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.
Finite-dimensional degenerate optimization problems with equality and inequality constraints are addressed. One sufficient condition for quadraticity is proposed, that is, when the problem under study can be reduced t...
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Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the...
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Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution;as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is
The integration of distributed generation (DG) into the distribution system is a complex problem that requires an efficient optimization technique. To maximize benefits while minimizing disadvantages, it is crucial to...
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This paper proposes a constrained sparse planar array optimization method based on the Adaptive Genetic Algorithm (AGA). By optimizing the positions of array elements, the method effectively reduces the number of elem...
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This paper investigates projection-free algorithms for stochastic constrained multi-level optimization. In this context, the objective function is a nested composition of several smooth functions, and the decision set...
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This paper investigates projection-free algorithms for stochastic constrained multi-level optimization. In this context, the objective function is a nested composition of several smooth functions, and the decision set is closed and convex. Existing projection-free algorithms for solving this problem suffer from two limitations: 1) they solely focus on the gradient mapping criterion and fail to match the optimal sample complexities in unconstrained settings;2) their analysis is exclusively applicable to non-convex functions, without considering convex and strongly convex objectives. To address these issues, we introduce novel projection-free variance reduction algorithms and analyze their complexities under different criteria. For gradient mapping, our complexities improve existing results and match the optimal rates for unconstrained problems. For the widely-used Frank-Wolfe gap criterion, we provide theoretical guarantees that align with those for single-level problems. Additionally, by using a stage-wise adaptation, we further obtain complexities for convex and strongly convex functions. Finally, numerical experiments on different tasks demonstrate the effectiveness of our methods. Copyright 2024 by the author(s)
We propose a novel algorithm that extends the methods of ball smoothing and Gaussian smoothing for noisy derivative-free optimization by accounting for the heterogeneous curvature of the objective function. The algori...
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We propose a novel algorithm that extends the methods of ball smoothing and Gaussian smoothing for noisy derivative-free optimization by accounting for the heterogeneous curvature of the objective function. The algorithm dynamically adapts the shape of the smoothing kernel to approximate the Hessian of the objective function around a local optimum. This approach significantly reduces the error in estimating the gradient from noisy evaluations through sampling. We demonstrate the efficacy of our method through numerical experiments on artificial problems. Additionally, we show improved performance when tuning NP-hard combinatorial optimization solvers compared to existing state-of-the-art heuristic derivative-free and Bayesian optimization methods. Copyright 2024 by the author(s)
To tackle the shortcomings of the original WOA, including its sluggish convergence rate and tendency to approach local optima, an adaptive whale optimization algorithm (MWOA) combining chaotic maps and dynamic paramet...
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The research offers a unique coevolution-based many-objective optimization (MaOO) approach to benefit from the underlying parallelism of the evolutionary process. The proposed MaOO handles individual objectives in par...
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The goal of a generic evolutionary multi- or many-objective algorithm is to explore a search space and find the trade-off optimal solutions for two or more conflicting objectives. In platform-based practical design op...
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