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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 aims to represent a multi-objective equilibrium optimizer slime mould algorithm (MOEOSMA) to solve real-world constraint engineering problems. The proposed algorithm has a better optimization performance than the existing multi-objective slime mould algorithm. In the MOEOSMA, dynamic coefficients are used to adjust exploration and exploitation trends. The elite archiving mechanism is used to promote the convergence of the algorithm. The crowding distance method is used to maintain the distribution of the Pareto front. The equilibrium pool strategy is used to simulate the cooperative foraging behavior of the slime mould, which helps to enhance the exploration ability of the algorithm. The performance of MOEOSMA is evaluated on the latest CEC2020 functions, eight real-world multi-objective constraint engineering problems, and four large-scale truss structure optimization problems. The experimental results show that the proposed MOEOSMA not only finds more Pareto optimal solutions, but also maintains a good distribution in the decision space and objective space. Statistical results show that MOEOSMA has a strong competitive advantage in terms of convergence, diversity, uniformity, and extensiveness, and its comprehensive performance is significantly better than other comparable algorithms.

The California Bearing Ratio (CBR) test is a crucial geotechnical parameter for evaluating soil strength. This study proposes a Least Squares Support Vector Regression (LSSVR) model to predict CBR values using compaction characteristics, moisture content, and soil properties. A dataset comprising 110 soil samples was used, with 70% for training and 30% for testing. To enhance predictive accuracy, three metaheuristic algorithms-Improved Arithmetic Optimization Algorithm (IAOA), Equilibrium Slime Mould Algorithm (ESMA), and Runge Kutta Optimization (RKO)-were integrated with LSSVR, forming hybrid models LSIA, LSEM, and LSRK. These algorithms optimized the regularization parameter (C) and kernel parameter (Gamma) to improve model generalization. Performance evaluation using R2 RMSE, and MAE showed that the LSIA model outperformed all others, achieving an R2 of 0.9975 (training) and 0.9932 (testing), along with the lowest RMSE (0.5489) and MAE (0.3176). The results confirm that LSIA exhibits superior predictive accuracy and robustness, making it a reliable and time-efficient alternative for geotechnical applications.

The unconfined compressive strength (UCS) of rocks is a crucial factor in geotechnical engineering, assuming a central role in various civil engineering undertakings, including tunnel construction, mining operations, and the design of foundations. The precision in forecasting UCS holds paramount importance in upholding the security and steadfastness of these endeavors. This article introduces a fresh methodology for UCS prognostication by amalgamating Gaussian process regression (GPR) with two pioneering optimization techniques: sand cat swarm optimization (SCSO) and the equilibrium slime mould algorithm (ESMA). Conventional techniques for UCS prediction frequently encounter obstacles like gradual convergence and the potential for becoming ensnared in local minima. In this investigation, GPR is the foundational predictive model due to its adeptness in managing nonlinear associations within the dataset. The fusion of GPR with cutting-edge optimizers is envisioned to elevate the precision and expeditiousness of UCS prognostications. An extensive collection of rock samples, each accompanied by UCS measurements, is harnessed to assess the suggested methodology. The efficacy of the GPSC and GPES models is juxtaposed with the conventional GPR technique. The findings reveal that incorporating SCSO and ESMA optimizers into GPR brings about a noteworthy enhancement in UCS prediction accuracy and expedites convergence. Notably, the GPSC models exhibit exceptional performance, evidenced by an exceptional R 2 value of 0.995 and an impressively minimal RMSE value of 1.913. These findings emphasize the GPSC model’s potential as an exceedingly auspicious tool for experts in the realms of engineering and geology. It presents a sturdy and dependable method for UCS prediction, a resource of immense value in augmenting the security and efficiency of civil engineering endeavors.

This study explores the application of machine learning (ML) techniques to predict the optimum moisture content (OMC) of soil-stabilizer combinations. OMC represents the moisture level where soil achieves peak compaction and strength in conjunction with a stabilizer, playing a vital role in attaining desired engineering properties in soil stabilization endeavors. Employing the adaptive neuro-fuzzy inference system (ANFIS) as a robust ML tool, this research endeavors to formulate intricate and accurate models. These models forge connections between OMC and many intrinsic soil properties, including particle-size linear shrinkage, plasticity, distribution, and the nature and quantity of stabilizing additives. A diverse dataset is curated to ascertain the responsiveness of OMC to variations in influential factors, encompassing distinct soil types and previously documented results from stabilization tests. In an endeavor to enhance model precision, this study integrates two meta-heuristic algorithms: the Cheetah optimization algorithm (CO) and the equilibrium slime mould algorithm (ESM). By synergistically leveraging these algorithms, the accuracy of the models is fortified. Rigorous validation ensues through an analysis of OMC samples drawn from diverse soil types obtained from historical stabilization test outcomes. The study unveils three notable models: ANCO (ANFIS + CO), ANES (ANFIS + ESM), and an independent ANFIS model. Each of these models furnishes invaluable insights that substantiate the meticulous projection of OMC for soil-stabilizer blends. Noteworthy among them is the ANCO model, exhibiting exceptional performance metrics. The R 2 (correlation coefficient) value of 0.996 and an impressively low RMSE of 0.436 indicate its precision and reliability. These findings not only underscore the accuracy of the ANCO model but also underscore its efficacy in prognosticating soil stabilization outcomes. This methodology introduces a promising avenue for accurately predicting OMC across a spectrum of engineering applications connected to soil-stabilizer amalgamations.

In the realm of photovoltaic grid integration with Shunt Active Power Filters operating under partial shading conditions, this study introduces an innovative approach aimed at minimizing both power consumption from the electrical grid and associated costs. The primary objective of this research is to maximize the efficiency of photovoltaic system output by implementing a novel algorithm known as the Equilibrium Slime Mould Optimization technique. This algorithm is employed to precisely track the global power point of the photovoltaic array under partial shading conditions, resulting in increased photovoltaic power injection and decreased grid-side consumption. The choice of the Equilibrium Slime Mould Optimization technique is motivated by its exceptional ability to efficiently explore the search space and avoid falling into local extrema. Additionally, this article incorporates Predictive Direct Power Control, one of the most contemporary Shunt Active Power Filter control techniques, to effectively eliminate harmonics and enhance overall system efficiency. To validate this proposed approach, a simulation setup was meticulously developed. The obtained results demonstrate a remarkable enhancement in the efficiency of photovoltaic power injection compared to the conventional sliding mode technique, which tends to get trapped at local maximum power point, thereby resulting in diminished power injection. This pioneering approach heralds a new era in the application of metaheuristic algorithms within practical systems, leading to enhanced productivity and reduced costs for consumers. Furthermore, it holds the potential to advance various categories of interconnected photovoltaic systems, ensuring improved performance across diverse operational scenarios.

In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e−17 and 10e−18, and the average solution time is 0.05 s and 0.36 s, respectively.

Aiming at the problems of slow convergence, low accuracy, and easy to fall into local optimum of the slime mould algorithm (SMA), we propose an improved SMA (OJESMA). OJESMA improves the performance of the algorithm by combining strategies based on opposition-based learning, joint opposite selection, and equilibrium optimizer. First, we introduce an adversarial learning-opposition-based learning, in generating the initial population of slime molds. Second, we incorporate a joint inverse selection strategy, including selective leading opposition and dynamic opposite. Finally, we introduce the balanced candidate principle of the equilibrium optimizer algorithm into SMA, which enhances the algorithm's optimal search capability and anti-stagnation ability. We conducted optimization search experiments on 29 test functions from CEC2017 and 10 benchmark test functions from CEC2020, as well as nonparametric statistical analysis (Friedman and Wilcoxon). The experimental results and non-parametric test results show that OJESMA has better optimization accuracy, convergence performance, and stability. To further validate the effectiveness of the algorithm, we also performed optimization tests on six engineering problems and the variable index Muskingum. In summary, OJESMA demonstrates its practical value and advantages in solving various complex optimization problems with its excellent performance, providing new perspectives and methods for the development of optimization algorithms.

Metaheuristic intelligent optimization algorithm is an effective method to settle high-dimensional nonlinear complicated optimization problems. Slime mould algorithm is a novel intelligent optimization algorithm proposed in 2020. However, the basic slime mould algorithm still has shortcomings, such as slow convergence rate, easy falling into local extremum, and imbalanced exploration and exploitation capabilities. To further enhance the optimization capability and expand the application scope of the slime mould algorithm, a slime mould algorithm based on the mechanism of multi-strategy information interaction and optimally oriented initialization (MSII-SMA) is proposed. Three improved mechanisms are introduced into the algorithm and the time complexity of MSII-SMA is analyzed. To verify the optimization effect, MSII-SMA and the other 5 typical comparison algorithms are applied to settle the CEC2015 test function set. The analysis of the optimization accuracy, convergence curve, Friedman test, boxplot and scalability test shows that the optimization ability, convergence rate, stability and scalability of MSII-SMA are evidently better than the comparison algorithm. Finally, MSII-SMA and other comparison algorithms are used to settle engineering design optimization problems with different complexity. The experimental results verify the universality, reliability and preponderance of MSII-SMA in dealing with engineering design constraint optimization problems.

Job Shop Scheduling Problem (JSSP) is a well-known NP-hard combinatorial optimization problem. In recent years, many scholars have proposed various metaheuristic algorithms to solve JSSP, playing an important role in solving small-scale JSSP. However, when the size of the problem increases, the algorithms usually take too much time to converge. In this paper, we propose a hybrid algorithm, namely EOSMA, which mixes the update strategy of Equilibrium Optimizer (EO) into Slime Mould Algorithm (SMA), adding Centroid Opposition-based Computation (COBC) in some iterations. The hybridization of EO with SMA makes a better balance between exploration and exploitation. The addition of COBC strengthens the exploration and exploitation, increases the diversity of the population, improves the convergence speed and convergence accuracy, and avoids falling into local optimum. In order to solve discrete problems efficiently, a Sort-Order-Index (SOI)-based coding method is proposed. In order to solve JSSP more efficiently, a neighbor search strategy based on a two-point exchange is added to the iterative process of EOSMA to improve the exploitation capability of EOSMA to solve JSSP. Then, it is utilized to solve 82 JSSP benchmark instances; its performance is evaluated compared to that of EO, Marine Predators Algorithm (MPA), Aquila Optimizer (AO), Bald Eagle Search (BES), and SMA. The experimental results and statistical analysis show that the proposed EOSMA outperforms other competing algorithms.