Improved Salp Swarm Algorithm Research Articles

Optimization of Rotary Drilling Rig Mast Structure Based on Multi-Dimensional Improved Salp Swarm Algorithm

The mast is a critical component of rotary drilling rigs, which has a cross-section consisting of a rectangular shape formed by two web plates and two flange plates. Structural optimization of the mast is necessary to address the issue of excessive weight. The shortcomings of the traditional structural optimization algorithms are summarized as follows: the optimized steel plate thickness is a non-integer, where rounding upwards may increase the cost to a certain extent, but it can ensure the safety of the structure; rounding downwards its load carrying capacity may not satisfy the requirements, and thus a novel Salp Swarm Algorithm is proposed to solve the optimization problem. First, this study improves the initialization and update strategy in the traditional Salp Swarm Algorithm. In order to obtain a solution for engineering, an innovative multi-dimensional running comparison is carried out. Secondly, the optimization model of rotary drilling rigs is established based on the division of the working conditions. The objective function of the optimization model is to minimize the weight of the mast while considering the constraints of strength, stiffness, stability, and welding process. Finally, the proposed optimization algorithm and the established optimization model are applied to optimize the design of the mast for a rotary drilling rig. The empirical results demonstrate that the weight of the mast has been reduced by 20%. In addition, the Improved Salp Swarm Algorithm exhibits higher solution quality, faster iteration capability, and extreme stability in optimizing welded box sections compared to the conventional algorithm. The example shows that the Improved Salp Swarm Algorithm is applicable to the optimization problem of box sections.

Anti‐Interference Sliding Mode Control of PMSM Based on Improved Salp Swarm Algorithm

AbstractAiming at the problem that the control performance of permanent magnet synchronous motor is easily affected by load disturbance during operation, a fractional‐order sliding mode control method combining fractal‐order sliding mode controller (FOSMC) and fractional‐order sliding mode load torque observer (FOSMO) is proposed. The parameters of this method are tuned using the improved salp swarm algorithm (ISSA). Initially, the fractional‐order sliding mode controller is designed by applying fractional‐order calculus theory to enhance the system's response speed. Subsequently, a fractional‐order sliding mode load torque observer is developed to improve the system's anti‐interference capability and robustness due to the slow response and low identification accuracy of traditional load torque observers. Lastly, the control parameters are self‐tuned using the improved salp swarm algorithm due to the difficulty of adjusting them manually. Simulation and experimental results demonstrate that the proposed control method exhibits good convergence, no overshoot, minimal buffeting, and strong robustness against uncertain factors like load disturbances. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A hybrid Improved Salp Swarm Algorithm and Harris Hawk Optimizer for energy planning in microgrids with minimum operating cost

ABSTRACT Achieving optimal energy planning in Microgrids (MGs) is pivotal for addressing complex challenges associated with cost-effective and reliable energy supplies. This paper proposes a novel hybrid metaheuristic algorithm for optimal energy planning in microgrids using an Improved Salp Swarm Algorithm with Harris Hawk Foraging (ISSAHF). This technique is based on an improved multi-leader Salp Swarm Algorithm with an elite leader following strategy combined with Harris Hawks foraging. A simulation study is conducted on a low-voltage microgrid in off-grid and grid-connected modes. The optimization algorithm resulted in a daily average cost of 28.3370€ in off-grid mode compared to 19.2676€ in grid-connected one. Furthermore, the statistical study shows that the proposed algorithm outperforms well-established metaheuristic techniques regarding search capability and robustness. It yields mean optimal cost of 623.5248€ in off-grid and 404.7475€ for the grid-connected one, compared to other optimization techniques that vary from 667.2141€ to 959.5747€ in off-grid mode, and from 424.5841€ to 813.932€ in grid-connected mode. For robustness, the proposed technique performs well with a standard deviation of 20.765€ compared to the best (17.024€) and the worst (47.2423€) cases in off-grid mode, while in grid-connected mode, it is 28.8771€ compared to the best (21.6316€) and the worst (45.3774€) values.

Fault identification of rolling bearing based on improved salp swarm algorithm

Due to the rapid development of industrial manufacturing technology, modern mechanical equipment involves complex operating conditions and structural characteristics of hardware systems. Therefore, the state of components directly affects the stable operation of mechanical parts. To ensure engineering reliability improvement and economic benefits, bearing diagnosis has always been a concern in the field of mechanical engineering. Therefore, this article studies an effective machine learning method to extract useful fault feature information from actual bearing vibration signals and identify bearing faults. Firstly, variational mode decomposition decomposes the source signal into several intrinsic mode functions according to the actual situation. The vibration signal of the bearing is decomposed and reconstructed. By iteratively solving the variational model, the optimal modulus function can be obtained, which can better describe the characteristics of the original signal. Then, the feature subset is efficiently searched using the wrapper method of feature selection and the improved binary salp swarm algorithm (IBSSA) to effectively reduce redundant feature vectors, thereby accurately extracting fault feature frequency signals. Finally, support vector machines are used to classify and identify fault types, and the advantages of support vector machines are verified through extensive experiments, improving the ability of global search potential solutions. The experimental findings demonstrate the superior fault recognition performance of the IBSSA algorithm, with a highest recognition accuracy of 97.5%. By comparing different recognition methods, it is concluded that this method can accurately identify bearing failure.

Autonomous Underwater Vehicle Path Planning Based on Improved Salp Swarm Algorithm

Aiming at the problem of path planning for autonomous underwater vehicle (AUV) to cope with the influence of obstacles and eddies in complex marine environments, a path planning method based on an improved salp swarm algorithm (ISSA) is proposed. Firstly, the motion model of the AUV and eddy current model are constructed, including the relationship between position, velocity, attitude, and control inputs. Secondly, the improved SSA is proposed, which introduces the Levy flight strategy to enhance the algorithm’s optimization seeking ability and adds a nonlinear convergence factor to enhance the convergence ability of the algorithm. The stability and robustness of the improved algorithm are verified by test functions. Finally, the ISSA is applied to AUV path planning, which optimizes the AUV travel distance, improves the search efficiency and accuracy, and avoids the local optimum of the algorithm. The ISSA enhances the adaptive ability and robustness of the algorithm by introducing a dynamic adjustment strategy and feedback mechanism. Experimental verification is carried out using a simulated marine environment. The results show that the ISSA is better than the traditional algorithm in terms of path length as well as algorithm stability, and can effectively improve the navigation performance of AUV.

Large-scale IoT attack detection scheme based on LightGBM and feature selection using an improved salp swarm algorithm

Due to the swift advancement of the Internet of Things (IoT), there has been a significant surge in the quantity of interconnected IoT devices that send and exchange vital data across the network. Nevertheless, the frequency of attacks on the Internet of Things is steadily rising, posing a persistent risk to the security and privacy of IoT data. Therefore, it is crucial to develop a highly efficient method for detecting cyber threats on the Internet of Things. Nevertheless, several current network attack detection schemes encounter issues such as insufficient detection accuracy, the curse of dimensionality due to excessively high data dimensions, and the sluggish efficiency of complex models. Employing metaheuristic algorithms for feature selection in network data represents an effective strategy among the myriad of solutions. This study introduces a more comprehensive metaheuristic algorithm called GQBWSSA, which is an enhanced version of the Salp Swarm Algorithm with several strategy improvements. Utilizing this algorithm, a threshold voting-based feature selection framework is designed to obtain an optimized set of features. This procedure efficiently decreases the number of dimensions in the data, hence preventing the negative effects of having a high number of dimensions and effectively extracting the most significant and crucial information. Subsequently, the extracted feature data is combined with the LightGBM algorithm to form a lightweight and efficient ensemble learning scheme for IoT attack detection. The proposed enhanced metaheuristic algorithm has superior performance in feature selection compared to the recent metaheuristic algorithms, as evidenced by the experimental evaluation conducted using the NSLKDD and CICIoT2023 datasets. Compared to current popular ensemble learning solutions, the proposed overall solution exhibits excellent performance on multiple key indicators, including accuracy, precision, as well as training and detection time. Especially on the large-scale dataset CICIoT2023, the proposed scheme achieves an accuracy rate of 99.70% in binary classification and 99.41% in multi classification.

Coupled optimization of task sequence and hoist scheduling for electroplating production lines based on an improved salp swarm algorithm

Automatic electroplating production lines have been widely used in electronics industries to reduce the labour intensity and improve the production efficiency. In the multi-variety and low-volume electroplating production, it is known that the task loading sequence and hoist scheduling are coupled with each other, and they codetermine the production efficiency, while all the existing scheduling methods consider them separately, and thus the optimal production schemes become unavailable. Therefore, this paper develops a Task sequence-Hoist scheduling Coupled Optimization (THCO) model which simultaneously considers the requirements and practical constrains of task sequence and hoist scheduling, having an optimization objective of minimizing the maximum completion time. For this model, a double-layer code is developed and an Improved Salp Swarm Algorithm (ISSA) is developed by introducing three improvement strategies: the random spare strategy which is used to increase the population diversity, the nonlinear adaptive weight strategy which is used to balance the exploration and exploitation capacities, and a golden sine algorithm which is used to improve the convergence rate. Experiments based on 23 benchmark functions are then conducted. The obtained results show that ISSA has better convergence and solving quality than existing algorithms. Furthermore, several production cases prove that THCO can generate production schemes that better meet the requirements of production lines.

Multi-objective optimization of permanent magnet motor based on Improved Salp Swarm Algorithm and Spearman correlation

In order to weaken the torque ripple caused by cogs torque and increase the output torque of permanent magnet synchronous motor, this paper proposes a multi-objective optimization method of permanent magnet synchronous motor based on Improved Multi-Objective Salp Swarm Optimization Algorithm (IMOSSA) and Spearman correlation analysis. Firstly, the improved strategy of Multi-Objective Salp Swarm Algorithm (MOSSA) was proposed, and the effectiveness of the improved algorithm was further verified by the performance test of the algorithm. Then, the motor simulation model was established, and the high correlation parameters were filtered out by Spearman correlation analysis. The response surface model was established by Response Surface Method (RSM), and the regression equation was given. The IMOSSA was used for iterative optimization. Through optimization, the experimental results show that under the premise of meeting the efficiency design requirements, the average output torque of the motor is increased by 21.35%, the torque ripple and cogging torque are reduced by 45.04% and 39.17% respectively, which verifies the effectiveness and feasibility of the proposed method.

Optimal Allocation of Hybrid Energy Storage Capacity Based on ISSA-Optimized VMD Parameters

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method’s effectiveness and superiority.

An Intelligent System for Thyroid Dysfunction Prediction

The prediction of thyroid diseases has become increasingly important in recent years. The feature engineering part is generally less studied in existing approaches than model optimization. To circumvent these restrictions, feature engineering for machine learning (ML) and deep learning models are examined in our research. This paper proposes a system to predict thyroid dysfunction. A model comprises three stages: preprocessing of the data, selection of features, and classification. A standard scalar normalizes the data after filling missing values with mean values. A Deep Neural Network (DNN) is utilized for classification, and the Improved Salp Swarm Algorithm (ISSA) is used for feature selection. Finally, accuracy is utilized to estimate the system proposed. The UCI thyroid illness datasets are used in our investigation. In experiments with the DNN model, deep learning classifiers based on selected features produce the highest accuracy of 99.68%. The model proposed has superior performance compared to state-of-the-art methods.

Hybrid design of optimal reconfiguration and DG sizing and siting using a novel improved salp swarm algorithm

Hybrid design of optimal reconfiguration and DG sizing and siting using a novel improved salp swarm algorithm

Cases of PD-CFPID selector for frequency adjustment of integrated power grid

In current article an improved salp swarm algorithm (SSA) has been suggested. Likewise, the adjustable scaling-component is locked in to manage salp’s situation in time of the hunt system to limit arbitrary development salps. The upgraded salp swarm method combines random objection-based learning, multiple management, and simulated hardening in swarm brilliant-based metaheuristic salp swarm method, which rises the exploration and exploitation of the primary salp swarm method. To exhibit viability of the improved SSA, a bunch of multichannel test capabilities are locked in. A partially decentralized combined fuzzy proportional integral derivative (PD-CFPID) regulator is planned for integrated grid. A partially decentralized robust control plan is introduced to deal with a class of multi-inputs multi outputs (MIMO) non-square, linear framework different systems. Partially decentralized command is a control composition that lies between a completely decentralized structure and a wholly centralized one, and has the primacy of achieving comparable performance as an entirely centralized regulator but with simpler shape. The proposed flexible partially decoupled command scheme works in a straightforward and systematic way. Simulations show that the proposed partially decentralized control performs well for the nonlinear system, nearly equivalent to the multivariable control, and they achieve better performance indexes. Simulation outcomes appear that the stated method is simple and can reach better performance. The results of the PD-CFPID regulator are compare among CFPID and PID as many circumstances of the presented control outlook.

Crop Yield Estimation using Improved Salp Swarm Algorithm based Feature Selection

Crop yield estimation is the art of yield prediction before harvest and it is essential for planning and making conclusive agricultural policies. The forecasting of crop yield is essential in optimal nutrient management, crop insurance, crop market planning and harvest management. However, the crop yield estimation is considered as a challenging task because of huge amount of abundant information exists in the crop data. Therefore, an effective feature selection is required to be developed for removing the redundant attributes. In this research, an Improved Salp Swarm Algorithm (ISSA) based feature selection for an effective crop yield estimation. The Opposition Based Learning (OBL) and Local Search Algorithm (LSA) are incorporated in the ISSA’s initialization and exploitation phase for selecting optimum feature subset. The selected features from the ISSA are used to enhance the classification using Modified Long Short Term Memory (MLSTM) classifier. The performance of the ISSA-MLSTM is analyzed using accuracy, precision, recall, F-score, Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The existing researches such as Ensemble approach and MLSTM are used to evaluate the ISSA-MLSTM. The accuracy of the ISSA-MLSTM is 99% that is high when compared to the MLSTM.

Time-Delay following Model for Connected and Automated Vehicles with Collision Conflicts and Forced Deceleration

The connected and automated car-following model can provide a model reference for the queue control algorithm of connected and automated driving and has become a hot research topic in the field of connected vehicles and intelligent transportation. A queue of fast-moving vehicles on urban roads can cause traffic congestion when forced to slow down and, in serious cases, can cause rear-impact accidents. Therefore, this paper introduces information on the time delay of information reception and processing, a collision risk quantification factor reflecting the speed characteristics of the front vehicle, and the speed limit and proposes an improved intelligent driver collision quantification model that considers drastic changes in the speed of the front vehicle. Additionally, the model parameters are calibrated using real vehicle data from urban roads combined with an improved salp swarm algorithm. Finally, the evolution rule of disturbance in the traffic flow under different states is analyzed using a time-space diagram, and the DIDM-CSCL model is compared with the classical IDM. The results show that the improved IDM can better describe the following behavior at the microscopic level, which provides a basis for research related to connected and automated driving.

Optimizing the Architecture of Convolutional Neural Networks Using Modified Salp Swarm Algorithm

Deep learning is highly effective in dealing with complex tasks such as image classification and recognition. However, finding the optimal architecture's hyperparameters for Convolutional Neural Networks (CNNs) to achieve the best performance and parameter regularization can be challenging. Metaheuristic optimization algorithms can be utilized to find solutions in this context. In this research, a computerized CNN was adjusted using an improved Salp Swarm Algorithm (SSA) to enhance crucial CNN settings, like dropout rate, hidden units, learning rate, and batch size. The refined design was tested on two standard datasets. MNIST and Fashion MNIST. The outcomes displayed model performance achieving accuracy levels of 99.6% for MNIST and 94.08% for Fashion MNIST. This tuned system outperformed the existing practices by 0.2% and 0.04% for each dataset while also cutting down on computational expenses. The fusion of SSA with CNNs displayed adaptability and resilience opening up possibilities, in image classification and consistently delivering outstanding outcomes.

An improved salp swarm algorithm for solving node coverage optimization problem in WSN

An improved salp swarm algorithm for solving node coverage optimization problem in WSN

Techno-economic optimization for isolated hybrid PV/wind/battery/diesel generator microgrid using improved salp swarm algorithm.

The main objective of this study is to develop a new method for solving the techno-economic optimization problem of an isolated microgrid powered by renewable energy sources like solar panels, wind turbines, batteries, and diesel generators while minimizing greenhouse gas emissions. An Improved Salp Swarm Algorithm (ISSA) with a position adaptation mechanism for the salp leader that involves a leader salp that moves about depending on both food availability and its previous position has been proposed to overcome the convergence problem. In the original SSA, as the approach converges, it can no longer find optimal solutions and becomes trapped in a local minimum. Three Microgrid System (MS) configurations are discussed: PV/WT/BESU/DG, PV/BESU/DG, and WT/BESU/DG. The proposed method seeks to find a middle ground between technical criteria and environmental concerns when deciding on PV, WT, BESU, and DG sizes. The findings indicate that the proposed ISSA approach gives superior results compared to other well-known algorithms like the original SSA, the Ant Lion Optimizer (ALO), the Dragonfly Approach (DA), and the Moth-Flame Optimization Algorithm (MFO), which, after significant investigation, has been proven to help determine the appropriate microgrid size. With PV sizes of 10, 9 WT, 24 BESU, and 3 DG, the PV/WT/BESU/DG configuration offers the highest level of cost-effectiveness with Cost of Energy (COE) of 0.2109 $/kWh, Net Present Cost (NPC) of 376,063.8 $, Loss of Power Supply Probability (LPSP) of 4%, Renewable Energy Fraction (REF) of 96%, and CO2 emission of 12.4457 tons/year. ISSA is brought up as a possible solution to both the problem of rising energy prices and the difficulties inherent in microgrid design.

Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction

Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system.

An innovative fuzzy logic based controller for solid oxide fuel cells

The solid oxide fuel cell (SOFC) stands out as a vital component in sustainable distributed generation systems, thanks to its reliability and adaptable power generation capabilities. This adaptability allows the SOFC to efficiently balance grid power by adjusting its power output. Nevertheless, maintaining the desired fuel usage rate and achieving rapid load tracking pose considerable hardships in SOFC control. To deal with these difficulties, this study introduces a novel approach, the Cascaded Double Input Interval Type-2 Fuzzy Logic Controller (C-DIT2-FLC), designed for SOFC control. In this innovative model, the Improved Salp Swarm Algorithm (ISSA) is applied to determine the crucial parameters of Proportional Integral-Proportional Derivative (PI-PD) cascade controllers. The C-DIT2-FLC dynamically adjusts the controller gains, which are optimized using ISSA, to enhance the effectiveness of SOFC operations. The research results substantiate the superior efficacy of the C-DIT2-FLC over conventional controllers, thereby providing enhanced control and performance capabilities for SOFC systems.

Improved salp swarm algorithm based on Newton interpolation and cosine opposition-based learning for feature selection

Feature selection (FS) is one of the most critical tasks in data mining, which aims to reduce the dimensionality of the data and maximize classification accuracy. The FS problem can be treated as an NP-hard problem. Recently, various swarm intelligent (SI) algorithms have been employed to deal with the FS problem to solve the expensive computation of the exact method. However, the performance of the SI algorithms is limited because these algorithms do not comprehensively take the characteristics of the FS problem into consideration. Therefore, a promising salp swarm algorithm called NCSSA is presented to solve this problem. In NCSSA, multi-perspective initialization strategy, Newton interpolation inertia weight, improved followers’ update model and cosine opposition-based learning (COBL) are proposed. In the majority of the SI algorithm-based FS method, the initial search agents are randomly generated or using a single filter method. However, a single filter method has different performance on various datasets. Therefore, a multi-perspective initialization strategy based on minimal redundancy maximal relevance (MRMR) and ReliefF is proposed, which can select the optimal subsets from different perspectives. Furthermore, Newton interpolation inertia weight is presented to balance the algorithm’s exploration and exploitation. Compare with the existing inertia weights, the adjustment flexibility of the proposed inertia weight is enhanced. Additionally, the followers update their positions according to the values of ReliefF and MRMR, which can make full use of the relationship between data and labels. Finally, the COBL is introduced to accelerate the convergence rate and helps the algorithm jump out of the local best solutions. The COBL is better than opposition-based learning (OBL) in terms of randomness, and considers the characteristics of the FS problem. The proposed NCSSA is compared to a series of non-SI-based methods and SI-based methods employing the standard datasets from the UCI Machine Learning Repository. Experimental results show that the NCSSA is a promising algorithm for the FS problem. The contribution analysis of each strategy indicates that the COBL is the most effective strategy in improving the SSA.