The improved slime mould algorithm with exponential functions

The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements. As a representative, Slime mould algorithm (SMA) is widely used because of its superior initial performance. Therefore, this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems. For this aim, the structure of SMA is adjusted to develop the efficiency of the original method. As a stochastic optimizer, SMA mainly stimulates the behavior of slime mold in nature. For the harmony of the exploration and exploitation of SMA, the paper proposed an enhanced algorithm of SMA called ECSMA, in which two mechanisms are embedded into the structure: elite strategy, and chaotic stochastic strategy. The details of the original SMA and the two introduced strategies are given in this paper. Then, the advantages of the improved SMA through mechanism comparison, balance-diversity analysis, and contrasts with other counterparts are validated. The experimental results demonstrate that both mechanisms have a significant enhancing effect on SMA. Also, SMA is applied to four structural design issues of the welded beam design problem, PV design problem, I-beam design problem, and cantilever beam design problem with excellent results.

A new strategy based on slime mould algorithm to extract the optimal model parameters of solar PV panel

Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection

The study focuses on a hypothesis-based critical analysis of proposed modifications to the slime mould algorithm. The modifications being investigated are opposition learning and wavelet mutation. These modifications help the slime mould algorithm to avoid local optima for better exploration of the search space. The goal is to assess the effectiveness of these modifications in improving the performance of slime mould algorithm. Both, the basic slime mould algorithm, and the proposed variant eventually achieve the desired convergence. However, to compare the efficiency of the two algorithms, the study introduces a measurement index called the ‘swiftness’ of the algorithm. This index quantifies the speed at which an algorithm reaches convergence. It is calculated as the area under the convergence curve of each algorithm. The rationale behind this approach is that visual inspection alone may not be sufficient to discriminate between the algorithms based on the convergence curve. The hybrid approach, incorporating opposition learning and wavelet mutation, is evaluated statistically using the CEC 2008 benchmark function. Additionally, the study examines small and medium single-objective power dispatch optimization problems that adhere to non-convex system limitations. By assessing the performance of the modified slime mould algorithm on these different problem domains, the researchers aim to provide a comprehensive analysis of its effectiveness.

Performance analysis of hybrid metaheuristic assisted collateral FO controller for HRES system integrated UPQC

Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid

In optimization, metaheuristic algorithms have received extensive attention and research due to their excellent performance. The slime mould algorithm (SMA) is a newly proposed metaheuristic algorithm. It has the characteristics of fewer parameters and strong optimization ability. However, with the increasing difficulty of optimization problems, SMA has some shortcomings in complex problems. For example, the main concerns are low convergence accuracy and prematurely falling into local optimal solutions. To overcome these problems, this paper has developed a variant of SMA called CCSMA. It is an improved SMA based on horizontal crossover (HC) and covariance matrix adaptive evolutionary strategy (CMAES). First, HC can enhance the exploitation of the algorithm by crossing the information between different individuals to promote communication within the population. Finally, CMAES facilitates algorithm exploration and exploitation to reach a balanced state by dynamically adjusting the size of the search range. This benefits the algorithm by allowing it to go beyond the local space to explore other solutions with better quality. To verify the superiority of the proposed algorithm, we select some new original and improved algorithms as competitors. CCSMA is compared with these competitors in 40 benchmark functions of IEEE CEC2017 and CEC2020. The results demonstrate that our work outperforms the competitors in terms of optimization accuracy and jumping out of the local space. In addition, CCSMA is applied to tackle three typical engineering optimization problems. These three problems include multiple disk clutch brake design, pressure vessel design, and speed reducer design. The results showed that CCSMA achieved the lowest optimization cost. This also proves that it is an effective tool for solving realistic optimization problems.

The slime mould algorithm (SMA), as an emerging and promising swarm intelligence algorithm, has been studied in various fields. However, SMA suffers from issues such as easily getting trapped in local optima and slow convergence, which pose challenges when applied to practical problems. Therefore, this study proposes an improved SMA, named HESMA, by incorporating the covariance matrix adaptation evolution strategy (CMA-ES) and storing the best position of each individual (SBP). On one hand, CMA-ES enhances the algorithm’s local exploration capability, addressing the issue of the algorithm being unable to explore the vicinity of the optimal solution. On the other hand, SBP enhances the convergence speed of the algorithm and prevents it from diverging to other inferior solutions. Finally, to validate the effectiveness of our proposed algorithm, this study conducted experiments on 30 IEEE CEC 2017 benchmark functions and compared HESMA with 12 conventional metaheuristic algorithms. The results demonstrated that HESMA indeed achieved improvements over SMA. Furthermore, to highlight the performance of HESMA further, this study compared it with 13 advanced algorithms, and the results showed that HESMA outperformed these advanced algorithms significantly. Next, this study applied HESMA to five engineering optimization problems, and the experimental results revealed that HESMA exhibited significant advantages in solving real-world engineering optimization problems. These findings further support the effectiveness and practicality of our algorithm in addressing complex engineering design challenges.

Optimal design of stand-alone hybrid PV/wind/biomass/battery energy storage system in Abu-Monqar, Egypt

Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. This is because placing data in an image can cause permanent distortion after data mining. Therefore, a reversible data hiding (RDH) technique is required. One of the well-known designs of RDH is the difference expansion (DE) method. In the DE-based RDH method, finding spaces that create less distortion in the marked image is a significant challenge, and has a high insertion capacity. Therefore, the smaller the difference between the selected pixels and the more correlation between two consecutive pixels, the less distortion can be achieved in the image after embedding the secret data. This paper proposes a multilayer RDH method using the multilevel thresholding technique to reduce the difference value in pixels and increase the visual quality and the embedding capacity. Optimization algorithms are one of the most popular methods for solving NP-hard problems. The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. In the proposed method, the SMA is applied to the host image for optimal multilevel thresholding of the image pixels. Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. Experimental results show that the proposed method is better than comparable methods regarding the degree of distortion, quality of the marked image, and insertion capacity.

A sustainable environment by decreasing fossil fuel utilization and anthropogenic greenhouse gases is a globally main goal due to climate change and serious air pollution. Carbon dioxide (CO2) is a heat-trapping (greenhouse) that is released into the earth's atmosphere from natural processes, such as volcanic respiration and eruptions, as well as human activities, such as burning fossil fuels and deforestation. Due to this fact, underground carbon storage (UCS) is a promising technology to cut carbon emissions. However, there are some barriers to prevent UCS from applying globally. One of them is evaluating the feasibility of storage projects. Thus, the prediction accuracy of CO2 storage efficiencies may promote the attention of the community for UCS. In this study, we utilize the recent advances of swarm intelligence to develop a hybrid algorithm called AOSMA, employed to train the long short-term memory (LSTM). The developed swarm intelligence method (AOSMA) is an enhanced Aquila optimizer (AO) using the search mechanism of the slime mould algorithm (SMA). It is used to boost the prediction capability of the LSTM by optimizing its parameters. We considered two CO2 trapping indices, called residual trapping index (RTI) and solubility trapping index (STI). The evaluation experiments have shown that the AOSMA achieved significant results compared to the original AO and SMA and several swarm intelligence and optimization algorithms. The developed smart tools could use as a game changer to provide fast and accurate storage efficiency for projects that have similar parameters falling within the range of the database.

This study presents a novel hybrid surrogate-assisted optimization framework for finite element (FE) model updating of composite panels. Accurately updated FE models offer a reliable basis for design and analysis of modern structural systems and are widely employed in various industrial and engineering applications. However, model updating of composite structures remains a significant challenge due to their inherent anisotropy and complex mechanical behavior, which often limit the effectiveness of conventional updating techniques. To address these limitations, a hybrid framework is introduced for the first time, combining surrogate modeling with the Slime Mould Algorithm (SMA), a recently developed metaheuristic optimizer. For the proposed model updating strategy, a baseline FE model was initially developed, and an objective function was defined to quantify discrepancies between numerical predictions and experimental measurements. The baseline model generated training data for surrogate models constructed using Response Surface Methodology (RSM), Kriging (KRG), and Artificial Neural Networks (ANN). Among these, the ANN-based surrogates achieved superior predictive accuracy and were selected for the optimization phase. Sensitivity analysis identified the most influential parameters for model updating. Optimization was then carried out to minimize the objective function using the SMA. The resulting ANN–SMA framework substantially reduced discrepancies between experimental and numerical modal responses, yielding a high-fidelity FE model. Comparative assessments against established metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), both integrated with ANN surrogates, revealed that the ANN-SMA framework exhibited enhanced convergence stability and computational efficiency, with reductions in runtime of approximately 7% and 30% relative to ANN-PSO and ANN-GA, respectively. These findings confirm that the proposed ANN–SMA hybrid framework provides a robust, efficient, and reliable tool for precise FE model updating of composite structures, with significant potential for broader applications in structural dynamics and structural health monitoring (SHM).

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem-based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi- and single-OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30-bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.