Salp Swarm Algorithm Research Articles

A hybrid swarm intelligent optimization algorithm for antenna design problems

Meta-heuristic optimization algorithms have seen significant advancements due to their diverse applications in solving complex problems. However, no single algorithm can effectively solve all optimization challenges. The Naked Mole-Rat Algorithm (NMRA), inspired by the mating patterns of naked mole-rats, has shown promise but suffers from poor convergence accuracy and a tendency to get trapped in local optima. To address these limitations, this paper proposes an enhanced version of NMRA, called Salp Swarm and Seagull Optimization-based NMRA (SSNMRA), which integrates the search mechanisms of the Seagull Optimization Algorithm (SOA) and the Salp Swarm Algorithm (SSA). This hybrid approach improves the exploration capabilities and convergence performance of NMRA. The effectiveness of SSNMRA is validated through the CEC 2019 benchmark test suite and applied to various electromagnetic optimization problems. Experimental results demonstrate that SSNMRA outperforms existing state-of-the-art algorithms, offering superior optimization capability and enhanced convergence accuracy, making it a promising solution for complex antenna design and other electromagnetic applications.

Hermit Crab Optimizer Approach: Harmonics Reduction of Hybrid Renewable Energy Sources

Micro Grids (MGs) have gained a lot of popularity recently because of their benefits in terms of high transmission efficiency and efficient power conversion. However, the nonlinear properties of renewable energy sources (RESs), the increased usage of power electronic devices, and unanticipated fluctuations in load are what lead to the formation of stability issues in the MG. This manuscript presents the optimization approach to improve the performance of microgrid with renewable energy sources (RES). The proposed method is the Hermit Crab Optimizer (HCO) Algorithm. Here, Solar PV and Wind Turbine is utilized as power sources, supported by DC/DC and AC/DC converters for efficient energy conversion. The main objective of proposed technique is to minimize the THD and maximize the efficiency of system. The HCO algorithm is used to optimize the duty cycle of the DC to DC converters within the system. By then, the proposed method's performance is put into practice using the MATLAB platform, and it is contrasted with a number of existing methods, including Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), and Particle Swarm Optimization (PSO). From the result, the THD of proposed method is -17.75. The THD of existing PSO is 17.81465, ALO is -17.81469 and SSA is -17.81545.

Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning-based ECG Signal Classification on Internet of Things Environment

The Internet of Things (IoT) in healthcare relates to implementing interconnected devices and systems for collecting and sharing healthcare information in real time. The integration of IoT in healthcare has the potential to enhance patient outcomes, reduce healthcare costs, and improve the efficacy of medical services. Electrocardiogram (ECG) is a non-invasive heart monitoring method that has become widely accessible due to user-friendly, low-cost, and lead-free wearable heart monitors. However, relying on overworked caregivers for manual monitoring is inefficient. This study develops a Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning (CLSSA-EDL) technique for ECG signal classification in IoT healthcare. The objective of CLSSA-EDL is to detect and classify ECG signals to support decision-making in the IoT healthcare environment. The CLSSA-EDL approach employs the DenseNet201 feature extraction method, with hyperparameters optimally selected by the CLSSA system. For ECG signal detection and classification, an ensemble model using a Stacked Autoencoder (SAE), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) is utilized. The CLSSA-EDL technique was evaluated on a benchmark ECG dataset, achieving an accuracy of 98.7%, sensitivity of 97.5%, and specificity of 99.1%, demonstrating superior performance compared to recent algorithms.

Hybrid optimization algorithm for analysis of influence propagation in social network

Influence maximization(IM) is defined as the problem of identifying a node subset in a social network which increases the spread of influence. IM plays a crucial role in social networks by catalyzing the dissemination of influence, resulting in an augmented count of influenced nodes following the propagation process. The existing researches mainly concentrated on increasing the spread of influence, but did not consider the running time of the network. In this manuscript, the salp swarm algorithm (SSA) and bi-adaptive strategy particle swarm optimization (BiAS-PSO) algorithms are integrated and named as SS-BiAS-PSO algorithm to increase the spread of influence based on the IM problem to minimize the running time of the network. The datasets utilized for the research are Ego-Facebook, Epinions, Gowalla, and HepTh, while linear threshold (LT) is utilized as a diffusion method. Then, the proposed SS-BiAS-PSO algorithm is deployed for the analysis of influence propagation. The proposed algorithm reaches a high influence spread of 645, 680, 715, and 750 with less running times respectively for 10, 20, 30, and 40 seed set sizes in Ego-Facebook. The proposed algorithm proves more effective than the existing techniques like traditional SSA and particle swarm optimization (PSO).

MULTI-CLASS AUTOMATED SPEECH LANGUAGE RECOGNITION USING NATURAL LANGUAGE PROCESSING WITH OPTIMAL DEEP LEARNING MODEL

With technological development, human–computer interaction (HCI) has improved, and spoken communication among machines and humans is one solution to enhance and expedite this process. Researchers have recently explored several systems to improve speech and speaker recognition performance in recent decades. A crucial threat in HCI is developing models that can effectually listen and respond like humans. It resulted in the development of the automated speech emotion recognition (SER) method, which can recognize various emotional classes by electing and extracting effectual features from speech signals. The fundamental problem of automated speech detection is the considerable variation in speech signals because of distinct speakers, language differences, speech differences, contents and acoustic conditions, voice modulation differences based on age and gender. With enhancements in deep learning (DL) and the affordability of computational resources, specifically graphical processing units (GPUs), research underwent a paradigm shift. Therefore, this study develops a multi-class automated speech language recognition using natural language processing with optimal deep learning (MASLR-NLPODL) technique. The MASLR-NLPODL technique intends to accomplish the efficient identification of different spoken languages. In the MASLR-NLPODL technique, the initial preprocessing technique involves windowing, frame blocking, and pre-emphasis block. Next, an adaptive time-frequency feature extractor approach utilizing the discrete fractional Fourier transform (DFrFT) was applied, which can be attained by extending the discrete Fourier transform (DFT) with eigenvectors. An improved Harris hawks optimization (IHHO) technique can be employed to select effectual features. Moreover, the classification of spoken languages can be performed by the gated recurrent unit (GRU) model. Finally, the salp swarm algorithm (SSA)-based hyperparameter selection process is involved in enhancing the performance of the GRU model. The design of the IHHO-based feature selection and SSA-based hyperparameter tuning process demonstrates the novelty of the work. The performance evaluation of the MASLR-NLPODL technique takes place under the VoxForge Dataset. The experimental validation of the MASLR-NLPODL technique exhibited a superior accuracy outcome of 96.40% over existing techniques.

An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy

Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is also reduced. Therefore, this paper examines the workflow scheduling problem of IoT devices in the fog-cloud environment, where reducing the EC of the computing system and reducing the MakeSpan Time (MST) of workflows as main objectives, under the constraints of priority, deadline and reliability. Therefore, in order to achieve these objectives, the combination of Aquila and Salp Swarm Algorithms (ASSA) is used to select the best Virtual Machines (VMs) for the execution of workflows. So, in each iteration of ASSA execution, a number of VMs are selected by the ASSA. Then by using the Reducing MakeSpan Time (RMST) technique, the MST of the workflow on selected VMs is reduced, while maintaining reliability and deadline. Then, using VM merging and Dynamic Voltage Frequency Scaling (DVFS) technique on the output from RMST, the static and dynamic EC is reduced, respectively. Experimental results show the effectiveness of the proposed method compared to previous methods.

Investigation of corrosion and water absorption of biomass natural coir fiber/hBN reinforced epoxy hybrid composites using different optimisation approaches

Agricultural waste or agro-waste, including natural fibers and particles from various crop parts, is increasingly recognized as a significant contributor to environmental issues. However, from a circular economy perspective, these materials present an opportunity to be repurposed into new, eco-friendly products. The present study, specifically focuses on understanding the effect of different factors, such as the particulate loading and the size (coir and hBN − 1 to 5 wt%; Coir Powder size (100–200 μm) of the particles on composite’s corrosion rates and water absorption properties. These hybrid particulate composites (HPC) are fabricated using the hand layup process. The study uses a Box-Behnken Design (BBD-L15), a statistical experimental design tool that facilitates the effective investigation of many input parameters and their interactions, to comprehensively investigate these impacts. In addition, the study utilizes four metaheuristic algorithms—the Dragonfly Algorithm (DFO), the Salp Swarm Algorithm (SSA), Teaching Learning Optimization (TLO) and Particle Swarm Optimization (PSO)—alongside regression equations to predict the optimal characteristics of the composite material. To determine the best-performing algorithm, a comparison is made using Deng’s method. The findings indicate that the composite with a higher weight% of hBN particulates exhibits reduced water absorption and corrosion rates. A larger Deng’s Value often indicates better performance. Based on its higher Deng’s Value, the SSO algorithm outperforms other algorithms in minimizing both corrosion resistance (CR) and water absorption (WA). The Deng’s Value for SSO reached a maximum of 0.68, while the other algorithms show comparable but lower performance.

An Integrated Framework for TFT-LCD Quality Prediction Based on L1 Regularized Feature Selection and SSA-Optimized MLP-XGBoost Ensemble

With the rapid development of display technology, accurate quality prediction of TFT-LCD is crucial because its performance directly affects the user experience. Traditional quality prediction methods rely on manual inspection or rule-based methods, facing the challenges of low efficiency, strong subjectivity, and lack of robustness to changes in production environment. To address these limitations, this paper proposes a novel regression model based on L1 regularized feature selection, Salp Swarm algorithm (SSA) optimization, and MLP-XGBoost integration. L1 regularization is used to automatically select key features from high-dimensional industrial data, reduce redundancy and enhance model generalization. Subsequently, SSA optimizes the hyperparameters of MLP and XGBoost, enabling effective exploration of the parameter space and thus improving model performance. The integrated MLP-XGBoost model combines the nonlinear feature extraction capability of MLP with the regression accuracy of XGBoost. Experimental results show that the proposed model outperforms traditional models in various indicators. Compared with the unoptimized MLP-XGBoost, the model achieves a significant reduction in prediction error, verifying its effectiveness in solving TFT-LCD quality prediction.

Novel application of sinh cosh optimizer for robust controller design in hybrid photovoltaic-thermal power systems

Load frequency control (LFC) is critical for maintaining stability in interconnected power systems, addressing frequency deviations and tie-line power fluctuations due to system disturbances. Existing methods often face challenges, including limited robustness, poor adaptability to dynamic conditions, and early convergence in optimization. This paper introduces a novel application of the sinh cosh optimizer (SCHO) to design proportional–integral (PI) controllers for a hybrid photovoltaic (PV) and thermal generator-based two-area power system. The SCHO algorithm’s balanced exploration and exploitation mechanisms enable effective tuning of PI controllers, overcoming challenges such as local minima entrapment and limited convergence speeds observed in conventional metaheuristics. Comprehensive simulations validate the proposed approach, demonstrating superior performance across various metrics. The SCHO-based PI controller achieves faster settling times (e.g., 1.6231 s and 2.4615 s for frequency deviations in Area 1 and Area 2, respectively) and enhanced robustness under parameter variations and solar radiation fluctuations. Additionally, comparisons with the controllers based on the salp swarm algorithm, whale optimization algorithm, and firefly algorithm confirm its significant advantages, including a 25–50% improvement in integral error indices (IAE, ITAE, ISE, ITSE). These results highlight the SCHO-based PI controller’s effectiveness and reliability in modern power systems with hybrid and renewable energy sources.

A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting

This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (DS) to predict Particulate Matter () levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time. Additionally, we contrast our methodology with recently created models that have been published in the literature, such as the Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Henry Gas Solubility Optimization (HGSO), Barnacles Mating Optimizer (BMO), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). In particular, the proposed HHO-SVR model outperforms other approaches, establishing it as the optimal model based on its superior results.

MLP Enhanced CO2 Emission Prediction Model with LWSSA Nature Inspired Optimization

Environmental degradation due to the rapid increase in CO₂ emissions is a pressing global challenge, necessitating innovative solutions for accurate prediction and policy development. Machine learning (ML) techniques offer a robust approach to modeling complex relationships between various factors influencing emissions. Furthermore, ML models can learn and interpret the significance of each factor’s contribution to the rise of CO2. This study proposes a novel hybrid framework combining a Multi-Layer Perceptron (MLP) with an enhanced Locally Weighted Salp Swarm Algorithm (LWSSA) to address the limitations of traditional optimization techniques, such as premature convergence and stagnation in locally optimal solutions. The LWSSA improves the standard Salp Swarm Algorithm (SSA) by incorporating a Locally Weighted Mechanism (LWM) and a Mutation Mechanism (MM) for greater exploration and exploitation. The LWSSA-MLP framework achieved a prediction accuracy of 97% and outperformed traditional optimizer-based MLP models across several evaluation metrics. A permutation feature significance analysis identified global trade, coal energy, export levels, urbanization, and natural resources as the most influential factors in CO₂ emissions, offering valuable insights for targeted interventions. The study provides a reliable and scalable framework for CO₂ emission prediction, contributing to actionable strategies for sustainable development and environmental resilience.

An optimized LSTM-based deep learning model for anomaly network intrusion detection

The increasing prevalence of network connections is driving a continuous surge in the requirement for network security and safeguarding against cyberattacks. This has triggered the need to develop and implement intrusion detection systems (IDS), one of the key components of network perimeter aimed at thwarting and alleviating the issues presented by network invaders. Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations. Several researchers have recommended the implementation of machine learning approaches in IDSs to counteract the menace posed by network intruders. Nevertheless, most previously recommended IDSs exhibit a notable false alarm rate. To mitigate this challenge, exploring deep learning methodologies emerges as a viable solution, leveraging their demonstrated efficacy across various domains. Hence, this article proposes an optimized Long Short-Term Memory (LSTM) for identifying anomalies in network traffic. The presented model uses three optimization methods, i.e., Particle Swarm Optimization (PSO), JAYA, and Salp Swarm Algorithm (SSA), to optimize the hyperparameters of LSTM. In this study, NSL KDD, CICIDS, and BoT-IoT datasets are taken into consideration. To evaluate the efficacy of the proposed model, several indicators of performance like Accuracy, Precision, Recall, F-score, True Positive Rate (TPR), False Positive Rate (FPR), and Receiver Operating Characteristic curve (ROC) have been chosen. A comparative analysis of PSO-LSTMIDS, JAYA-LSTMIDS, and SSA-LSTMIDS is conducted. The simulation results demonstrate that SSA-LSTMIDS surpasses all the models examined in this study across all three datasets.

QSHO: Quantum spotted hyena optimizer for global optimization

Spotted Hyena Optimizer (SHO) is a population-based metaheuristic algorithm inspired by the spotted hyenas’ social behavior, and it has been developed to solve global optimization problems. SHO has shown superior performance over its competitive metaheuristic algorithms in solving benchmark function optimization and engineering design problems. However, it suffers from getting stuck in local optima due to its lack of exploration while solving multi-modal optimization problems. This article proposes an improved SHO, quantum SHO (QSHO), inspired by quantum computing. The QSHO implements a quantum computing mechanism to promote its exploration ability. The novel method is tested on well-known IEEE CEC2013 and IEEE CEC2017 benchmark suits with 30 and 50 dimensions and four real-world engineering optimization problems. The results of QSHO are compared with that of Classical SHO, improved SHO (ISHO), Modified SHO (MSHO), Oppositional SHO with mutation operator (OBL-MO-SHO), SHO with space transformation search (STS-SHO), Quantum Salp Swarm Algorithm (QSSA), and Chimp Optimization Algorithm (ChOA). The results are analyzed using the Wilcoxon Signed Rank Test (WSRT) and Friedman Test. The empirical results show that QSHO statistically outperforms other compared algorithms for benchmark problem suits with 30 and 50 dimensions. According to Friedman Test statistics, the QSHO algorithm ranked first and second in solving CEC2013 30D and 50D, respectively, whereas it ranked first in both solving CEC2017 30D and 50D. In addition, we have assessed the QSHO in four real-world engineering optimization problems, and the QSHO statistically outperforms the competitive algorithms.

Enhancing Skin Cancer Diagnosis using Cubic Pythagorean Fuzzy Hypersoft Set with Salp Swarm Algorithm

Due to the rapid increase in population density, medical sciences now face a major challenge in the automated detection of diseases. Intelligent system assists health personnel in earlier disease diagnosis and provides reliable treatment to reduce the fatality rates. Skin cancer is one of the most severe and deadliest kinds of cancer. A health professional uses dermoscopic images to manually diagnose skin tumors. This technique can be time-consuming and labor-intensive and needs a considerable level of expertise. The automatic recognition method is essential for the earlier diagnosis of skin tumors. In recent times, N-soft Set model has become widespread, which is a generalization of fuzzy set where all the elements have a membership value in the complement (0 to 1) and in the set (0 or 1). This study presents a Skin Cancer Diagnosis using Cubic Pythagorean Fuzzy Hypersoft Set (SCD-CPFHSS) technique. The presented SCD-CPFHSS technique performs identification of skin cancer using the application of NSs and metaheuristic algorithms. In the SCD-CPFHSS technique, neural architectural search network (NASNet) model derives feature extractors from the dermoscopic image. In addition, the efficacy of the NASNet model can be boosted by the design of salp swarm algorithm (SSA). For skin cancer recognition, the SCD-CPFHSS technique applies CPFHSS model. The experimental outcome of the SCD-CPFHSS methodology was validated using medical dataset. The extensive results pointed out that the SCD-CPFHSS technique reaches better results on skin cancer diagnosis

An improved salp swarm algorithm for collaborative scheduling of discrete manufacturing logistics with multiple depots

An improved salp swarm algorithm for collaborative scheduling of discrete manufacturing logistics with multiple depots

An improved salp swarm algorithm for collaborative scheduling of discrete manufacturing logistics with multiple depots

An improved salp swarm algorithm for collaborative scheduling of discrete manufacturing logistics with multiple depots

A Rolling Bearing Fault Diagnosis Method Combining MSSSA-VMD with the Parallel Network of GASF-CNN and BiLSTM

Once the rolling bearing fails, it will threaten the normal operation of the whole rotating machinery. Therefore, it is very necessary to conduct research on rolling bearing fault diagnosis. This paper proposes a rolling bearing fault diagnosis method combining MSSSA-VMD (variational mode decomposition optimized by the improved salp swarm algorithm based on mixed strategy) with the parallel network of GASF-CNN (convolutional neural network based on Gramian angular summation field) and bi-directional long short-term memory (BiLSTM) to solve the problem of poor diagnostic performance for the rolling bearing faults caused by the respective limitations of existing fault diagnosis methods based on signal processing and deep learning. Firstly, MSSSA-VMD is proposed to solve the problem where the decomposition effect of VMD is not ideal due to improper parameter selection. Then, MSSSA-VMD is employed to preprocess and extract characteristics. Finally, the extracted characteristics are input into the parallel network of GASF-CNN and BiLSTM for diagnosis. In one channel of the parallel network, GASF is used to convert the characteristic vectors into a two-dimensional image, which is then fed into CNN for spatial characteristic extraction. In the other channel of the parallel network, the characteristic vectors are directly input into BiLSTM for temporal characteristic extraction. Experimental results demonstrate that the proposed method has good performance in terms of fault diagnosis performance under constant operating conditions, generalization ability under variable operating conditions and noise resistance.

A Robust Salp Swarm Algorithm for Photovoltaic Maximum Power Point Tracking Under Partial Shading Conditions

Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. To address these challenges, this paper introduces a hybrid algorithm that integrates an improved salp swarm algorithm (SSA) with the perturb and observe (P&O) method. Initially, the SSA is augmented with a dynamic spiral evolution mechanism and a Lévy flight strategy, expanding the search space and bolstering global search capabilities, which in turn enhances the tracking precision. Subsequently, the application of a Gaussian operator for distribution calculations allows for the adaptive adjustment of step sizes in each iteration, quickening convergence and diminishing power oscillations. Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.

Efficient distribution network based on photovoltaic fed electric vehicle charging station using WSO-RBFNN approach

An integral and prevalent aspect of modern life is the electric vehicle (EV).EV charging networks struggle with power losses and high energy costs, particularly as demand rises leading to inefficiencies and potential system overloads. A hybrid WSO-RBFNN approach is proposed for the distribution network's photovoltaic (PV) fed electric vehicle charging stations. The performance of the proposed hybrid strategy is a combination of war strategy optimization (WSO) and Radial basis function neural network (RBFNN). It is hence referred to as the WSO-RBFNN technique. WSO optimizes the distribution network by minimizing power loss, improving voltage sensitivity, and reducing costs. Meanwhile, the RBFNN predicts the load demand. This innovative technique WSO-RBFNN identifies the nearest charging spots that minimize power loss and RBFNN optimizes power flow predicts charging demands and addresses both environmental and electrical grid stability concerns. The proposed technique is implemented in MATLAB. MATLAB is powerful computational software widely used in different fields like numerical computation, visualization, and algorithm development. MATLAB provides powerful tools for data visualization and plotting. The results are compared to various existing Heap-based optimizer (HBO), Wild horse optimizer (WHO), and Salp Swarm Algorithm (SSA) techniques. The proposed approach contributes only 0.59 % of a power loss it is less and the cost of energy is 0.18$ which is lesser and the voltage deviation is 6.6 pu which is less than the existing techniques.

Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm Algorithm (SSA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.