Can AI 'solve' politics? In this paper, I explore optipolitics, i.e., the idea that politics along with other complex social issues can be framed as mathematical optimization problems and solved as such. I beg...
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Almost all hydrological models require calibration. The same model but with different parameters may lead to diverse simulations of the hydrological phenomena. Hence, the choice of a calibration method may affect the ...
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Almost all hydrological models require calibration. The same model but with different parameters may lead to diverse simulations of the hydrological phenomena. Hence, the choice of a calibration method may affect the model performance. The present paper is the first study analyzing how the choice of air2water model calibration procedure may influence projections of surface water temperature in lowland lakes under future climatic conditions. To address this issue, projections from 14 atmospheric circulation models, data from 22 lowland Polish lakes located in a temperate climate zone, and 12 different optimization algorithms are employed. The studied lake areas range from 1.5 km2 to 115 km2, and their maximum depths range from 2.5 m to 70 m. Depending on which calibration algorithm is applied, the differences in mean monthly surface water temperatures projected for future climatic conditions may exceed 1.5 degrees C for a small deep lake. On the contrary, the differences observed for shallow and relatively large lakes, due to the optimization procedure used, were lower than 0.6 degrees C each month. The largest differences in projected lake water temperatures were observed for the winter and summer months, which are especially critical for aquatic biota. Among the optimization algorithms resulting in the largest differences were those that fit historical data well, as well as those that do not reproduce historical data appropriately. Therefore, strong performance for historical data does not guarantee reliable projections for future conditions. We have shown that projected lake water temperatures largely depend on the calibration method used for a particular model.
In recent years, the quest for optimizing metaheuristic algorithms has led to a surge in research efforts aimed at enhancing their performance. While existing reviews have diligently summarized these endeavors, they p...
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In recent years, the quest for optimizing metaheuristic algorithms has led to a surge in research efforts aimed at enhancing their performance. While existing reviews have diligently summarized these endeavors, they primarily focus on presenting the collective body of work undertaken to augment standard algorithms. In contrast, this paper takes a unique perspective by concentrating on the myriad methodologies employed by authors to improve one such algorithm, the Sine Cosine Algorithm (SCA). Our comprehensive review dissects the various strategies used to elevate the effectiveness of SCA variants, meticulously scrutinizing their advantages and disadvantages. This in-depth analysis extends beyond the confines of SCA and provides valuable insights into the broader landscape of metaheuristic optimization algorithms. By evaluating the pros and cons of these enhancement methods, our work forms a foundational review that can be applied to other optimization algorithms. Through this broader lens, we offer readers a comprehensive overview of the strategies adopted by researchers in recent years to enhance optimization algorithms, facilitating a deeper understanding of the advancement of this vital field. Our paper thus serves as a guidepost for researchers and practitioners navigating the ever-evolving terrain of metaheuristic optimization, shedding light on the strengths and potential pitfalls of enhancement methodologies. It provides a holistic perspective that empowers the community to make informed choices when selecting or devising strategies to optimize algorithms for diverse problem domains.
Basalt fibre has recently become a popular choice for concrete reinforcement due to its superior mechanical properties and sustainable production process. This research presents novel hybrid machine learning models fo...
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Basalt fibre has recently become a popular choice for concrete reinforcement due to its superior mechanical properties and sustainable production process. This research presents novel hybrid machine learning models for predicting the compressive strength (CS) and tensile strength (TS) of basalt fibre reinforced concrete (BFRC). The study integrates support vector regression (SVR) with firefly algorithm (FFA), grey wolf optimization (GWO), and particle swarm optimization (PSO) to develop hybrid models for forecasting BFRC properties. Random forest (RF) and decision tree (DT) were also employed for comparison. SVR-PSO exhibited the strongest performance, achieving the highest coefficient of determination (R2) scores of 0.993 for CS and 0.954 for TS, surpassing SVRFFA (CS = 0.990, TS = 0.944) and SVR-GWO (CS = 0.977, TS = 0.930). The RF model achieved R2 values of 0.974 for CS and 0.918 for TS, while the DT model had R2 values of 0.865 for CS and 0.897 for TS. SHapley Additive exPlanations (SHAP) analysis revealed the water-to-cement ratio (W/C) as the most critical feature for CS, while fine aggregate (FA) was most significant for TS. Partial dependence plots (PDP) analysis indicated FC and FA negatively affect CS, whereas FC and CA positively influence TS. A user-friendly graphical user interface was developed to streamline the prediction of CS and TS, crucial for ensuring the safety and stability of buildings and bridges. Future research should consider incorporating additional input features to enhance the accuracy of CS and TS predictions for BFRC. Expanding datasets is essential for the effective implementation of deep learning algorithms. The proposed hybrid models demonstrated high efficacy in predicting CS and TS, suggesting their potential application in estimating the durability characteristics of BFRC.
In this paper, a neurodynamic approach with communication delay is proposed for the sake of solving distributed optimization problems. First, the relationship between the equilibrium of the algorithm and the optimal s...
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Decomposition-based Many-Objective Evolutionary algorithms (MaOEAs) usually adopt a set of pre-defined distributed weight vectors to guide the solutions towards the Pareto optimal Front (PF). However, when solving Man...
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This paper simulates and optimises the movement process of the traditional folk cultural activity 'bench dragon' by means of a mathematical model to optimise its path and speed. Firstly, a pitch optimisation m...
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The traditional secretary bird optimization algorithm is known to exhibit certain deficiencies, including a slow convergence speed and a tendency to fall into local optimization. This paper presents an enhanced versio...
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The problems of complex mathematical models, high computational complexity, and repeated parameter adjustments in the subjective and objective weighting method is studied. Such problems are generally defined as multi-...
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This paper explores the possibility of designing an efficient global optimization algorithm using an artificial intelligence chatbot, ChatGPT. The main idea is to use the swarm intelligence metaheuristic method, which...
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