An Improved Elite Slime Mould Algorithm for Engineering Design

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

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.

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

Applying the Slime Mould Algorithm (SMA) in optimization and coding it with Python programming language, we found that runtime warnings occurred because of the inherent limits of arctanh functions. Considering the circumstances that the maximum residual errors would be constrained and the maximum allowed iteration number would be bothering, we therefore proposed an improvement to the SMA with exponential functions. Three kinds of benchmark functions were introduced to verify the suitability, accessibility, and capability. Experiment results demonstrated that the improved SMA with exponential functions would be suitable and accessible. Comparing with the standard SMA, the improved algorithm would result in better performance with exception that they perform similar when optimizing the Quintic function, the representative of those kinds of benchmark functions with basins in their three-dimensional profiles.

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.

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.

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

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

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.

A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters

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.

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.

In this paper, a novel optimization method called Slime Mould Algorithm (SMA) for solving the ELD and CEED problem is presented. To investigate the effectiveness of the proposed algorithm, the 10 and 40 units considering the valve point loading effect test has been executed. The solving the Economic Load Dispatch (ELD) and Combined Economic and Emission Dispatch (CEED) are crucial task in modern power systems. The aim of ELD is assigning the best generation scheduling for minimum cost generation with satisfying the load demands while the CEED means assigning the best generation scheduling for cost and emission reduction simultaneously. The effectiveness of the proposed algorithm is compared with other algorithms. In this paper, a novel optimization method called Slime Mould Algorithm (SMA) for solving the ELD and CEED problem is presented. To investigate the effectiveness of the proposed algorithm, the 10 and 40 units considering the valve point loading effect test has been executed. The solving the Economic Load Dispatch (ELD) and Combined Economic and Emission Dispatch (CEED) are crucial task in modern power systems. The aim of ELD is assigning the best generation scheduling for minimum cost generation with satisfying the load demands while the CEED means assigning the best generation scheduling for cost and emission reduction simultaneously. The effectiveness of the proposed algorithm is compared with other algorithms.

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.

This manuscript proposes a hybrid technique for optimal energy management (EM) of fuel cell (FC) and ultra-capacitor (UC) hybrid electric vehicles (FCHEVs) under uncertainty. The proposed method is a joint execution of the Slime Mould Algorithm (SMA) and the Adaptive Neuro-fuzzy Inference System (ANFIS), otherwise called the SMA-ANFIS system. The main aim of the proposed system is to achieve and regulate the steady state of DC bus voltage with minimal steady-state error (SSE) under different load conditions by using the proposed SMA-ANFIS technique. FC supplies power during vehicular operation and it is used to regulate DC bus voltage to the chosen value and recharge, and UC supplies current optimally through the proposed technique. The optimal EMS performs and controls the power flows through the associated power converters to accomplish some power allocation and energy efficiency levels. Finally, the proposed method is done in the MATLAB platform, and the performance is compared with other methods.