaluate its performance, HCOADE is tested on benchmark suites from CEC 2014, 2017, 2020, and 2022, which encompass unimodal, multimodal, hybrid, and composition functions. It is also applied to real-world constrained engineering problems, such as pressure vessel design, cantilever beam optimization, and reinforced concrete beam design. Comparative experiments against state-of-the-art algorithms—including COA, DE, RSA, PSO, SSA, BBO, QIO, DMOA, and others—demonstrate that HCOADE consistently delivers superior solution quality, faster convergence, and higher robustness. Quantitative results show that HCOADE achieved the 1st place average rank across all four benchmark suites. It obtained top performance on 80% of the functions in CEC 2014, 66.7% in CEC 2017, 70% in CEC 2020, and 66.7% in CEC 2022. Furthermore, HCOADE outperformed or matched CEC competition-winning algorithms such as LSHADE-cnEpSin, LSHADE-SPACMA, and CMA-ES on numerous challenging functions of CEC 2017. Statistical analyses, including Wilcoxon Rank Sum Tests and ranking evaluations, confirm the significance and reliability of HCOADE’s performance. Furthermore, convergence behaviour, measurement of exploration and exploitation, and sensitivity analysis highlight the algorithm’s adaptability and stability across varied problem domains. This study contributes a computationally efficient and generalizable hybrid optimization framework, offering a promising solution for theoretical benchmarks and real-world engineering applications.