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High-performance concrete (HPC) is commonly utilized in the construction industry because of its strength and durability. The mechanical properties of HPC, specifically its compressive and tensile strength, are crucial indicators. Accurate prediction of concrete strength is crucial for optimizing the design as well as the performance of concrete structures. In this investigation, a novel approach for strength prediction of HPC is proposed, employing the Support Vector Regression (SVR) algorithm in conjunction with three optimizers: the Slime Mold Algorithm (SMA), Adaptive Opposition Slime Mold Algorithm (AOSM), and Equilibrium Slime Mold Algorithm (ESMA). The SVR algorithm is a robust machine-learning technique that has displayed promising results in various prediction tasks. The utilization of SVR allows for the effective modeling and prediction of the complex relationship between the strength properties of HPC and the influencing factors. To achieve this, a dataset comprising 344 samples of high-performance concrete was collected and utilized to train and assess the SVR algorithm. However, the choice of suitable optimization algorithms becomes crucial to enhance prediction accuracy and convergence speed. Through extensive experimentation and comparative analysis, the proposed framework’s performance is evaluated using real-world HPC strength data. The results demonstrate that combining SVR with AOSM, ESMA, and SMA outperforms traditional prediction accuracy and convergence speed optimization methods. The suggested framework provides an effective and reliable solution for accurately predicting the compressive strength (CS) of HPC, enabling engineers and researchers to optimize the design and construction processes of HPC structures.

This paper introduces the chaotic enhanced leader slime mold algorithm (CELSMA), an advanced bio-inspired optimization technique aimed at addressing high-dimensional engineering challenges. Building on the traditional slime mold algorithm (SMA), CELSMA implements a multi-leader strategy that utilizes three candidate leaders to enhance both exploration and exploitation capabilities. Additionally, CELSMA harnesses the ergodic and non-repetitive characteristics of chaotic maps to improve global search behavior and reduce the risk of premature convergence to local optima. The proposed algorithm is applied to the size optimization of truss structures under frequency constraints, a computationally intensive task due to the repeated evaluation of structural eigenvalues. To tackle this issue, the largest eigenvalues of sparse matrix (LESM) technique is employed to significantly decrease computational time, facilitating the practical optimization of large-scale truss systems. Comprehensive numerical experiments were conducted on large-scale dome truss structures and benchmarked against established metaheuristic algorithms. The results clearly indicate that the CELSMA-LESM approach achieves superior accuracy and convergence speed, consistently yielding optimal solutions with fewer iterations. The CELSMA source code is publicly available at: https://github.com/nut123456/CELSMA.git .

In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solution. The optimization performance of the proposed DESMAOA was analyzed by using 23 classical benchmark functions. Firstly, the impacts of different components are discussed. Then, the exploitation and exploration capabilities, convergence behaviors, and performances are evaluated in detail. Cases at different dimensions also were investigated. Compared with the SMA, AOA, and another five well-known optimization algorithms, the results showed that the proposed method can outperform other optimization algorithms with high superiority. Finally, three classical engineering design problems were employed to illustrate the capability of the proposed algorithm for solving the practical problems. The results also indicate that the DESMAOA has very promising performance when solving these problems.

The slime mold algorithm (SMA) is applied to optimize the allocation of water resources in Wuzhi. The cost of using mathematical methods to optimize an engineered water allocation problem is enormous, and heuristic algorithms have become reliable and effective optimization tools. In this study, a multi-objective water resources optimal allocation model integrating social, economic and environmental objectives is constructed for the study area, and SMA equipped with fast convergence and accurate search is applied to optimize the problem. Water allocation schemes for the region in 2025 and 2030 were obtained, and the distribution results were independently analyzed from both the demand and supply sides. The results show that the total water distribution in 2025 and 2030 are about 323 million m 3 and 346 million m 3 , and the water deficit ratios are 2.90% and 6.95%, respectively. From the perspective of regional development, the water dispatched in the region still is less than the water demand and the optimized water resource allocation plan can guide the development of the region.

Aviation engines, as vital aircraft components, encounter challenges in Condition Monitoring (CM) signal fault diagnosis, including low accuracy and poor real-time performance. To tackle these, by integrating an Auto-Encoder (AE) and Bidirectional Gated Recurrent Unit (BiGRU), this study proposes an AE-BiGRU-based fault signal feature extraction model for aviation engines. Then, an ISMA-SVM-based aviation engine CM signal fault diagnosis method is introduced, employing the Improved Slime Mould Algorithm (ISMA) to optimize Support Vector Machine (SVM) parameters. The findings reveal that in function optimization, the ISMA exhibits stronger variance reduction in both unimodal and multimodal functions, particularly in the latter. In terms of fault diagnosis, the model performs excellently in precision (0.90), recall (0.95), and F1 score (0.92), achieving the best performance in the C-MAPSS dataset prediction. Case applications show their advantages in extracting weak fault signals and identifying fault frequencies, enhancing aviation engine fault diagnosis, safeguarding health status, and aiding damage repair.

Crude oil is the most important energy source in the world, and fluctuations in oil prices can significantly influence investors, companies, and governments. However, crude oil prices have numerous characteristics, including randomness, sudden structural changes, intrinsic nonlinearity, volatility, and chaotic nature. This makes the accurate forecasting of crude oil prices a difficult and challenging task. In this paper, a hybrid prediction model for crude oil futures prices is proposed, the accuracy and robustness of which are demonstrated via controlled experiments and sensitivity analysis. This study uses a new data denoising method for data processing to improve the accuracy and stability of the predictions of crude oil prices. Furthermore, the chaotic time-series prediction method, shallow neural networks, linear model prediction methods, and deep learning methods are adopted as submodels. The results of interval forecasts with narrow widths and high prediction accuracies are derived by introducing a confidence interval adjustment coefficient. The results of the simulation experiments indicate that the proposed hybrid prediction model exhibits higher accuracy and efficiency, as well as better robustness of the forecasting than the control models. In summary, the proposed forecasting framework can derive accurate point and interval forecasts and provide a valuable reference for the price forecasting of crude oil futures.

Slime mold algorithm (SMA) is a recently developed meta-heuristic algorithm that mimics the ability of a single-cell organism (slime mold) for finding the shortest paths between food centers to search or explore a better solution. It is noticed that entrapment in local minima is the most common problem of these meta-heuristic algorithms. Thus, to further enhance the exploitation phase of SMA, this paper introduces a novel chaotic algorithm in which sinusoidal chaotic function has been combined with the basic SMA. The resultant chaotic slime mold algorithm (CSMA) is applied to 23 extensively used standard test functions and 10 multidisciplinary design problems. To check the validity of the proposed algorithm, results of CSMA has been compared with other recently developed and well-known classical optimizers such as PSO, DE, SSA, MVO, GWO, DE, MFO, SCA, CS, TSA, PSO-DE, GA, HS, Ray and Sain, MBA, ACO, and MMA. Statistical results suggest that chaotic strategy facilitates SMA to provide better performance in terms of solution accuracy. The simulation result shows that the developed chaotic algorithm outperforms on almost all benchmark functions and multidisciplinary engineering design problems with superior convergence.

Improving the path tracking performance of two-wheel differential mobile robots is very important, especially in problems that consider dynamic nonlinearities and motor torque constraints. This paper proposes a hybrid controller that combines backstepping control (BSC) with fractional order PID controller (FOPID). The parameters of BSC after being proven globally stable by Lyapunov will be determined by the hedge algebra method. The parameters of the FOPID controller are optimized using an improved metaheuristic algorithm, which is a combination of the wolf optimizer (GWO) algorithm with the slime molding algorithm. This is intended to improve trajectory tracking accuracy. In this work, the optimization process for FOPID is based on a cost function consisting of Integral absolute error and integral squared error, which helps to reduce the position and velocity errors. The controller performance is validated through MATLAB-Simulink with various trajectory scenarios and compared with conventional optimization methods such as standard PSO and GWO. Simulation results show superior trajectory tracking, error reduction and improved control performance.

The slime mold algorithm (SMA) is a metaheuristic optimization algorithm that simulates the foraging behavior of slime molds. Compared to other optimization algorithms, SMA has fewer parameters, faster convergence speed, and stronger optimization capabilities. However, the standard SMA uses two randomly selected individuals to guide the search direction of the population, which results in excessive randomness during the search process. This can lead to the loss of valuable information and waste computational resources. To overcome these limitations, this study proposes an enhanced slime mold algorithm (S2SMA) based on a spiral sensing search mechanism. The main contributions of this study are as follows: Firstly, a fitness–distance balanced oscillation search mechanism is introduced to solve the issue of lack of guidance in the individual oscillatory search phase in the original SMA, thus enhancing the global exploration ability of the algorithm. Secondly, the spiral sensing search mechanism is introduced, reshaping the random redistribution behavior in SMA. This aims to fully utilize the effective information in the existing population, improve search efficiency, and enhance population diversity. Finally, the computational logic of SMA is restructured based on the existing parameters, improving the algorithm’s performance while avoiding additional computational overhead. To validate the effectiveness of the proposed S2SMA, experiments were conducted on 71 test instances from the IEEE CEC2017 and IEEE CEC2021 benchmark sets, as well as three engineering problems. The algorithm was compared with classical algorithms, high‐performance algorithms, and advanced SMA variants. Experimental results show that S2SMA outperforms the classical algorithms, high‐performance algorithms, and other SMA variants in terms of both performance and robustness, demonstrating its potential application in engineering optimization.

Soil resistance characteristics, particularly the California Bearing Ratio (CBR), play a pivotal role in pavement and subgrade design. However, conventional laboratory-based CBR testing is often time-consuming, labor-intensive, and costly. This study presents a novel machine learning framework that combines Support Vector Regression (SVR) with three recent metaheuristic optimization algorithms—Dingo Optimization Algorithm (DOA), Alibaba and the Forty Thieves Optimization (AFT), and Adaptive Opposition Slime Mold Algorithm (AOSMA)—to predict CBR values efficiently and accurately. A dataset consisting of 220 soil samples with eight geotechnical input parameters was used to develop and evaluate the hybrid models. The predictive performance of each model was assessed using multiple evaluation metrics, including R², RMSE, MSE, RSR, and WAPE. Results indicate that the SVR–AFT (SVAF) hybrid model outperformed the others, achieving an R² of 0.9968 and an RMSE of 0.7946 in the testing phase, demonstrating high generalization ability and predictive precision. The integration of SVR with metaheuristic algorithms significantly enhances model robustness and accuracy, offering a practical and cost-effective alternative to empirical CBR testing methods. This work highlights the potential of hybrid AI models in solving complex geotechnical prediction problems and contributes to the growing body of research at the intersection of civil engineering and artificial intelligence.