Transformers are crucial and expensive assets of power grids. Reducing power losses in power and distribution transformers is important because it increases the efficiency of the transformer, which in turn reduces the...
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Transformers are crucial and expensive assets of power grids. Reducing power losses in power and distribution transformers is important because it increases the efficiency of the transformer, which in turn reduces the costs for the utility company and consumers. Losses in the transformer generate heat, which can reduce the lifespan of the transformer and require additional cooling. Additionally, reducing losses can help to decrease greenhouse gas emissions associated with the generation of electricity. This study presents an optimization method for transformer design problem using variables that have a great impact on the performance of a transformer. Due to the non-convex nature of the transformer design problems, the empirical methods fail to find the optimal solution and the design process is very tedious and time-consuming. Considering No Free Lunch (NFL) theorem, the design problem is solved using four novel heuristic optimization algorithms, the Firefly Optimization Algorithm (FA), Arithmetic Optimization Algorithm (AOA), Grey Wolf Optimization Algorithm (GWO), and Artificial Gorilla Troops Optimizer Algorithm (GTO) and the results are compared to an already manufactured 1000 kVA eco-friendly distribution transformer using the empirical methods. The outcome of the optimization shows that the suggested method along with the algorithms mentioned leads to a notable decrease in power losses by up to 3.5%, and a reduction in transformer weight by up to 8.3%. This leads to an increase in efficiency, decreased costs for materials, longer lifespan and a reduction in emissions. The developed model is capable of optimally designing oil-immersed distribution transformers with different power ratings and voltage levels.
Optimizing reaction conditions to improve the yield is fundamental for chemical synthesis and industrial processes. Experiments can only be performed under a small portion of reaction conditions for a system, so a str...
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Optimizing reaction conditions to improve the yield is fundamental for chemical synthesis and industrial processes. Experiments can only be performed under a small portion of reaction conditions for a system, so a strategy of experimental design is required. Bayesian optimization, a global optimization algorithm, was found to outperform human decision-making in reaction optimization. Similarly, heuristic algorithms also have the potential to solve optimization problems. In this work, we optimize these reaction conditions for Buchwald-Hartwig and Suzuki systems by predicting reaction yields with three heuristic algorithms and three encoding methods. Our results demonstrate that particle swarm optimization with numerical encoding is better than the genetic algorithm or simulated annealing. Moreover, its performance is comparable to Bayesian optimization without the computational costs of descriptors. Particle swarm optimization is simple and easy to perform, and it can be implemented into laboratory practice to promote chemical synthesis.
This article presents an ER-based PEM strategy for PV integrated smart homes to jointly optimize their load scheduling delays, energy transactions cost, and battery degradation cost. The proposed approach incorporates...
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This article presents an ER-based PEM strategy for PV integrated smart homes to jointly optimize their load scheduling delays, energy transactions cost, and battery degradation cost. The proposed approach incorporates a MA case, where, the ER acts as a main selecting agent realized by all other system elements. This leads to a combinatorial optimization problem, which can be effectively solved by heuristic optimization methods (HOMs), namely, genetic algorithm (GA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and harmony search algorithm (HSA). Specifically, we investigate the impact of the hyperparameters of the HOMs on the designed ER-based PEM system. Simulations are carried out for multiple smart homes under varying weather conditions to evaluate the effectiveness of HOMs in terms of selected performance metrics. Results show that the ER-based PEM reduces the average aggregated system cost, ensures economic benefits by selling surplus energy, while meeting customers energy packet demand, satisfying their quality-of-service, and operational constraints.
The safe operation of electrical devices is related to the national economy and people's safe and well-being. It is crucial for operators to correctly and quickly identify interface information. To achieve this go...
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The safe operation of electrical devices is related to the national economy and people's safe and well-being. It is crucial for operators to correctly and quickly identify interface information. To achieve this goal, optimizing the facility layout to reduce operator cognitive load and improve layout usability are vital prerequisites. Studies have developed methods for arranging interface elements, but their evaluations have rarely focused on the usability verification of optimized schemes;hence, the optimal effect has been uncertain in real situations. Given these considerations, this study proposed a new method that combines heuristic algorithms (genetic algorithm [GA] and ant colony algorithm [ACA]) and eye movements to obtain optimal interfaces. The optimized mathematical model was constructed using the Delphi method and analytic hierarchy process. A comparative study found that the GA-based interface (GABI) achieved superior results to the ACA-based interface (ACABI). Furthermore, the eye movement results indicated that compared with the original interface (OI) of the armored exhaust AC metal-enclosed switchgear, both algorithm-based interfaces significantly reduced the cognitive load and improved the overall usability. The results demonstrated that the GABI was superior to the ACABI overall. Therefore, the method proposed in this study can obtain better applicable schemes that account for both ergonomic requirements and user experience, thereby facilitating the convenient production of more effective layout schemes and providing a reference for electrical device designers and practitioners.
The geometric version of the traveling salesman problem (TSP) has been extensively studied, leading to the development of various approaches for solving its special cases. However, these algorithms often fall short wh...
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The geometric version of the traveling salesman problem (TSP) has been extensively studied, leading to the development of various approaches for solving its special cases. However, these algorithms often fall short when applied to problems beyond the geometric TSP. In this paper, we explore the pseudo-geometric TSP version, a generalization of the geometric TSP, and propose an adapted geometric algorithm for solving its specific instances. We leverage the knowledge of error bounds to estimate the reconstruction error of the TSP solution even when using geometric approaches for the pseudo-geometric TSP. This allows us to achieve reliable results despite uncertainties or noise in the data. We provide a concise description of our algorithmic adaptation and present the results of computational experiments to demonstrate its effectiveness.
Current security challenges are made more difficult by the complexity and diffi-culty of spotting cyberattacks due to the Internet of Things explosive growth in connected devices and apps. Therefore, various sophistic...
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In this paper, we propose some heuristic probabilistic polynomial time algorithms with one-sided error for recognition of cubic hypersurfaces the singular loci of which do not contain any linear subspace of sufficient...
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In this paper, we propose some heuristic probabilistic polynomial time algorithms with one-sided error for recognition of cubic hypersurfaces the singular loci of which do not contain any linear subspace of sufficiently large dimension. These algorithms are easy to implement in computer algebra systems. The algorithms are based on checking the condition that the Hessian determinant of a cubic form does not vanish identically or does not determine any cone in the projective space. In turn, the properties of the Hessian can be verified with one-sided-error probabilistic algorithms based on the Schwartz-Zippel lemma.
Enhanced index tracking problem (EITP) aims to add sustainable value to portfolio management by emulating the behavior of the benchmark index while limiting the number of assets it holds. There have been numerous ...
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This research utilizes machine learning techniques, particularly Random Forest, to examine the impact of mental stress on nurses. Stress, arising from challenging events or demanding conditions (stressors), poses sign...
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The hybrid renewable energy system (HRES) has been presented as the most studied solution for improving the sustainability of energy production infrastructures in isolated areas. With the rapid growth of HRES markets,...
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The hybrid renewable energy system (HRES) has been presented as the most studied solution for improving the sustainability of energy production infrastructures in isolated areas. With the rapid growth of HRES markets, various issues and aspects must be taken into consideration when the major working about the hybridization of renewable energy sources, consequently optimization problem solving for this system is a requirement. Therefore, this paper presents a state-of-the-art review of hybrid meta-heuristic algorithms applied for the optimal size of HRES. The relevant literature source and their distribution are presented firstly. We then review the literature from two viewpoints, including existing applied hybrid meta-heuristic algorithms for single-objective and for multi-objective design. Finally, some promising paths ranging from improving algorithms to technical applications are outlined to encourage researchers to conduct research in related fields.
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